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Category articles

General General

Diabetic Eye Disease: Advancements in Technology, Detection, and Access to Care.

In The Senior care pharmacist

OBJECTIVE: To educate pharmacists on advancements in, detection of, and access to care for diabetic eye disease (DED) by reviewing the etiologies, treatment options, and technological evolution in teleophthalmology for DED.<br/> DATA SYNTHESIS: The literature included review articles, original research articles, treatment information, and advancements in teleophthalmology for DED.<br/> CONCLUSION: DED encompasses a group of eye conditions that affect people with diabetes, which primarily includes diabetic retinopathy and diabetic macular edema but may also include cataracts, ocular surface disease, and glaucoma. Emerging technologies with retinal imaging tools and artificial intelligence have increased access to care for diabetic people with diabetes in many studies. Pharmacists familiar with diabetic screening advancements can work closely with primary care physicians and ophthalmologists to better educate patients on treatment regimens.

Lyford Trevor, Sheppard John

2020-Jun-01

General General

Advanced Digital Health Technologies for COVID-19 and Future Emergencies.

In Telemedicine journal and e-health : the official journal of the American Telemedicine Association

Background: Coronavirus disease 2019 (COVID-19) has led to a national health care emergency in the United States and exposed resource shortages, particularly of health care providers trained to provide critical or intensive care. This article describes how digital health technologies are being or could be used for COVID-19 mitigation. It then proposes the National Emergency Tele-Critical Care Network (NETCCN), which would combine digital health technologies to address this and future crises. Methods: Subject matter experts from the Society of Critical Care Medicine and the Telemedicine and Advanced Technology Research Center examined the peer-reviewed literature and science/technology news to see what digital health technologies have already been or could be implemented to (1) support patients while limiting COVID-19 transmission, (2) increase health care providers' capability and capacity, and (3) predict/prevent future outbreaks. Results: Major technologies identified included telemedicine and mobile care (for COVID-19 as well as routine care), tiered telementoring, telecritical care, robotics, and artificial intelligence for monitoring. Several of these could be assimilated to form an interoperable scalable NETCCN. NETCCN would assist health care providers, wherever they are located, by obtaining real-time patient and supplies data and disseminating critical care expertise. NETCCN capabilities should be maintained between disasters and regularly tested to ensure continual readiness. Conclusions: COVID-19 has demonstrated the impact of a large-scale health emergency on the existing infrastructures. Short term, an approach to meeting this challenge is to adopt existing digital health technologies. Long term, developing a NETCCN may ensure that the necessary ecosystem is available to respond to future emergencies.

Scott Benjamin K, Miller Geoffrey T, Fonda Stephanie J, Yeaw Ronald E, Gaudaen James C, Pavliscsak Holly H, Quinn Matthew T, Pamplin Jeremy C

2020-May-26

coronavirus, critical care, digital health, emergencies, natural disasters, pandemics, telemedicine

General General

Competitive learning suggests circulating miRNA profiles for cancers decades prior to diagnosis.

In RNA biology

MicroRNAs are regulators of gene expressionand may be key markers in liquid biopsy.Early diagnosis is an effective means to increase patients' overall survival. We generated genome-wide miRNA profiles from serum of patients and controls from the population-based Janus Serum Bank (JSB) and analyzed them by bioinformatics and artificial intelligence approaches. JSB contains sera from 318,628 originally healthy persons, more than 96,000 of whom developed cancer. We selected 210 serum samples from patients with lung, colon or breast cancer at three time points prior to diagnosis (up to 32 years prior to diagnosis with median 5 years interval between TPs), one time-point after diagnosis and from individually matched controls. The controls were matched on age and year of all pre-diagnostic sampling time-points for the corresponding case. Using ANOVA we report 70 significantly deregulated markers (adjusted p-value<0.05). The driver for the significance was the diagnostic time point (miR-575, miR-6821-5p, miR-630 with adjusted p-values<10-10). Further, 91miRNAs were differently expressed in pre-diagnostic samples as compared to controls (nominal p<0.05). Self-organized maps (SOMs)indicated larges effects in lung cancer samples while breast cancer samples showed the least pronounced changes. SOMsalsohighlighted cancer and time point specific miRNA dys-regulation. Intriguingly, a detailed breakdown of the results highlighted that 51% of all miRNAs were highly specific, either for a time-point or a cancer entity. Pathway analysis highlighted 12 pathways including Hipo signalling and ABC transporters.Our results indicate that tumors may be indicated by serum miRNAs decades prior the clinical manifestation.

Keller Andreas, Fehlmann Tobias, Backes Christina, Kern Fabian, Gislefoss Randi, Langseth Hilde, Rounge Trine B, Ludwig Nicole, Meese Eckart

2020-May-27

General General

Parietal-Prefrontal Feedforward Connectivity in Association With Schizophrenia Genetic Risk and Delusions.

In The American journal of psychiatry

OBJECTIVE : Conceptualizations of delusion formation implicate deficits in feedforward information updating across the posterior to prefrontal cortices, resulting in dysfunctional integration of new information about contexts in working memory and, ultimately, failure to update overfamiliar prior beliefs. The authors used functional MRI and machine learning models to address individual variability in feedforward parietal-prefrontal information updating in patients with schizophrenia. They examined relationships between feedforward connectivity, and delusional thinking and polygenic risk for schizophrenia.

METHODS : The authors studied 66 schizophrenia patients and 143 healthy control subjects during performance of context updating in working memory. Dynamic causal models of effective connectivity were focused on regions of the prefrontal and parietal cortex potentially implicated in delusion processes. The effect of polygenic risk for schizophrenia on connectivity was examined in healthy individuals. The authors then leveraged support vector regression models to define optimal normalized target connectivity tailored for each patient and tested the extent to which deviation from this target could predict individual variation in severity of delusions.

RESULTS : In schizophrenia patients, updating and manipulating context information was disproportionately less accurate than was working memory maintenance, with an interaction of task accuracy by diagnosis. Patients with delusions also tended to have relatively reduced parietal-prefrontal feedforward effective connectivity during context updating in working memory manipulation. The same connectivity was adversely influenced by polygenic risk for schizophrenia in healthy subjects. Individual patients' deviation from predicted "normal" feedforward connectivity based on the support vector regression models correlated with severity of delusions.

CONCLUSIONS : These computationally derived observations support a role for feedforward parietal-prefrontal information processing deficits in delusional psychopathology and in genetic risk for schizophrenia.

Greenman Danielle L B, La Michelle A N, Shah Shefali, Chen Qiang, Berman Karen F, Weinberger Daniel R, Tan Hao Yang

2020-May-27

Computational Psychiatry, Delusions, Schizophrenia Spectrum and Other Psychotic Disorders

General General

A Machine Learning-Based Approach for Predicting Patient Punctuality in Ambulatory Care Centers.

In International journal of environmental research and public health ; h5-index 73.0

Late-arriving patients have become a prominent concern in several ambulatory care clinics across the globe. Accommodating them could lead to detrimental ramifications such as schedule disruption and increased waiting time for forthcoming patients, which, in turn, could lead to patient dissatisfaction, reduced care quality, and physician burnout. However, rescheduling late arrivals could delay access to care. This paper aims to predict the patient-specific risk of late arrival using machine learning (ML) models. Data from two different ambulatory care facilities are extracted, and a comprehensive list of predictor variables is identified or derived from the electronic medical records. A comparative analysis of four ML algorithms (logistic regression, random forests, gradient boosting machine, and artificial neural networks) that differ in their training mechanism is conducted. The results indicate that ML algorithms can accurately predict patient lateness, but a single model cannot perform best with respect to predictive performance, training time, and interpretability. Prior history of late arrivals, age, and afternoon appointments are identified as critical predictors by all the models. The ML-based approach presented in this research can serve as a decision support tool and could be integrated into the appointment system for effectively managing and mitigating tardy arrivals.

Srinivas Sharan

2020-May-24

ambulatory care center, clinical decision support, late-arriving patients, machine learning, predicting tardy arrivals

General General

Monitoring of Delayed Cerebral Ischemia in Patients with Subarachnoid Hemorrhage via Near-Infrared Spectroscopy.

In Journal of clinical medicine

We investigated the role of near infrared spectroscopy (NIRS) in identifying delayed cerebral ischemia (DCI) in patients with subarachnoid hemorrhage (SAH). We measured the cerebral regional oxygen saturation (rSO2) continuously for 14 days. The differences in rSO2 according to DCI were analyzed. We also compared the diagnostic accuracy of NIRS and transcranial Doppler ultrasonography (TCD) for DCI detection using the area under receiver operator characteristic (ROC) curve. Fifty-two patients treated with coil embolization were enrolled, including 18 with DCI (34.6%) and 34 without DCI (65.4%). Significant differences in rSO2 levels were observed from days 7 to 9. The rSO2 level was 60.95 (58.10-62.30) at day 7 in the DCI vs. 63.90 (62.50-67.10) in the non-DCI patients. By day 8, it was 59.50 (56.90-64.50) in the DCI vs. 63.30 (59.70-68.70) in the non-DCI cases. By day 9, it was 61.85 (59.40-65.20) in the DCI vs. 66.00 (62.70-68.30) in the non-DCI. A decline of >12.7% in SO2 rate yielded a sensitivity of 94.44% (95% CI: 72.7-99.9%) and a specificity of 70.59% (95% CI: 52.5-84.9%) for identifying DCI. Changes in NIRS tended to yield better diagnostic accuracy than TCD, but were not statistically significant. NIRS is a feasible method for real-time detection of DCI.

Park Jeong Jin, Kim Chulho, Jeon Jin Pyeong

2020-May-24

delayed cerebral ischemia, near-infrared spectroscopy, subarachnoid hemorrhage

Public Health Public Health

Fighting COVID-19, a Place for Artificial Intelligence.

In Transboundary and emerging diseases ; h5-index 40.0

The emergence of the coronavirus disease 2019 (COVID-19) heralded a new era in the cross-species transmission of severe respiratory illness leading to rapid spread in mainland China and around the world with a case fatality rate of 2.3% in China and 1.8-7.2% outside China (Wu & McGoogan, 2019; Centers for Disease Control and Prevention, 2020; Onder Rezza, & Brusaferro, 2020; World Health Organization, 2020). As of May 15, 2020, a total of 4,338,658 confirmed cases of COVID-19 and 297,119 death cases have been documented globally (World Health Organization, 2020). Several strategies have been adopted to contain the outbreak including classic infection-control and public health measures, nevertheless these measures may not be effective for tackling the scale of COVID-19.

Emile Sameh Hany, Hamid Hytham K S

2020-May-27

Public Health Public Health

[Algorithm to stratify the risk of myocardial infarction in patients with acute coronary syndrome at primary examination.]

In Klinicheskaia laboratornaia diagnostika

The episode of acute coronary syndrome is most often preceded by the development of systemic and local inflammation, which plays a significant role in the pathogenesis of the disease. General clinical blood analysis, directly or indirectly reflecting systemic pathological processes in the patient's body based on quantitative and morphological assessment of blood composition, is one of the most affordable methods of laboratory diagnostics in modern public health. Taking into account the growing number of digital data obtained by diagnosticians from analytical systems, there is a growing potential for the use of machine learning methods to increase the effectiveness of provided diagnostic information in the interests of the patient. The aim of this study was to create an algorithm for stratifying the risk of myocardial infarction based on the methods of machine learning in patients with acute coronary syndrome at primary examination. A prospective pilot study was conducted. In total 307 patients with acute coronary syndrome (169 men and 138 women) were examined. The average age of patients was 68.6 ± 12.5 years. Retrospectively, the patients were divided into two groups: the main group - patients with the final diagnosis "Myocardial infarction" and the control group with the diagnosis "Unstable angina pectoris". All patients at hospitalization at the primary laboratory examination along with the study of the concentration of cardiac troponin I by a highly sensitive method were examined by a general clinical bloodanalysis on an automatic hematological 5-diff analyzer. As a result of the application of the ensemble method as a method of machine learning and artificial neural networks as 6 independent models of the ensemble it was possible to achieve the area under the ROC curve = 0.77 on the test set when assessing the quality of patient stratification. Taking into account the volume of the training sample in 214 patients and the results of similar studies, the achieved stratification quality can be considered acceptable and promising for further accumulation of the database with the purpose of additional training of the developed algorithm and improvement of the disease prognosis accuracy characteristics.

Pushkin A S, Shulkin D, Borisova L V, Akhmedov T A, Rukavishnikova S A

2020

acute coronary syndrome, cardiomarkers, hematology, machine learning, troponin

Surgery Surgery

The Economic Burden of Out-of-Pocket Expenses for Plastic Surgery Procedures.

In Plastic and reconstructive surgery ; h5-index 62.0

BACKGROUND : Health insurance reimbursement structure has evolved, with patients becoming increasingly responsible for their health care costs through rising out-of-pocket expenses. High levels of cost sharing can lead to delays in access to care, influence treatment decisions, and cause financial distress for patients.

METHODS : Patients undergoing the most common outpatient reconstructive plastic surgery operations were identified using Truven MarketScan databases from 2009 to 2017. Total cost of the surgery paid to the insurer and out-of-pocket expenses, including deductible, copayment, and coinsurance, were calculated. Multivariable generalized linear modeling with log link and gamma distribution was used to predict adjusted total and out-of-pocket expenses. All costs were inflation-adjusted to 2017 dollars.

RESULTS : The authors evaluated 3,165,913 outpatient plastic and reconstructive surgical procedures between 2009 and 2017. From 2009 to 2017, total costs had a significant increase of 25 percent, and out-of-pocket expenses had a significant increase of 54 percent. Using generalized linear modeling, procedures performed in outpatient hospitals conferred an additional $1999 in total costs (95 percent CI, $1978 to $2020) and $259 in out-of-pocket expenses (95 percent CI, $254 to $264) compared with office procedures. Ambulatory surgical center procedures conferred an additional $1698 in total costs (95 percent CI, $1677 to $1718) and $279 in out-of-pocket expenses (95 percent CI, $273 to $285) compared with office procedures.

CONCLUSIONS : For outpatient plastic surgery procedures, out-of-pocket expenses are increasing at a faster rate than total costs, which may have implications for access to care and timing of surgery. Providers should realize the increasing burden of out-of-pocket expenses and the effect of surgical location on patients' costs when possible.

Billig Jessica I, Chen Jung-Sheng, Lu Yu-Ting, Chung Kevin C, Sears Erika D

2020-Jun

Radiology Radiology

The 2019 International Society of Urological Pathology (ISUP) Consensus Conference on Grading of Prostatic Carcinoma.

In The American journal of surgical pathology ; h5-index 63.0

Five years after the last prostatic carcinoma grading consensus conference of the International Society of Urological Pathology (ISUP), accrual of new data and modification of clinical practice require an update of current pathologic grading guidelines. This manuscript summarizes the proceedings of the ISUP consensus meeting for grading of prostatic carcinoma held in September 2019, in Nice, France. Topics brought to consensus included the following: (1) approaches to reporting of Gleason patterns 4 and 5 quantities, and minor/tertiary patterns, (2) an agreement to report the presence of invasive cribriform carcinoma, (3) an agreement to incorporate intraductal carcinoma into grading, and (4) individual versus aggregate grading of systematic and multiparametric magnetic resonance imaging-targeted biopsies. Finally, developments in the field of artificial intelligence in the grading of prostatic carcinoma and future research perspectives were discussed.

van Leenders Geert J L H, van der Kwast Theodorus H, Grignon David J, Evans Andrew J, Kristiansen Glen, Kweldam Charlotte F, Litjens Geert, McKenney Jesse K, Melamed Jonathan, Mottet Nicholas, Paner Gladell P, Samaratunga Hemamali, Schoots Ivo G, Simko Jeffry P, Tsuzuki Toyonori, Varma Murali, Warren Anne Y, Wheeler Thomas M, Williamson Sean R, Iczkowski Kenneth A

2020-May-26

Ophthalmology Ophthalmology

Improving the Detection of Glaucoma and Its Progression: A Topographical Approach.

In Journal of glaucoma

Glaucoma is typically defined as a progressive optic neuropathy characterized by a specific (arcuate) pattern of visual field (VF) and anatomical changes. Therefore, we should be comparing arcuate patterns of damage seen on VFs with those seen on optical coherence tomography (OCT) maps. Instead, clinicians often use summary metrics such as VF pattern standard deviation (PSD), OCT retinal nerve fiber (RNF) global thickness, etc. There are two major impediments to topographically comparing patterns of damage on VF and OCT maps. First, until recently, it was not easy to make these comparisons with commercial reports. While recent reports do make it easier to compare VF and OCT maps, they have shortcomings. In particular, the 24-2 VF covers a larger retinal region than the commercial OCT scans, and, further, it is not easy to understand the topographical relationship among the different maps/plots within the current OCT reports. Here we show how a model of RNF bundles can overcome these problems. The second major impediment is the lack of a quantitative, and automated, method for comparing patterns of damage seen on VF and OCT maps. However, it is now possible to objectively and automatically quantify this agreement. Together, the RNF bundle model and the automated structure-function method should improve the power of topographical methods for detecting glaucoma and its progression. This should prove useful in clinical studies and trials, as well as for training and validating artificial intelligence/deep learning approaches for these purposes.

Hood Donald C, Zemborain Zane Z, Tsamis Emmanouil, De Moraes C Gustavo

2020-May-26

General General

Detecting screams from home audio recordings to identify tantrums: a feasibility study using transfer machine learning.

In JMIR formative research

BACKGROUND : Qualitative self- or parent-reports used in assessing children's behavioral disorders are often inconvenient to collect and can be misleading due to missing information, rater biases, and limited validity. A data-driven approach to quantify behavioral disorder could alleviate these concerns. This study proposes a machine learning approach to identify screams in voice recordings that avoids the need to gather large amounts of clinical data for model training.

OBJECTIVE : The goal of this study is to evaluate if a machine learning model trained only on publicly available audio datasets can be used to detect screaming sounds in audio streams captured in an at-home setting.

METHODS : Two sets of audio samples were prepared to evaluate the model: a subset of the publicly available AudioSet dataset, and a set of audio data extracted from the TV show Supernanny, which was chosen for its similarity to clinical data. Scream events were manually annotated for the Supernanny data and existing annotations were refined for the AudioSet data. Audio feature extraction was performed with a convolutional neural network pre-trained on AudioSet. A gradient-boosted tree model was trained and cross-validated for scream classification on the AudioSet data and then validated independently on the Supernanny audio.

RESULTS : On the held-out AudioSet clips, the model achieved a ROC-AUC of 0.86. The same model applied to three full episodes of Supernanny audio achieves a ROC-AUC of 0.95 and an average precision (positive predictive value) of 42% despite screams only making up 1.3% of the total runtime.

CONCLUSIONS : These results suggest that a scream-detection model trained with publicly available data could be valuable for monitoring clinical recordings and identifying tantrums, as opposed to depending on collecting costly privacy-protected clinical data for model training.

O’Donovan Rebecca, Sezgin Emre, Bambach Sven, Butter Eric, Lin Simon

2020-Apr-19

General General

Improvements in Patient Monitoring for the Intensive Care Unit: Survey Study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Due to demographic change and, more recently, the Coronavirus Disease 2019 (COVID-19), the importance of modern intensive care units (ICU) is becoming apparent. One of the key components of an ICU is the continuous monitoring of patients' vital parameters. However, existing advances in informatics, signal processing, or engineering that could alleviate the burden on ICUs have not yet been applied. This could be related to the lack of user involvement in research and development.

OBJECTIVE : This study focused on satisfaction of ICU staff with the current patient monitoring and their suggestions for future improvements. We aimed to identify aspects disturbing patient care, display devices for remote monitoring, use cases for artificial intelligence (AI), and whether ICU staff is willing to improve their digital literacy or contribute to the improvement of patient monitoring. We further desired to uncover differences in the responses of the professional groups.

METHODS : This survey study was realized with ICU staff from four ICUs of a German university hospital between November 2019 and January 2020. We developed a web-based 36-item survey questionnaire by analyzing a preceding qualitative interview study with ICU staff about clinical requirements of future patient monitoring. Statistical analyses of questionnaire results included median values with their bootstrapped 95% confidence intervals, and Chi-square tests to compare the distributions of item responses of the professional groups.

RESULTS : Eighty-six of the 270 ICU physicians and nurses completed the survey questionnaire. The majority stated to feel confident using the patient monitoring, but high rates of false positive alarms and the many sensor cables were considered to disturb patient care. Wireless sensors, reduction of false positive alarms and hospital standard operating procedures (SOP) for alarm management were demanded. Responses to the display devices proposed for remote patient monitoring were split. Regarding its use, most respondents indicated responsibility for multiple wards or earlier alerting. AI for ICUs would be useful for early detection of complications and increased risk of mortality, as well as to have guidelines for therapy and diagnostics proposed. Transparency, interoperability, usability, and staff training were essential to promote usage of an AI. The majority wanted to learn more about new technologies for ICU and desired more time for it. Physicians had fewer reservations than nurses about using mobile phones for remote monitoring, and AI-based intelligent alarm management.

CONCLUSIONS : This survey study among ICU staff revealed key improvements for patient monitoring in intensive care medicine. Hospital providers and medical device manufacturers should focus on reducing false alarms, implementing hospital alarm SOPs, introducing wireless sensors, preparing for the use of AI, and enhancing digital literacy of ICU staff. Our results may contribute to the user-centered transfer of digital technologies into practice to alleviate challenges in intensive care medicine.

CLINICALTRIAL : ClinicalTrials.gov NCT03514173; https://clinicaltrials.gov/ct2/show/NCT03514173.

Poncette Akira-Sebastian, Mosch Lina, Spies Claudia, Schmieding Malte, Schiefenhövel Fridtjof, Krampe Henning, Balzer Felix

2020-May-13

General General

Extraction of Information Related to Drug Safety Surveillance From Electronic Health Record Notes: Joint Modeling of Entities and Relations Using Knowledge-aware Neural Attentive Models.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : An adverse drug event (ADE) is commonly defined as "an injury resulting from medical intervention related to a drug". Providing information related to ADEs and alerting caregivers at the point-of-care can reduce the risk of prescription and diagnosis errors, and improve health outcomes. ADEs captured in Electronic Health Records (EHR) structured data, as either coded problems or allergies, are often incomplete leading to underreporting. It is therefore important to develop capabilities to process unstructured EHR data in the form of clinical notes, which contain richer documentation of a patient's adverse drug events. Several natural language processing (NLP) systems were previously proposed to automatically extract information related to ADEs. However, the results from these systems showed that significant improvement is still required for automatic extraction of ADEs from clinical notes.

OBJECTIVE : The objective of this study is to improve automatic extraction of ADEs and related information such as drugs and their reason for administration from patient clinical notes.

METHODS : This research was conducted using discharge summaries from the MIMIC-III database obtained through the 2018 National NLP Clinical Challenges (n2c2) annotated with Drugs, drug attributes (Strength, Form, Frequency, Route, Dosage, Duration), Adverse Drug Events, Reasons, and relations between drugs and other entities. We developed a deep learning-based system for extracting these drug-centric concepts and relations simultaneously using a joint method enhanced with contextualized embeddings, a position-attention mechanism, and knowledge representations. The joint method generated different sentence representations with respect to each drug, which were then used to extract related concepts and relations simultaneously. Contextualized representations trained on the MIMIC-III database were used to capture context-sensitive meanings of words. The position-attention mechanism amplified benefits of the joint method by generating sentence representations that capture long-distance relations. Knowledge representations were obtained from graph embeddings created using the FAERS database to improve relation extraction, especially when contextual clues are insufficient.

RESULTS : Our system achieved new state-of-the-art results on the n2c2 dataset, with significant improvements in recognizing the crucial Drug-->Reason (F1 0.650 vs 0.579) and Drug-->ADE (0.490 vs 0.476) relations.

CONCLUSIONS : We present a system for extracting drug-centric concepts and relations that outperformed current state-of-the-art results. We show that contextualized embeddings, position-attention mechanism and knowledge graph embeddings effectively improve deep learning-based concept and relation extraction. This study demonstrates the further potential for deep learning-based methods to help extract real world evidence from unstructured patient data for drug safety surveillance.

CLINICALTRIAL :

Dandala Bharath, Joopudi Venkata, Tsou Ching-Huei, Liang Jennifer J, Suryanarayanan Parthasarathy

2020-May-13

Ophthalmology Ophthalmology

Automated Classification of Colorectal Neoplasms in White-Light Colonoscopy Images via Deep Learning.

In Journal of clinical medicine

Background: Classification of colorectal neoplasms during colonoscopic examination is important to avoid unnecessary endoscopic biopsy or resection. This study aimed to develop and validate deep learning models that automatically classify colorectal lesions histologically on white-light colonoscopy images. Methods: White-light colonoscopy images of colorectal lesions exhibiting pathological results were collected and classified into seven categories: stages T1-4 colorectal cancer (CRC), high-grade dysplasia (HGD), tubular adenoma (TA), and non-neoplasms. The images were then re-classified into four categories including advanced CRC, early CRC/HGD, TA, and non-neoplasms. Two convolutional neural network models were trained, and the performances were evaluated in an internal test dataset and an external validation dataset. Results: In total, 3828 images were collected from 1339 patients. The mean accuracies of ResNet-152 model for the seven-category and four-category classification were 60.2% and 67.3% in the internal test dataset, and 74.7% and 79.2% in the external validation dataset, respectively, including 240 images. In the external validation, ResNet-152 outperformed two endoscopists for four-category classification, and showed a higher mean area under the curve (AUC) for detecting TA+ lesions (0.818) compared to the worst-performing endoscopist. The mean AUC for detecting HGD+ lesions reached 0.876 by Inception-ResNet-v2. Conclusions: A deep learning model presented promising performance in classifying colorectal lesions on white-light colonoscopy images; this model could help endoscopists build optimal treatment strategies.

Yang Young Joo, Cho Bum-Joo, Lee Myung-Je, Kim Ju Han, Lim Hyun, Bang Chang Seok, Jeong Hae Min, Hong Ji Taek, Baik Gwang Ho

2020-May-24

artificial intelligence, colonoscopy, colorectal neoplasm, convolutional neural network, deep learning

Public Health Public Health

Fighting COVID-19, a Place for Artificial Intelligence.

In Transboundary and emerging diseases ; h5-index 40.0

The emergence of the coronavirus disease 2019 (COVID-19) heralded a new era in the cross-species transmission of severe respiratory illness leading to rapid spread in mainland China and around the world with a case fatality rate of 2.3% in China and 1.8-7.2% outside China (Wu & McGoogan, 2019; Centers for Disease Control and Prevention, 2020; Onder Rezza, & Brusaferro, 2020; World Health Organization, 2020). As of May 15, 2020, a total of 4,338,658 confirmed cases of COVID-19 and 297,119 death cases have been documented globally (World Health Organization, 2020). Several strategies have been adopted to contain the outbreak including classic infection-control and public health measures, nevertheless these measures may not be effective for tackling the scale of COVID-19.

Emile Sameh Hany, Hamid Hytham K S

2020-May-27

General General

Mobile Personal Healthcare System for Non-Invasive, Pervasive and Continuous Blood Pressure Monitoring: A Feasibility Study.

In JMIR mHealth and uHealth

BACKGROUND : Smartphone-based blood pressure (BP) monitor using photoplethysmogram (PPG) technology has emerged as a promising approach to empower users with self-monitoring for effective diagnosis and control of hypertension (HT).

OBJECTIVE : This study aimed to develop a mobile personal healthcare system for non-invasive, pervasive, and continuous estimation of BP level and variability to be user-friendly to elderly.

METHODS : The proposed approach was integrated by a self-designed cuffless, calibration-free, wireless and wearable PPG-only sensor, and a native purposely-designed smartphone application using multilayer perceptron machine learning techniques from raw signals. We performed a feasibility study with three elder adults (mean age 61.3 ± 1.5 years; 66% women) to test usability and accuracy of the smartphone-based BP monitor.

RESULTS : The employed artificial neural network (ANN) model performed with good average accuracy >90% and very strong correlation >0.90 (P < .0001) to predict the reference BP values of our validation sample (n=150). Bland-Altman plots showed that most of the errors for BP prediction were less than 10 mmHg. However, according to Association for the Advancement of Medical Instrumentation (AAMI) and British Hypertension Society (BHS) standards, only DBP prediction met the clinically accepted accuracy thresholds.

CONCLUSIONS : With further development and validation, the proposed system could provide a cost-effective strategy to improve the quality and coverage of healthcare, particularly in rural zones, areas lacking physicians, and solitary elderly populations.

Mena Luis J, Felix Vanessa G, Ostos Rodolfo, Gonzalez Jesus A, Martínez Peláez Rafael, Melgarejo Jesus D, Maestre Gladys E

2020-Apr-26

General General

PDE Learning of Filtering and Propagation for Task-Aware Facial Intrinsic Image Analysis.

In IEEE transactions on cybernetics

Filtering and propagation are two basic operations in image analysis and rendering, and they are also widely used in computer graphics and machine learning. However, the models of filtering and propagation were based on diverse mathematical formulations, which have not been fully understood. This article aims to explore the properties of both filtering and propagation models from a partial differential equation (PDE) learning perspective. We propose a unified PDE learning framework based on nonlinear reaction-diffusion with a guided map, graph Laplacian, and reaction weight. It reveals that: 1) the guided map and reaction weight determines whether the PDE produces filtering or propagation diffusion and 2) the kernel of graph Laplacian controls the diffusion pattern. Based on the proposed PDE framework, we derive the mathematical relations between different models, including learning to diffusion (LTD) model, label propagation, edit propagation, and edge-aware filter. In practical verification, we apply the PDE framework to design diffusion operations with the adaptive kernel to tackle the ill-posed problem of facial intrinsic image analysis (FIIA). A flexible task-aware FIIA system is built to achieve various facial rendering effects, such as face image relighting and delighting, artistic illumination transfer, illumination-aware face swapping, or transfiguring. Qualitative and quantitative experiments show the effectiveness and flexibility of task-aware FIIA and provide new insights on PDE learning for visual analysis and rendering.

Liang Lingyu, Jin Lianwen, Xu Yong

2020-May-27

General General

Machine-Learning Enabled Exploration of Morphology Influence on Wire-Array Electrodes for Electrochemical Nitrogen Fixation.

In The journal of physical chemistry letters ; h5-index 129.0

Neural networks, trained on data generated by a microkinetic model and finite-element simulations, expand explorable parameter space by significantly accelerating the predictions of electrocatalytic performance. In addition to modelling electrode reactivity, we use micro/nano-wire arrays as a well-defined, easily tuned, and experimentally relevant exemplary morphology for electrochemical nitrogen fixation. This model system provides the data necessary for training neural networks which are subsequently exploited for electrocatalytic material morphology optimizations and explorations into the influence of geometry on nitrogen fixation electrodes, feats untenable without large-scale simulations, on both a global and local basis.

Hoar Benjamin B, Lu Shengtao, Liu Chong

2020-May-27

General General

An On-the-fly Approach to Construct Generalized Energy‒Based Fragmentation Machine Learning Force Fields of Complex Systems.

In The journal of physical chemistry. A

An on-the-fly fragment-based machine learning (ML) approach was developed to construct the machine learning force field for large complex systems. In this approach, the energy, forces, and molecular properties of the target system are obtained by combining machine learning force fields of various subsystems with the generalized energy-based fragmentation (GEBF) approach. Using nonparametric Gaussian process (GP) model, all the force fields of subsystems are automatically generated online without data selection and parameter optimization. With the GEBF-ML force field constructed for a normal alkane, C60H122, long-time molecular dynamics (MD) simulations are performed on different sizes of alkanes, and the predicted en-ergy, forces, and molecular properties (dipole moment) are favorably comparable with full quantum mechanics (QM) calcu-lations. The predicted IR spectra also show excellent agreement with the direct ab initio MD results. Our results demonstrate that the GEBF-ML method provides an automatic and efficient way to build force fields for a broad range of complex sys-tems such as biomolecules and supramolecular systems.

Cheng Zheng, Zhao Dongbo, Ma Jing, Li Wei, Li Shuhua

2020-May-27

General General

Systematic evaluation of machine learning methods for identifying human-pathogen protein-protein interactions.

In Briefings in bioinformatics

In recent years, high-throughput experimental techniques have significantly enhanced the accuracy and coverage of protein-protein interaction identification, including human-pathogen protein-protein interactions (HP-PPIs). Despite this progress, experimental methods are, in general, expensive in terms of both time and labour costs, especially considering that there are enormous amounts of potential protein-interacting partners. Developing computational methods to predict interactions between human and bacteria pathogen has thus become critical and meaningful, in both facilitating the detection of interactions and mining incomplete interaction maps. In this paper, we present a systematic evaluation of machine learning-based computational methods for human-bacterium protein-protein interactions (HB-PPIs). We first reviewed a vast number of publicly available databases of HP-PPIs and then critically evaluate the availability of these databases. Benefitting from its well-structured nature, we subsequently preprocess the data and identified six bacterium pathogens that could be used to study bacterium subjects in which a human was the host. Additionally, we thoroughly reviewed the literature on 'host-pathogen interactions' whereby existing models were summarized that we used to jointly study the impact of different feature representation algorithms and evaluate the performance of existing machine learning computational models. Owing to the abundance of sequence information and the limited scale of other protein-related information, we adopted the primary protocol from the literature and dedicated our analysis to a comprehensive assessment of sequence information and machine learning models. A systematic evaluation of machine learning models and a wide range of feature representation algorithms based on sequence information are presented as a comparison survey towards the prediction performance evaluation of HB-PPIs.

Chen Huaming, Li Fuyi, Wang Lei, Jin Yaochu, Chi Chi-Hung, Kurgan Lukasz, Song Jiangning, Shen Jun

2020-May-27

bioinformatics, human–pathogen interactions, machine learning, protein–protein interactions, sequential analysis, systematic evaluation

Cardiology Cardiology

On stethoscopes, patient records, artificial intelligence and zettabytes: a glimpse into the future of digital medicine in Mexico.

In Archivos de cardiologia de Mexico

Science and technology are modifying medicine at a dizzying pace. Although access in our country to the benefits of innovations in the area of devices, data storage and artificial intelligence is still very restricted, the advance of digital medicine offers the opportunity to solve some of the biggest problems faced by medical practice and public health in Mexico. The potential areas where digital medicine can be disruptive are: accessibility to quality medical care, centralization of specialties in large cities, dehumanization of medical treatment, lack of resources to access evidence-supported treatments, among others. This review presents some of the advances that are guiding the new revolution in medicine, discusses the potential and potential barriers to implementation, and suggests crucial elements for the path of incorporation of digital medicine in Mexico.

Araiza-Garaygordobil Diego, Jordán-Ríos Antonio, Sierra-Fernández Carlos, Juárez-Orozco Luis E

2020

Cardiology, Cardiología, Digital health, Digital medicine, Medicina digital, Mexico, México, Salud digital

General General

Artificial Intelligence-Based Neural Network for the Diagnosis of Diabetes: Model Development.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : The incidence of diabetes is increasing in China, and its impact on national health cannot be ignored. Smart medicine is a medical model that uses technology to assist the diagnosis and treatment of disease.

OBJECTIVE : The aim of this paper was to apply artificial intelligence (AI) in the diagnosis of diabetes.

METHODS : We established an AI diagnostic model in the MATLAB software platform based on a backpropagation neural network by collecting data for the cases of integration and extraction and selecting an input feature vector. Based on this diagnostic model, using an intelligent combination of the LabVIEW development platform and the MATLAB software-designed diabetes diagnosis system with user data, we called the neural network diagnostic module to correctly diagnose diabetes.

RESULTS : Compared to conventional diagnostic procedures, the system can effectively improve diagnostic efficiency and save time for physicians.

CONCLUSIONS : The development of AI applications has utility to aid diabetes diagnosis.

Liu Yue

2020-May-27

artificial intelligence, diabetes, neural network

General General

PTML Model for Selection of Nanoparticle, Anticancer Drug, and Vitamin in the Design of Drug-Vitamin Nanoparticle Release Systems for Cancer Co-Therapy.

In Molecular pharmaceutics ; h5-index 60.0

Nano-systems are gaining momentum in pharmaceutical sciences due to the wide variety of possibilities for designing these systems to have specific functions. Specifically, studies of new cancer co-therapy drug-vitamin release nano-systems (DVRNs) including anticancer compounds and vitamins or vitamins derivatives have revealed encouraging results. However, the number of possible combinations of design and synthesis conditions is remarkably high. In addition, there are a high number of anticancer and vitamin derivatives already assayed but a notably less cases of DVRNs assayed as a hole (with the anticancer and the vitamin linked to them). Our approach combine Perturbation Theory and Machine Learning (PTML) to predict the probability of obtaining and interesting DVRN' if we change the anticancer compound and/or the vitamin present in a DVRN already tested for other anticancer' or vitamin' do not tested yet as part of a DVRN. In a previous work, we built a linear PTML model useful for the design of these nano-systems. In so doing, we used Information Fusion (IF) techniques to carry out a data enrichment of DVRNs data compiled from literature with data for preclinical assays of vitamins from ChEMBL database. The design features of DVRNs and the assay conditions nanoparticles and vitamins were included as multiplicative PT Opertators (PTOs) to the system, which gives us a measure of the importance of these variables. However, the previous work omitted experiments with non-linear ML techniques and different types of PTOs such as metric-based PTOs. More importantly, the previous work do not considered the structure of the anticancer drug to be included in the new DVRNs. In this work, we are going to accomplish three main objectives (tasks). In the first task, we found a new model, alternative to the published before, for the rational design of DVRNs using metric-based PTOs. The most accurate PTML model was an Artificial Neural Network (ANN) which showed values of specificity, sensitivity, and accuracy in the range of 90-95% in training and external validation series for more than 130000 cases (DVRNs vs. ChEMBL assays). Furthermore, in a second task, we used IF techniques to carry out a data enrichment of our previous dataset. In so doing, we constructed a new working data set of >970000 cases with data of preclinical assays of DVRNs, vitamins, and anticancer compounds from ChEMBL database. All these assays have multiple continue variables or descriptors dk and categorical variables cj (conditions of assay) for drug (dack, cacj), vitamin (dvk, cvj), and nanoparticle (dnk, cnj). It includes, > 20000 potential anticancer with > 270 protein targets (cac1), > 580 assay cells organisms (cac2), etc. Furthermore, we include > 36000 vitamin derivatives assays in > 6200 types of cell (c2vit), > 120 organisms of assay (c3vit), > 60 assay strains (c4vit) etc. The enriched dataset also contains > 20 types of DVRNs (c5n) with 9 nanoparticle core materials (c4n), 8 synthesis methods (c7n), etc. We expressed all this information with PTOs and trained a PTML model that is a qualitatively new because it also incorporates information of the anticancer drugs. The new model presents 96-97% of accuracy for training and external validation subsets. Last, in a third task, we carry out a comparative study of ML and/or PTML models published and how the models we are presenting cover a gap of knowledge in terms of drug delivery. In conclusion, we present here by the first time a multipurpose PTML model able to select nanoparticles, anticancer compounds, and vitamins and their conditions of assay for DVRNs design.

Santana Ricardo, Zuluaga Robin, Gañán Piedad, Arrasate Sonia, Onieva Enrique, Montemore Matthew M, González-Díaz Humbert

2020-May-27

General General

Identification of genes associated with cancer stem cell characteristics in head and neck squamous cell carcinoma through co-expression network analysis.

In Head & neck ; h5-index 50.0

BACKGROUND : Head and neck squamous cell carcinoma (HNSCC) is a type of invasive malignancy and the seventh most common cancer in the worldwide. Cancer stem cells (CSCs) are self-renewal cells in tumors and can produce heterogeneous tumor cells, which play an important role in the development of HNSCC. In our research, we aimed to identify genes related to the CSCs characteristics in HNSCC.

METHODS : Messenger RNA (mRNA) expression-based stiffness index (mRNAsi) can be used as a quantitative characterization of CSCs. We used one-class logistic regression machine learning algorithm (OCLR) to calculate the mRNAsi and investigate the relationship between mRNAsi and clinical characteristics of HNSCC. Then, a weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network was constructed to screen hub genes related to mRNAsi of HNSCC.

RESULTS : The results indicated that the score of mRNAsi in HNSCC tissues is higher than in paracancer tissues, while the mRNAsi was not statistically correlated with the prognosis and clinical characteristics of HNSCC. Six positive and six negative hub genes related to mRNAsi of HNSCC were selected, which may act as therapeutic targets for inhibiting CSCs within HNSCC.

CONCLUSIONS : In conclusion, our research selected 12 hub genes related to mRNAsi of HNSCC through weighted gene co-expression network analysis. These genes may become therapeutic targets to inhibit the CSCs of HNSCC in the future.

Pei Su, Chen Long, Yang Yunli, Zhu Xiaodong

2020-May-27

CSCs characteristic, HNSCC, TCGA, WGCNA, mRNAsi

General General

Exploring What Is Encoded in Distributional Word Vectors: A Neurobiologically Motivated Analysis.

In Cognitive science

The pervasive use of distributional semantic models or word embeddings for both cognitive modeling and practical application is because of their remarkable ability to represent the meanings of words. However, relatively little effort has been made to explore what types of information are encoded in distributional word vectors. Knowing the internal knowledge embedded in word vectors is important for cognitive modeling using distributional semantic models. Therefore, in this paper, we attempt to identify the knowledge encoded in word vectors by conducting a computational experiment using Binder et al.'s (2016) featural conceptual representations based on neurobiologically motivated attributes. In an experiment, these conceptual vectors are predicted from text-based word vectors using a neural network and linear transformation, and prediction performance is compared among various types of information. The analysis demonstrates that abstract information is generally predicted more accurately by word vectors than perceptual and spatiotemporal information, and specifically, the prediction accuracy of cognitive and social information is higher. Emotional information is also found to be successfully predicted for abstract words. These results indicate that language can be a major source of knowledge about abstract attributes, and they support the recent view that emphasizes the importance of language for abstract concepts. Furthermore, we show that word vectors can capture some types of perceptual and spatiotemporal information about concrete concepts and some relevant word categories. This suggests that language statistics can encode more perceptual knowledge than often expected.

Utsumi Akira

2020-Jun

Abstract words, Computational modeling, Conceptual representation, Distributional semantic models, Language-derived information, Word embedding, Word vectors

Public Health Public Health

Google Street View Derived Built Environment Indicators and Associations with State-Level Obesity, Physical Activity, and Chronic Disease Mortality in the United States.

In International journal of environmental research and public health ; h5-index 73.0

Previous studies have demonstrated that there is a high possibility that the presence of certain built environment characteristics can influence health outcomes, especially those related to obesity and physical activity. We examined the associations between select neighborhood built environment indicators (crosswalks, non-single family home buildings, single-lane roads, and visible wires), and health outcomes, including obesity, diabetes, cardiovascular disease, and premature mortality, at the state level. We utilized 31,247,167 images collected from Google Street View to create indicators for neighborhood built environment characteristics using deep learning techniques. Adjusted linear regression models were used to estimate the associations between aggregated built environment indicators and state-level health outcomes. Our results indicated that the presence of a crosswalk was associated with reductions in obesity and premature mortality. Visible wires were associated with increased obesity, decreased physical activity, and increases in premature mortality, diabetes mortality, and cardiovascular mortality (however, these results were not significant). Non-single family homes were associated with decreased diabetes and premature mortality, as well as increased physical activity and park and recreational access. Single-lane roads were associated with increased obesity and decreased park access. The findings of our study demonstrated that built environment features may be associated with a variety of adverse health outcomes.

Phan Lynn, Yu Weijun, Keralis Jessica M, Mukhija Krishay, Dwivedi Pallavi, Brunisholz Kimberly D, Javanmardi Mehran, Tasdizen Tolga, Nguyen Quynh C

2020-May-22

big data, built environment, cardiovascular disease, diabetes, google street view, mortality, obesity, physical activity

General General

Improvements in Patient Monitoring for the Intensive Care Unit: Survey Study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Due to demographic change and, more recently, the Coronavirus Disease 2019 (COVID-19), the importance of modern intensive care units (ICU) is becoming apparent. One of the key components of an ICU is the continuous monitoring of patients' vital parameters. However, existing advances in informatics, signal processing, or engineering that could alleviate the burden on ICUs have not yet been applied. This could be related to the lack of user involvement in research and development.

OBJECTIVE : This study focused on satisfaction of ICU staff with the current patient monitoring and their suggestions for future improvements. We aimed to identify aspects disturbing patient care, display devices for remote monitoring, use cases for artificial intelligence (AI), and whether ICU staff is willing to improve their digital literacy or contribute to the improvement of patient monitoring. We further desired to uncover differences in the responses of the professional groups.

METHODS : This survey study was realized with ICU staff from four ICUs of a German university hospital between November 2019 and January 2020. We developed a web-based 36-item survey questionnaire by analyzing a preceding qualitative interview study with ICU staff about clinical requirements of future patient monitoring. Statistical analyses of questionnaire results included median values with their bootstrapped 95% confidence intervals, and Chi-square tests to compare the distributions of item responses of the professional groups.

RESULTS : Eighty-six of the 270 ICU physicians and nurses completed the survey questionnaire. The majority stated to feel confident using the patient monitoring, but high rates of false positive alarms and the many sensor cables were considered to disturb patient care. Wireless sensors, reduction of false positive alarms and hospital standard operating procedures (SOP) for alarm management were demanded. Responses to the display devices proposed for remote patient monitoring were split. Regarding its use, most respondents indicated responsibility for multiple wards or earlier alerting. AI for ICUs would be useful for early detection of complications and increased risk of mortality, as well as to have guidelines for therapy and diagnostics proposed. Transparency, interoperability, usability, and staff training were essential to promote usage of an AI. The majority wanted to learn more about new technologies for ICU and desired more time for it. Physicians had fewer reservations than nurses about using mobile phones for remote monitoring, and AI-based intelligent alarm management.

CONCLUSIONS : This survey study among ICU staff revealed key improvements for patient monitoring in intensive care medicine. Hospital providers and medical device manufacturers should focus on reducing false alarms, implementing hospital alarm SOPs, introducing wireless sensors, preparing for the use of AI, and enhancing digital literacy of ICU staff. Our results may contribute to the user-centered transfer of digital technologies into practice to alleviate challenges in intensive care medicine.

CLINICALTRIAL : ClinicalTrials.gov NCT03514173; https://clinicaltrials.gov/ct2/show/NCT03514173.

Poncette Akira-Sebastian, Mosch Lina, Spies Claudia, Schmieding Malte, Schiefenhövel Fridtjof, Krampe Henning, Balzer Felix

2020-May-13

Cardiology Cardiology

Relationship between post-IVIG IgG levels and clinical outcomes in Kawasaki disease patients: new insight into the mechanism of action of IVIG.

In Clinical rheumatology ; h5-index 39.0

INTRODUCTION/OBJECTIVES : The dosing of intravenous immunoglobulin (IVIG) therapy for Kawasaki disease (KD) has been a matter of debate for decades, with recent studies implicating that larger doses lead to better outcomes. Despite this, few have investigated post-IVIG infusion immunoglobulin G (IgG) levels in relation to outcomes of KD such as response to IVIG and development of coronary artery abnormalities (CAAs). The present study investigated how varying levels of post-infusion IgG affected these outcomes.

METHOD : We collected demographic and laboratory data, including post-infusion IgG, from children with KD who were admitted to six hospitals in Japan between 2006 and 2012. We conducted multivariate analyses to examine the relationship between independent variables and non-response to IVIG and development of CAAs. We used random forest, a decision tree-based machine learning tool, to investigate the marginal effect of varying post-infusion IgG levels on non-response to IVIG and development of CAAs.

RESULTS : Of 456 patients included in the study, 130 (28.5%) were non-responders and 38 (8.3%) developed CAAs. Sodium, post-infusion IgG, and AST were significantly associated with non-response. Post-infusion IgG and sodium were significantly associated with CAA development. The random forest plots revealed a decrease in non-response and CAA rates with increasing post-infusion IgG until post-infusion IgG was near the median (2821 mg/dL), after which the non-response and CAA rates leveled off.

CONCLUSIONS : Greater post-infusion IgG is associated with better response to IVIG and decreased CAA development in KD patients, but this effect levels off at post-infusion IgG levels greater than the median.Key points• Though previous studies have shown that post-intravenous immunoglobulin (IVIG) infusion immunoglobulin G (IgG) is associated with non-response to IVIG therapy and coronary artery abnormality (CAA) development in Kawasaki disease (KD) patients, no study has investigated the relationship between varying levels of post-infusion IgG and these clinical outcomes.• Our study showed that non-response to IVIG therapy and CAA development in Kawasaki disease patients follow a decreasing trend with increasing post-infusion IgG at post-infusion IgG levels below the median.• At values of post-infusion IgG greater than the median, non-response and CAA development rates remain relatively constant with increasing post-infusion IgG.• Our study suggests that when post-infusion IgG is greater than the median, IgG may have fully bound to the therapeutic targets of KD, and in these patients, there may be limited benefit in administering additional IVIG.

Goto Ryunosuke, Inuzuka Ryo, Shindo Takahiro, Namai Yoshiyuki, Oda Yoichiro, Harita Yutaka, Oka Akira

2020-May-27

Coronary artery abnormality, Machine learning, Non-response, Random forest

Radiology Radiology

Artificial intelligence in gastroenterology: where are we heading?

In Frontiers of medicine

Artificial intelligence (AI) is coming to medicine in a big wave. From making diagnosis in various medical conditions, following the latest advancements in scientific literature, suggesting appropriate therapies, to predicting prognosis and outcome of diseases and conditions, AI is offering unprecedented possibilities to improve care for patients. Gastroenterology is a field that AI can make a significant impact. This is partly because the diagnosis of gastrointestinal conditions relies a lot on image-based investigations and procedures (endoscopy and radiology). AI-assisted image analysis can make accurate assessment and provide more information than conventional analysis. AI integration of genomic, epigenetic, and metagenomic data may offer new classifications of gastrointestinal cancers and suggest optimal personalized treatments. In managing relapsing and remitting diseases such as inflammatory bowel disease, irritable bowel syndrome, and peptic ulcer bleeding, convoluted neural network may formulate models to predict disease outcome, enhancing treatment efficacy. AI and surgical robots can also assist surgeons in conducting gastrointestinal operations. While the advancement and new opportunities are exciting, the responsibility and liability issues of AI-assisted diagnosis and management need much deliberations.

Sung Joseph J Y, Poon Nicholas C H

2020-May-26

artificial intelligence, endoscopy, gastrointestinal diseases, robotics

Radiology Radiology

Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors.

In European radiology ; h5-index 62.0

OBJECTIVE : The objective was to identify barriers and facilitators to the implementation of artificial intelligence (AI) applications in clinical radiology in The Netherlands.

MATERIALS AND METHODS : Using an embedded multiple case study, an exploratory, qualitative research design was followed. Data collection consisted of 24 semi-structured interviews from seven Dutch hospitals. The analysis of barriers and facilitators was guided by the recently published Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework for new medical technologies in healthcare organizations.

RESULTS : Among the most important facilitating factors for implementation were the following: (i) pressure for cost containment in the Dutch healthcare system, (ii) high expectations of AI's potential added value, (iii) presence of hospital-wide innovation strategies, and (iv) presence of a "local champion." Among the most prominent hindering factors were the following: (i) inconsistent technical performance of AI applications, (ii) unstructured implementation processes, (iii) uncertain added value for clinical practice of AI applications, and (iv) large variance in acceptance and trust of direct (the radiologists) and indirect (the referring clinicians) adopters.

CONCLUSION : In order for AI applications to contribute to the improvement of the quality and efficiency of clinical radiology, implementation processes need to be carried out in a structured manner, thereby providing evidence on the clinical added value of AI applications.

KEY POINTS : • Successful implementation of AI in radiology requires collaboration between radiologists and referring clinicians. • Implementation of AI in radiology is facilitated by the presence of a local champion. • Evidence on the clinical added value of AI in radiology is needed for successful implementation.

Strohm Lea, Hehakaya Charisma, Ranschaert Erik R, Boon Wouter P C, Moors Ellen H M

2020-May-26

Artificial intelligence, Computer systems, Computer-assisted, Diagnosis, Information systems, Radiology

Public Health Public Health

Hybrid Stem Cell States: Insights Into the Relationship Between Mammary Development and Breast Cancer Using Single-Cell Transcriptomics.

In Frontiers in cell and developmental biology

Similarities between stem cells and cancer cells have implicated mammary stem cells in breast carcinogenesis. Recent evidence suggests that normal breast stem cells exist in multiple phenotypic states: epithelial, mesenchymal, and hybrid epithelial/mesenchymal (E/M). Hybrid E/M cells in particular have been implicated in breast cancer metastasis and poor prognosis. Mounting evidence also suggests that stem cell phenotypes change throughout the life course, for example, through embryonic development and pregnancy. The goal of this study was to use single cell RNA-sequencing to quantify cell state distributions of the normal mammary (NM) gland throughout developmental stages and when perturbed into a stem-like state in vitro using conditional reprogramming (CR). Using machine learning based dataset alignment, we integrate multiple mammary gland single cell RNA-seq datasets from human and mouse, along with bulk RNA-seq data from breast tumors in the Cancer Genome Atlas (TCGA), to interrogate hybrid stem cell states in the normal mammary gland and cancer. CR of human mammary cells induces an expanded stem cell state, characterized by increased expression of embryonic stem cell associated genes. Alignment to a mouse single-cell transcriptome atlas spanning mammary gland development from in utero to adulthood revealed that NM cells align to adult mouse cells and CR cells align across the pseudotime trajectory with a stem-like population aligning to the embryonic mouse cells. Three hybrid populations emerge after CR that are rare in NM: KRT18+/KRT14+ (hybrid luminal/basal), EPCAM+/VIM+ (hybrid E/M), and a quadruple positive population, expressing all four markers. Pseudotime analysis and alignment to the mouse developmental trajectory revealed that E/M hybrids are the most developmentally immature. Analyses of single cell mouse mammary RNA-seq throughout pregnancy show that during gestation, there is an enrichment of hybrid E/M cells, suggesting that these cells play an important role in mammary morphogenesis during lactation. Finally, pseudotime analysis and alignment of TCGA breast cancer expression data revealed that breast cancer subtypes express distinct developmental signatures, with basal tumors representing the most "developmentally immature" phenotype. These results highlight phenotypic plasticity of normal mammary stem cells and provide insight into the relationship between hybrid cell populations, stemness, and cancer.

Thong Tasha, Wang Yutong, Brooks Michael D, Lee Christopher T, Scott Clayton, Balzano Laura, Wicha Max S, Colacino Justin A

2020

breast cancer, epithelial, hybrid, mesenchymal, pregnancy, single-cell RNA sequencing, stem cells

General General

Lower Limb Kinematics Trajectory Prediction Using Long Short-Term Memory Neural Networks.

In Frontiers in bioengineering and biotechnology

This study determined whether the kinematics of lower limb trajectories during walking could be extrapolated using long short-term memory (LSTM) neural networks. It was hypothesised that LSTM auto encoders could reliably forecast multiple time-step trajectories of the lower limb kinematics, specifically linear acceleration (LA) and angular velocity (AV). Using 3D motion capture, lower limb position-time coordinates were sampled (100 Hz) from six male participants (age 22 ± 2 years, height 1.77 ± 0.02 m, body mass 82 ± 4 kg) who walked for 10 min at 5 km/h on a 0% gradient motor-driven treadmill. These data were fed into an LSTM model with a sliding window of four kinematic variables with 25 samples or time steps: LA and AV for thigh and shank. The LSTM was tested to forecast five samples (i.e., time steps) of the four kinematic input variables. To attain generalisation, the model was trained on a dataset of 2,665 strides from five participants and evaluated on a test set of 1 stride from a sixth participant. The LSTM model learned the lower limb kinematic trajectories using the training samples and tested for generalisation across participants. The forecasting horizon suggested higher model reliability in predicting earlier future trajectories. The mean absolute error (MAE) was evaluated on each variable across the single tested stride, and for the five-sample forecast, it obtained 0.047 m/s2 thigh LA, 0.047 m/s2 shank LA, 0.028 deg/s thigh AV and 0.024 deg/s shank AV. All predicted trajectories were highly correlated with the measured trajectories, with correlation coefficients greater than 0.98. The motion prediction model may have a wide range of applications, such as mitigating the risk of falls or balance loss and improving the human-machine interface for wearable assistive devices.

Zaroug Abdelrahman, Lai Daniel T H, Mudie Kurt, Begg Rezaul

2020

LSTM, forecasting, gait, machine learning, neural networks, walking

General General

Extracellular Vesicles in Renal Cell Carcinoma: Multifaceted Roles and Potential Applications Identified by Experimental and Computational Methods.

In Frontiers in oncology

Renal cell carcinoma (RCC) is the most common type of kidney cancer. Increasingly evidences indicate that extracellular vesicles (EVs) orchestrate multiple processes in tumorigenesis, metastasis, immune evasion, and drug response of RCC. EVs are lipid membrane-bound vesicles in nanometer size and secreted by almost all cell types into the extracellular milieu. A myriad of bioactive molecules such as RNA, DNA, protein, and lipid are able to be delivered via EVs for the intercellular communication. Hence, the abundant content of EVs is appealing reservoir for biomarker identification through computational analysis and experimental validation. EVs with excellent biocompatibility and biodistribution are natural platforms that can be engineered to offer achievable drug delivery strategies for RCC therapies. Moreover, the multifaceted roles of EVs in RCC progression also provide substantial targets and facilitate EVs-based drug discovery, which will be accelerated by using artificial intelligence approaches. In this review, we summarized the vital roles of EVs in occurrence, metastasis, immune evasion, and drug resistance of RCC. Furthermore, we also recapitulated and prospected the EVs-based potential applications in RCC, including biomarker identification, drug vehicle development as well as drug target discovery.

Qin Zhiyuan, Xu Qingwen, Hu Haihong, Yu Lushan, Zeng Su

2020

artificial intelligence, biomarkers, drug targets, drug vehicles, exosomes, extracellular vesicles, machine learning, renal cell carcinoma

General General

Digital Innovations for Global Mental Health: Opportunities for Data Science, Task Sharing, and Early Intervention.

In Current treatment options in psychiatry

Purpose : Globally, individuals living with mental disorders are more likely to have access to a mobile phone than mental health care. In this commentary, we highlight opportunities for expanding access to and use of digital technologies to advance research and intervention in mental health, with emphasis on the potential impact in lower resource settings.

Recent findings : Drawing from empirical evidence, largely from higher income settings, we considered three emerging areas where digital technology will potentially play a prominent role: supporting methods in data science to further our understanding of mental health and inform interventions, task sharing for building workforce capacity by training and supervising non-specialist health workers, and facilitating new opportunities for early intervention for young people in lower resource settings. Challenges were identified related to inequities in access, threats of bias in big data analyses, risks to users, and need for user involvement to support engagement and sustained use of digital interventions.

Summary : For digital technology to achieve its potential to transform the ways we detect, treat, and prevent mental disorders, there is a clear need for continued research involving multiple stakeholders, and rigorous studies showing that these technologies can successfully drive measurable improvements in mental health outcomes.

Naslund John A, Gonsalves Pattie P, Gruebner Oliver, Pendse Sachin R, Smith Stephanie L, Sharma Amit, Raviola Giuseppe

2019-Dec

Artificial intelligence, Big data, Digital technology, Early intervention, Global mental health, Task sharing

General General

Circular RNA in Schizophrenia and Depression.

In Frontiers in psychiatry

Schizophrenia (SZ) and depression (DEP) are two common major psychiatric disorders that are associated with high risk of suicide. These disorders affect not only physical and mental health, but they also affect the social function of the individual. However, diagnoses of SZ and DEP are mainly based on symptomatic changes and the clinical experience of psychiatrists. These rather subjective measures can induce misdiagnoses and missed diagnoses. Therefore, it is necessary to further explore objective indexes for improving the early diagnoses and prognoses of SZ and DEP. Current research indicates that non-coding RNA (ncRNA) may play a role in the occurrence and development of SZ and DEP. Circular RNA (circRNA), as an important component of ncRNA, is associated with many biological functions, especially post-transcriptional regulation. Since circRNA is easily detected in peripheral blood and has a high degree of spatiotemporal tissue specificity and stability, these attributes provide us with a new idea to further explore the potential value for the diagnosis and treatment of SZ and DEP. Here, we summarize the classification, characteristics, and biological functions of circRNA and the most significant results of experimental studies, aiming to highlight the involvement of circRNA in SZ and DEP.

Li Zexuan, Liu Sha, Li Xinrong, Zhao Wentao, Li Jing, Xu Yong

2020

biological function, circular RNA (circRNA), depression (DEP), epigenetic characteristics, expression, schizophrenia (SZ)

General General

Application of Neural Network and Cluster Analyses to Differentiate TCM Patterns in Patients With Breast Cancer.

In Frontiers in pharmacology

Background and Purpose : Pattern differentiation is a critical element of the prescription process for Traditional Chinese Medicine (TCM) practitioners. Application of advanced machine learning techniques will enhance the effectiveness of TCM in clinical practice. The aim of this study is to explore the relationships between clinical features and TCM patterns in breast cancer patients.

Methods : The dataset of breast cancer patients receiving TCM treatment was recruited from a single medical center. We utilized a neural network model to standardize terminologies and address TCM pattern differentiation in breast cancer cases. Cluster analysis was applied to classify the clinical features in the breast cancer patient dataset. To evaluate the performance of the proposed method, we further compared the TCM patterns to therapeutic principles of Chinese herbal medication in Taiwan.

Results : A total of 2,738 breast cancer cases were recruited and standardized. They were divided into 5 groups according to clinical features via cluster analysis. The pattern differentiation model revealed that liver-gallbladder dampness-heat was the primary TCM pattern identified in patients. The main therapeutic goals of the top 10 Chinese herbal medicines prescribed for breast cancer patients were to clear heat, drain dampness, and detoxify. These results demonstrated that the neural network successfully identified patterns from a dataset similar to the prescriptions of TCM clinical practitioners.

Conclusion : This is the first study using machine-learning methodology to standardize and analyze TCM electronic medical records. The patterns revealed by the analyses were highly correlated with the therapeutic principles of TCM practitioners. Machine learning technology could assist TCM practitioners to comprehensively differentiate patterns and identify effective Chinese herbal medicine treatments in clinical practice.

Huang Wei-Te, Hung Hao-Hsiu, Kao Yi-Wei, Ou Shi-Chen, Lin Yu-Chuan, Cheng Wei-Zen, Yen Zi-Rong, Li Jian, Chen Mingchih, Shia Ben-Chang, Huang Sheng-Teng

2020

breast cancer, cluster analysis, electronic medical records, neural network analysis, pattern differentiation, traditional Chinese medicine

General General

Chronic within-hive video recordings detect altered nursing behaviour and retarded larval development of neonicotinoid treated honey bees.

In Scientific reports ; h5-index 158.0

Risk evaluations for agricultural chemicals are necessary to preserve healthy populations of honey bee colonies. Field studies on whole colonies are limited in behavioural research, while results from lab studies allow only restricted conclusions on whole colony impacts. Methods for automated long-term investigations of behaviours within comb cells, such as brood care, were hitherto missing. In the present study, we demonstrate an innovative video method that enables within-cell analysis in honey bee (Apis mellifera) observation hives to detect chronic sublethal neonicotinoid effects of clothianidin (1 and 10 ppb) and thiacloprid (200 ppb) on worker behaviour and development. In May and June, colonies which were fed 10 ppb clothianidin and 200 ppb thiacloprid in syrup over three weeks showed reduced feeding visits and duration throughout various larval development days (LDDs). On LDD 6 (capping day) total feeding duration did not differ between treatments. Behavioural adaptation was exhibited by nurses in the treatment groups in response to retarded larval development by increasing the overall feeding timespan. Using our machine learning algorithm, we demonstrate a novel method for detecting behaviours in an intact hive that can be applied in a versatile manner to conduct impact analyses of chemicals, pests and other stressors.

Siefert Paul, Hota Rudra, Ramesh Visvanathan, Grünewald Bernd

2020-May-26

Radiology Radiology

Gold Nanoparticle Mediated Multi-Modal CT Imaging of Hsp70 Membrane-Positive Tumors.

In Cancers

Imaging techniques such as computed tomographies (CT) play a major role in clinical imaging and diagnosis of malignant lesions. In recent years, metal nanoparticle platforms enabled effective payload delivery for several imaging techniques. Due to the possibility of surface modification, metal nanoparticles are predestined to facilitate molecular tumor targeting. In this work, we demonstrate the feasibility of anti-plasma membrane Heat shock protein 70 (Hsp70) antibody functionalized gold nanoparticles (cmHsp70.1-AuNPs) for tumor-specific multimodal imaging. Membrane-associated Hsp70 is exclusively presented on the plasma membrane of malignant cells of multiple tumor entities but not on corresponding normal cells, predestining this target for a tumor-selective in vivo imaging. In vitro microscopic analysis revealed the presence of cmHsp70.1-AuNPs in the cytosol of tumor cell lines after internalization via the endo-lysosomal pathway. In preclinical models, the biodistribution as well as the intratumoral enrichment of AuNPs were examined 24 h after i.v. injection in tumor-bearing mice. In parallel to spectral CT analysis, histological analysis confirmed the presence of AuNPs within tumor cells. In contrast to control AuNPs, a significant enrichment of cmHsp70.1-AuNPs has been detected selectively inside tumor cells in different tumor mouse models. Furthermore, a machine-learning approach was developed to analyze AuNP accumulations in tumor tissues and organs. In summary, utilizing mHsp70 on tumor cells as a target for the guidance of cmHsp70.1-AuNPs facilitates an enrichment and uniform distribution of nanoparticles in mHsp70-expressing tumor cells that enables various microscopic imaging techniques and spectral-CT-based tumor delineation in vivo.

Kimm Melanie A, Shevtsov Maxim, Werner Caroline, Sievert Wolfgang, Zhiyuan Wu, Schoppe Oliver, Menze Bjoern H, Rummeny Ernst J, Proksa Roland, Bystrova Olga, Martynova Marina, Multhoff Gabriele, Stangl Stefan

2020-May-22

biomarker, gold nanoparticle, heat shock protein 70, molecular imaging, spectral-CT

General General

Advanced Digital Health Technologies for COVID-19 and Future Emergencies.

In Telemedicine journal and e-health : the official journal of the American Telemedicine Association

Background: Coronavirus disease 2019 (COVID-19) has led to a national health care emergency in the United States and exposed resource shortages, particularly of health care providers trained to provide critical or intensive care. This article describes how digital health technologies are being or could be used for COVID-19 mitigation. It then proposes the National Emergency Tele-Critical Care Network (NETCCN), which would combine digital health technologies to address this and future crises. Methods: Subject matter experts from the Society of Critical Care Medicine and the Telemedicine and Advanced Technology Research Center examined the peer-reviewed literature and science/technology news to see what digital health technologies have already been or could be implemented to (1) support patients while limiting COVID-19 transmission, (2) increase health care providers' capability and capacity, and (3) predict/prevent future outbreaks. Results: Major technologies identified included telemedicine and mobile care (for COVID-19 as well as routine care), tiered telementoring, telecritical care, robotics, and artificial intelligence for monitoring. Several of these could be assimilated to form an interoperable scalable NETCCN. NETCCN would assist health care providers, wherever they are located, by obtaining real-time patient and supplies data and disseminating critical care expertise. NETCCN capabilities should be maintained between disasters and regularly tested to ensure continual readiness. Conclusions: COVID-19 has demonstrated the impact of a large-scale health emergency on the existing infrastructures. Short term, an approach to meeting this challenge is to adopt existing digital health technologies. Long term, developing a NETCCN may ensure that the necessary ecosystem is available to respond to future emergencies.

Scott Benjamin K, Miller Geoffrey T, Fonda Stephanie J, Yeaw Ronald E, Gaudaen James C, Pavliscsak Holly H, Quinn Matthew T, Pamplin Jeremy C

2020-May-26

coronavirus, critical care, digital health, emergencies, natural disasters, pandemics, telemedicine

General General

Real-time Inference and Detection of Disruptive EEG Networks for Epileptic Seizures.

In Scientific reports ; h5-index 158.0

Recent studies in brain science and neurological medicine paid a particular attention to develop machine learning-based techniques for the detection and prediction of epileptic seizures with electroencephalogram (EEG). As a noninvasive monitoring method to record brain electrical activities, EEG has been widely used for capturing the underlying dynamics of disruptive neuronal responses across the brain in real-time to provide clinical guidance in support of epileptic seizure treatments in practice. In this study, we introduce a novel dynamic learning method that first infers a time-varying network constituted by multivariate EEG signals, which represents the overall dynamics of the brain network, and subsequently quantifies its topological property using graph theory. We demonstrate the efficacy of our learning method to detect relatively strong synchronization (characterized by the algebraic connectivity metric) caused by abnormal neuronal firing during a seizure onset. The computational results for a realistic scalp EEG database show a detection rate of 93.6% and a false positive rate of 0.16 per hour (FP/h); furthermore, our method observes potential pre-seizure phenomena in some cases.

Bomela Walter, Wang Shuo, Chou Chun-An, Li Jr-Shin

2020-May-26

General General

A deep learning approach for designed diffraction-based acoustic patterning in microchannels.

In Scientific reports ; h5-index 158.0

Acoustic waves can be used to accurately position cells and particles and are appropriate for this activity owing to their biocompatibility and ability to generate microscale force gradients. Such fields, however, typically take the form of only periodic one or two-dimensional grids, limiting the scope of patterning activities that can be performed. Recent work has demonstrated that the interaction between microfluidic channel walls and travelling surface acoustic waves can generate spatially variable acoustic fields, opening the possibility that the channel geometry can be used to control the pressure field that develops. In this work we utilize this approach to create novel acoustic fields. Designing the channel that results in a desired acoustic field, however, is a non-trivial task. To rapidly generate designed acoustic fields from microchannel elements we utilize a deep learning approach based on a deep neural network (DNN) that is trained on images of pre-solved acoustic fields. We use then this trained DNN to create novel microchannel architectures for designed microparticle patterning.

Raymond Samuel J, Collins David J, O’Rorke Richard, Tayebi Mahnoush, Ai Ye, Williams John

2020-May-26

General General

Non-invasive identification of swallows via deep learning in high resolution cervical auscultation recordings.

In Scientific reports ; h5-index 158.0

High resolution cervical auscultation is a very promising noninvasive method for dysphagia screening and aspiration detection, as it does not involve the use of harmful ionizing radiation approaches. Automatic extraction of swallowing events in cervical auscultation is a key step for swallowing analysis to be clinically effective. Using time-varying spectral estimation of swallowing signals and deep feed forward neural networks, we propose an automatic segmentation algorithm for swallowing accelerometry and sounds that works directly on the raw swallowing signals in an online fashion. The algorithm was validated qualitatively and quantitatively using the swallowing data collected from 248 patients, yielding over 3000 swallows manually labeled by experienced speech language pathologists. With a detection accuracy that exceeded 95%, the algorithm has shown superior performance in comparison to the existing algorithms and demonstrated its generalizability when tested over 76 completely unseen swallows from a different population. The proposed method is not only of great importance to any subsequent swallowing signal analysis steps, but also provides an evidence that such signals can capture the physiological signature of the swallowing process.

Khalifa Yassin, Coyle James L, Sejdić Ervin

2020-May-26

General General

Evidence of Biorealistic Synaptic Behavior in Diffusive Li-based Two-terminal Resistive Switching Devices.

In Scientific reports ; h5-index 158.0

Following the recent advances in artificial synaptic devices and the renewed interest regarding artificial intelligence and neuromorphic computing, a new two-terminal resistive switching device, based on mobile Li+ ions is hereby explored. Emulation of neural functionalities in a biorealistic manner has been recently implemented through the use of synaptic devices with diffusive dynamics. Mimicking of the spontaneous synaptic weight relaxation of neuron cells, which is regulated by the concentration kinetics of positively charged ions like Ca2+, is facilitated through the conductance relaxation of such diffusive devices. Adopting a battery-like architecture, using LiCoO2 as a resistive switching cathode layer, SiOx as an electrolyte and TiO2 as an anode, Au/LiCoO2/SiOx/TiO2/p++-Si two-terminal devices have been fabricated. Analog conductance modulation, via voltage-driven regulation of Li+ ion concentration in the cathode and anode layers, along with current rectification and nanobattery effects are reported. Furthermore, evidence is provided for biorealistic synaptic behavior, manifested as paired pulse facilitation based on the summation of excitatory post-synaptic currents and spike-timing-dependent plasticity, which are governed by the Li+ ion concentration and its relaxation dynamics.

Ioannou Panagiotis S, Kyriakides Evripides, Schneegans Olivier, Giapintzakis John

2020-May-26

Public Health Public Health

Predicting Phenotypic Polymyxin Resistance in Klebsiella pneumoniae through Machine Learning Analysis of Genomic Data.

In mSystems

Polymyxins are used as treatments of last resort for Gram-negative bacterial infections. Their increased use has led to concerns about emerging polymyxin resistance (PR). Phenotypic polymyxin susceptibility testing is resource intensive and difficult to perform accurately. The complex polygenic nature of PR and our incomplete understanding of its genetic basis make it difficult to predict PR using detection of resistance determinants. We therefore applied machine learning (ML) to whole-genome sequencing data from >600 Klebsiella pneumoniae clonal group 258 (CG258) genomes to predict phenotypic PR. Using a reference-based representation of genomic data with ML outperformed a rule-based approach that detected variants in known PR genes (area under receiver-operator curve [AUROC], 0.894 versus 0.791, P = 0.006). We noted modest increases in performance by using a bacterial genome-wide association study to filter relevant genomic features and by integrating clinical data in the form of prior polymyxin exposure. Conversely, reference-free representation of genomic data as k-mers was associated with decreased performance (AUROC, 0.692 versus 0.894, P = 0.015). When ML models were interpreted to extract genomic features, six of seven known PR genes were correctly identified by models without prior programming and several genes involved in stress responses and maintenance of the cell membrane were identified as potential novel determinants of PR. These findings are a proof of concept that whole-genome sequencing data can accurately predict PR in K. pneumoniae CG258 and may be applicable to other forms of complex antimicrobial resistance.IMPORTANCE Polymyxins are last-resort antibiotics used to treat highly resistant Gram-negative bacteria. There are increasing reports of polymyxin resistance emerging, raising concerns of a postantibiotic era. Polymyxin resistance is therefore a significant public health threat, but current phenotypic methods for detection are difficult and time-consuming to perform. There have been increasing efforts to use whole-genome sequencing for detection of antibiotic resistance, but this has been difficult to apply to polymyxin resistance because of its complex polygenic nature. The significance of our research is that we successfully applied machine learning methods to predict polymyxin resistance in Klebsiella pneumoniae clonal group 258, a common health care-associated and multidrug-resistant pathogen. Our findings highlight that machine learning can be successfully applied even in complex forms of antibiotic resistance and represent a significant contribution to the literature that could be used to predict resistance in other bacteria and to other antibiotics.

Macesic Nenad, Bear Don’t Walk Oliver J, Pe’er Itsik, Tatonetti Nicholas P, Peleg Anton Y, Uhlemann Anne-Catrin

2020-May-26

antimicrobial resistance, genotype, machine learning, phenotype, prediction

General General

Monitoring Big Data During Mechanical Ventilation in the ICU.

In Respiratory care ; h5-index 37.0

The electronic health record allows the assimilation of large amounts of clinical and laboratory data. Big data describes the analysis of large data sets using computational modeling to reveal patterns, trends, and associations. How can big data be used to predict ventilator discontinuation or impending compromise, and how can it be incorporated into the clinical workflow? This article will serve 2 purposes. First, a general overview is provided for the layperson and introduces key concepts, definitions, best practices, and things to watch out for when reading a paper that incorporates machine learning. Second, recent publications at the intersection of big data, machine learning, and mechanical ventilation are presented.

Smallwood Craig D

2020-Jun

big data, data science, machine learning, mechanical ventilation, neural network

General General

Monitoring Asynchrony During Invasive Mechanical Ventilation.

In Respiratory care ; h5-index 37.0

Mechanical ventilation in critically ill patients must effectively unload inspiratory muscles and provide safe ventilation (ie, enhancing gas exchange, protect the lungs and the diaphragm). To do that, the ventilator should be in synchrony with patient's respiratory rhythm. The complexity of such interplay leads to several concerning issues that clinicians should be able to recognize. Asynchrony between the patient and the ventilator may induce several deleterious effects that require a proper physiological understanding to recognize and manage them. Different tools have been developed and proposed beyond the careful analysis of the ventilator waveforms to help clinicians in the decision-making process. Moreover, appropriate handling of asynchrony requires clinical skills, physiological knowledge, and suitable medication management. New technologies and devices are changing our daily practice, from automated real-time recognition of asynchronies and their distribution during mechanical ventilation, to smart alarms and artificial intelligence algorithms based on physiological big data and personalized medicine. Our goal as clinicians is to provide care of patients based on the most accurate and current knowledge, and to incorporate new technological methods to facilitate and improve the care of the critically ill.

Esperanza José Aquino, Sarlabous Leonardo, de Haro Candelaria, Magrans Rudys, Lopez-Aguilar Josefina, Blanch Lluis

2020-Jun

asynchrony, mechanical ventilation, patient–ventilator interactions, respiratory monitoring, respiratory physiology

General General

Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis.

In Proceedings of the National Academy of Sciences of the United States of America

Artificial intelligence (AI) systems for computer-aided diagnosis and image-based screening are being adopted worldwide by medical institutions. In such a context, generating fair and unbiased classifiers becomes of paramount importance. The research community of medical image computing is making great efforts in developing more accurate algorithms to assist medical doctors in the difficult task of disease diagnosis. However, little attention is paid to the way databases are collected and how this may influence the performance of AI systems. Our study sheds light on the importance of gender balance in medical imaging datasets used to train AI systems for computer-assisted diagnosis. We provide empirical evidence supported by a large-scale study, based on three deep neural network architectures and two well-known publicly available X-ray image datasets used to diagnose various thoracic diseases under different gender imbalance conditions. We found a consistent decrease in performance for underrepresented genders when a minimum balance is not fulfilled. This raises the alarm for national agencies in charge of regulating and approving computer-assisted diagnosis systems, which should include explicit gender balance and diversity recommendations. We also establish an open problem for the academic medical image computing community which needs to be addressed by novel algorithms endowed with robustness to gender imbalance.

Larrazabal Agostina J, Nieto Nicolás, Peterson Victoria, Milone Diego H, Ferrante Enzo

2020-May-26

computer-aided diagnosis, deep learning, gender bias, gendered innovations, medical image analysis

General General

Network effects govern the evolution of maritime trade.

In Proceedings of the National Academy of Sciences of the United States of America

Maritime transport accounts for over 80% of the world trade volume and is the backbone of the global economy. Global supply chains create a complex network of trade flows. The structure of this network impacts not only the socioeconomic development of the concerned regions but also their ecosystems. The movements of ships are a considerable source of CO2 emissions and contribute to climate change. In the wake of the announced development of Arctic shipping, the need to understand the behavior of the maritime trade network and to predict future trade flows becomes pressing. We use a unique database of daily movements of the world fleet over the period 1977-2008 and apply machine learning techniques on network data to develop models for predicting the opening of new shipping lines and for forecasting trade volume on links. We find that the evolution of this system is governed by a simple rule from network science, relying on the number of common neighbors between pairs of ports. This finding is consistent over all three decades of temporal data. We further confirm it with a natural experiment, involving traffic redirection from the port of Kobe after the 1995 earthquake. Our forecasting method enables researchers and industry to easily model effects of potential future scenarios at the level of ports, regions, and the world. Our results also indicate that maritime trade flows follow a form of random walk on the underlying network structure of sea connections, highlighting its pivotal role in the development of maritime trade.

Kosowska-Stamirowska Zuzanna

2020-May-26

evolving networks, machine learning, maritime trade, network science, transport networks

Public Health Public Health

Simulating the Influence of Conjugative Plasmids Kinetic Values on the Multilevel Dynamics of Antimicrobial Resistance in a Membrane Computing Model.

In Antimicrobial agents and chemotherapy ; h5-index 79.0

Bacterial plasmids harboring antibiotic resistance genes are critical in the spread of antibiotic resistance. It is known that plasmids differ in their kinetic values i.e. conjugation rate, segregation rate by copy-number incompatibility with related plasmids, and rate of stochastic loss during replication. They also differ in cost to the cell in terms of reducing fitness and in the frequency of compensatory mutations compensating plasmid cost. However, we do not know how variation in these values influences the success of a plasmid and their resistance genes in complex ecosystems, as the microbiota. Genes are in plasmids, plasmids in cells, cells in bacterial populations and microbiotas, which are inside hosts, hosts in human communities at the hospital or the community, under various levels of cross-colonization and antibiotic exposure. Differences in plasmid kinetics might have consequences on the global spread of antibiotic resistance. New membrane computing methods help to predict these consequences. In our simulation, conjugation frequency of at least 10-3 influences the dominance of a strain with a resistance plasmid. Coexistence of different antibiotic resistances occur if host strains can maintain two copies of similar plasmids. Plasmid loss rates of 10-4 or 10-5 or plasmid fitness costs ≥0.06 favor plasmids located in the most abundant species. The beneficial effect of compensatory mutations for plasmid fitness cost is proportional to this cost at high mutation frequencies (10-3-10-5). The results of this computational model clearly show how changes in plasmid kinetics can modify the entire population ecology of antibiotic resistance in the hospital setting.

Campos Marcelino, San Millán Álvaro, Sempere José M, Lanza Val F, Coque Teresa M, Llorens Carlos, Baquero Fernando

2020-May-26

General General

Pilot study: can machine learning analyses of movement discriminate between leg movements in sleep (LMS) with vs. without cortical arousals?

In Sleep & breathing = Schlaf & Atmung

PURPOSE : Clinical and animal studies indicate frequent small micro-arousals (McA) fragment sleep leading to health complications. McA in humans is defined by changes in EEG and EMG during sleep. Complex EEG recordings during the night are usually required to detect McA-limiting large-scale, prospective studies on McA and their impact on health. Even with the use of EEG, reliably measuring McA can be difficult because of low inter-scorer reliability. Surrogate measures in place of EEG could provide easier and possibly more reliable measures of McA. These have usually involved measuring heart rate and arm movements. They have not provided a reliable measurement of McA in part because they cannot adequately detect short wake periods and periods of wake after sleep onset. Leg movements in sleep (LMS) offer an attractive alternative. LMS and cortical arousal, including McA, commonly occur together. Not all McA occur with LMS, but the most clinically significant ones may be those with LMS [1]. Conversely, most LMS do not occur with McA, but LMS vary considerably in their characteristics. Evaluating LMS characteristics may serve to identify the LMS associated with McA. The use of standard machine learning approaches seems appropriate for this particular task. This proof-of-concept pilot project aims to determine the feasibility of detecting McA from machine learning methods analyzing movement characteristics of the LMS.

METHODS : This study uses a small but diverse group of subjects to provide a large variety of LMS and McA adequate for supervised machine learning. LMS measurements were obtained from a new advanced technology in the RestEaZe™ leg band that integrates gyroscope, accelerometer, and capacitance measurements. Eleven RestEaZe™ LMS features were selected for logistic regression analyses.

RESULTS : With the optimum logit probability threshold selected, the system accurately detected 76% of the McA matching the accuracy of trained visual inter-scorer reliability (71-76%). The classifier provided a sensitivity of 76% and a specificity of 86% for the identification of the LMS with McA. The classifier identified regions in sleep with high versus low rates of LMS with McA, indicating possible areas of fragmented versus undisturbed restful sleep.

CONCLUSION : These pilot data are encouraging as a preliminary proof-of-concept for using advanced machine learning analyses of LMS to identify sleep fragmented by McA.

Jha Amitanshu, Banerjee Nilanjan, Feltch Cody, Robucci Ryan, Earley Christopher J, Lam Janet, Allen Richard

2020-May-26

Cortical arousal, Leg movements in sleep, Machine learning, Movement analyses, PLMS, RestEaZe™ analyses

General General

A review of epileptic seizure detection using machine learning classifiers.

In Brain informatics

Epilepsy is a serious chronic neurological disorder, can be detected by analyzing the brain signals produced by brain neurons. Neurons are connected to each other in a complex way to communicate with human organs and generate signals. The monitoring of these brain signals is commonly done using Electroencephalogram (EEG) and Electrocorticography (ECoG) media. These signals are complex, noisy, non-linear, non-stationary and produce a high volume of data. Hence, the detection of seizures and discovery of the brain-related knowledge is a challenging task. Machine learning classifiers are able to classify EEG data and detect seizures along with revealing relevant sensible patterns without compromising performance. As such, various researchers have developed number of approaches to seizure detection using machine learning classifiers and statistical features. The main challenges are selecting appropriate classifiers and features. The aim of this paper is to present an overview of the wide varieties of these techniques over the last few years based on the taxonomy of statistical features and machine learning classifiers-'black-box' and 'non-black-box'. The presented state-of-the-art methods and ideas will give a detailed understanding about seizure detection and classification, and research directions in the future.

Siddiqui Mohammad Khubeb, Morales-Menendez Ruben, Huang Xiaodi, Hussain Nasir

2020-May-25

Applications of machine learning on epilepsy, Black-box and non-black-box classifiers, EEG signals, Epilepsy, Seizure detection, Seizure localization, Statistical features

General General

Genomic analysis of the natural history of attention-deficit/hyperactivity disorder using Neanderthal and ancient Homo sapiens samples.

In Scientific reports ; h5-index 158.0

Attention-deficit/hyperactivity disorder (ADHD) is an impairing neurodevelopmental condition highly prevalent in current populations. Several hypotheses have been proposed to explain this paradox, mainly in the context of the Paleolithic versus Neolithic cultural shift but especially within the framework of the mismatch theory. This theory elaborates on how a particular trait once favoured in an ancient environment might become maladaptive upon environmental changes. However, given the lack of genomic data available for ADHD, these theories have not been empirically tested. We took advantage of the largest GWAS meta-analysis available for this disorder consisting of over 20,000 individuals diagnosed with ADHD and 35,000 controls, to assess the evolution of ADHD-associated alleles in European populations using archaic, ancient and modern human samples. We also included Approximate Bayesian computation coupled with deep learning analyses and singleton density scores to detect human adaptation. Our analyses indicate that ADHD-associated alleles are enriched in loss of function intolerant genes, supporting the role of selective pressures in this early-onset phenotype. Furthermore, we observed that the frequency of variants associated with ADHD has steadily decreased since Paleolithic times, particularly in Paleolithic European populations compared to samples from the Neolithic Fertile Crescent. We demonstrate this trend cannot be explained by African admixture nor Neanderthal introgression, since introgressed Neanderthal alleles are enriched in ADHD risk variants. All analyses performed support the presence of long-standing selective pressures acting against ADHD-associated alleles until recent times. Overall, our results are compatible with the mismatch theory for ADHD but suggest a much older time frame for the evolution of ADHD-associated alleles compared to previous hypotheses.

Esteller-Cucala Paula, Maceda Iago, Børglum Anders D, Demontis Ditte, Faraone Stephen V, Cormand Bru, Lao Oscar

2020-May-25

General General

PTB-XL, a large publicly available electrocardiography dataset.

In Scientific data

Electrocardiography (ECG) is a key non-invasive diagnostic tool for cardiovascular diseases which is increasingly supported by algorithms based on machine learning. Major obstacles for the development of automatic ECG interpretation algorithms are both the lack of public datasets and well-defined benchmarking procedures to allow comparison s of different algorithms. To address these issues, we put forward PTB-XL, the to-date largest freely accessible clinical 12-lead ECG-waveform dataset comprising 21837 records from 18885 patients of 10 seconds length. The ECG-waveform data was annotated by up to two cardiologists as a multi-label dataset, where diagnostic labels were further aggregated into super and subclasses. The dataset covers a broad range of diagnostic classes including, in particular, a large fraction of healthy records. The combination with additional metadata on demographics, additional diagnostic statements, diagnosis likelihoods, manually annotated signal properties as well as suggested folds for splitting training and test sets turns the dataset into a rich resource for the development and the evaluation of automatic ECG interpretation algorithms.

Wagner Patrick, Strodthoff Nils, Bousseljot Ralf-Dieter, Kreiseler Dieter, Lunze Fatima I, Samek Wojciech, Schaeffter Tobias

2020-May-25

General General

Work effort, readability and quality of pharmacy transcription of patient directions from electronic prescriptions: a retrospective observational cohort analysis.

In BMJ quality & safety

BACKGROUND : Free-text directions generated by prescribers in electronic prescriptions can be difficult for patients to understand due to their variability, complexity and ambiguity. Pharmacy staff are responsible for transcribing these directions so that patients can take their medication as prescribed. However, little is known about the quality of these transcribed directions received by patients.

METHODS : A retrospective observational analysis of 529 990 e-prescription directions processed at a mail-order pharmacy in the USA. We measured pharmacy staff editing of directions using string edit distance and execution time using the Keystroke-Level Model. Using the New Dale-Chall (NDC) readability formula, we calculated NDC cloze scores of the patient directions before and after transcription. We also evaluated the quality of directions (eg, included a dose, dose unit, frequency of administration) before and after transcription with a random sample of 966 patient directions.

RESULTS : Pharmacy staff edited 83.8% of all e-prescription directions received with a median edit distance of 18 per e-prescription. We estimated a median of 6.64 s of transcribing each e-prescription. The median NDC score increased by 68.6% after transcription (26.12 vs 44.03, p<0.001), which indicated a significant readability improvement. In our sample, 51.4% of patient directions on e-prescriptions contained at least one pre-defined direction quality issue. Pharmacy staff corrected 79.5% of the quality issues.

CONCLUSION : Pharmacy staff put significant effort into transcribing e-prescription directions. Manual transcription removed the majority of quality issues; however, pharmacy staff still miss or introduce following their manual transcription processes. The development of tools and techniques such as a comprehensive set of structured direction components or machine learning-based natural language processing techniques may help produce clear directions.

Zheng Yifan, Jiang Yun, Dorsch Michael P, Ding Yuting, Vydiswaran V G Vinod, Lester Corey A

2020-May-25

human error, human factors, information technology, medication safety, pharmacists

General General

How can endoscopists adapt and collaborate with artificial intelligence for early gastric cancer detection?

In Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society

Early detection is essential to improve the prognosis and mortality of gastric cancer, particularly in countries with high incidence of gastric cancer such as Japan and Korea. Endoscopy has been recently accepted as a primary tool in population-based gastric cancer screening 1 . Early detection also allows for minimally invasive endoscopic resection which has been shown to have excellent overall survival comparable to gastrectomy, while preserving stomach function.

Abe Seiichiro, Oda Ichiro

2020-May-26

General General

Development and validation of explainable AI-based decision-supporting tool for prostate biopsy.

In BJU international ; h5-index 62.0

OBJECTIVES : To develop and validate a risk calculator for prostate cancer (PC) and clinically significant PC (csPC) using explainable artificial intelligence (XAI).

MATERIALS AND METHODS : We used data of 3791 patients to develop and validate the risk calculator. We initially divided the data into development and validation sets. An extreme gradient-boosting algorithm was applied to the development calculator using five-fold cross-validation with hyperparameter tuning following feature selection in the development set. The model feature importance was determined based on the Shapley value. The area under the curve (AUC) of the receiver operating characteristic curve was analysed for each validation set of the calculator.

RESULTS : Approximately 1216 (32.7%) and 562 (14.8%) patients were diagnosed with PC and csPC. The data of 2843 patients were used for development, whereas the data of 948 patients were used as a test set. We selected the variables for each PC and csPC risk calculation according to the least absolute shrinkage and selection operator regression. The AUC of the final PC model was 0.869 (95% confidence interval (CI); 0.844 to 0.893), whereas that of the csPC model was 0.945 (95% CI; 0.927 to 0.963). The prostate-specific antigen (PSA), free PSA, age, prostate volume (both the transitional zone and total), hypoechoic lesions on ultrasound, and testosterone level were found to be important parameters in the PC model. The number of previous biopsies was not associated with the risk of csPC, but was negatively associated with the risk of PC.

CONCLUSION : We successfully developed and validated a decision-supporting tool using XAI for calculating the probability of PC and csPC prior to prostate biopsy.

Suh Jungyo, Yoo Sangjun, Park Juhyun, Cho Sung Yong, Cho Min Chul, Son Hwancheol, Jeong Hyeon

2020-May-26

Decision-supporting tool, Explainable AI, Machine learning, Prediction model, Prostate cancer, Web-based model

General General

Targeting Precision with Data Augmented Samples in Deep Learning.

In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

In the last five years, deep learning (DL) has become the state-of-the-art tool for solving various tasks in medical image analysis. Among the different methods that have been proposed to improve the performance of Convolutional Neural Networks (CNNs), one typical approach is the augmentation of the training data set through various transformations of the input image. Data augmentation is typically used in cases where a small amount of data is available, such as the majority of medical imaging problems, to present a more substantial amount of data to the network and improve the overall accuracy. However, the ability of the network to improve the accuracy of the results when a slightly modified version of the same input is presented is often overestimated. This overestimation is the result of the strong correlation between data samples when they are considered independently in the training phase. In this paper, we emphasize the importance of optimizing for accuracy as well as precision among multiple replicates of the same training data in the context of data augmentation. To this end, we propose a new approach that leverages the augmented data to help the network focus on the precision through a specifically-designed loss function, with the ultimate goal to improve both the overall performance and the network's precision at the same time. We present two different applications of DL (regression and segmentation) to demonstrate the strength of the proposed strategy. We think that this work will pave the way to a explicit use of data augmentation within the loss function that helps the network to be invariant to small variations of the same input samples, a characteristic that is always required to every application in the medical imaging field.

Nardelli Pietro, Estépar Raúl San José

2019-Oct

Accuracy, Data augmentation, Deep learning, Precision

General General

Conceptual Organization is Revealed by Consumer Activity Patterns.

In Computational brain & behavior

Computational models using text corpora have proved useful in understanding the nature of language and human concepts. One appeal of this work is that text, such as from newspaper articles, should reflect human behaviour and conceptual organization outside the laboratory. However, texts do not directly reflect human activity, but instead serve a communicative function and are highly curated or edited to suit an audience. Here, we apply methods devised for text to a data source that directly reflects thousands of individuals' activity patterns. Using product co-occurrence data from nearly 1.3-m supermarket shopping baskets, we trained a topic model to learn 25 high-level concepts (or topics). These topics were found to be comprehensible and coherent by both retail experts and consumers. The topics indicated that human concepts are primarily organized around goals and interactions (e.g. tomatoes go well with vegetables in a salad), rather than their intrinsic features (e.g. defining a tomato by the fact that it has seeds and is fleshy). These results are consistent with the notion that human conceptual knowledge is tailored to support action. Individual differences in the topics sampled predicted basic demographic characteristics. Our findings suggest that human activity patterns can reveal conceptual organization and may give rise to it.

Hornsby Adam N, Evans Thomas, Riefer Peter S, Prior Rosie, Love Bradley C

2020

Big data, Cognition, Computational social science, Decision making, Machine learning

Radiology Radiology

Abdominal musculature segmentation and surface prediction from CT using deep learning for sarcopenia assessment.

In Diagnostic and interventional imaging

PURPOSE : The purpose of this study was to build and train a deep convolutional neural networks (CNN) algorithm to segment muscular body mass (MBM) to predict muscular surface from a two-dimensional axial computed tomography (CT) slice through L3 vertebra.

MATERIALS AND METHODS : An ensemble of 15 deep learning models with a two-dimensional U-net architecture with a 4-level depth and 18 initial filters were trained to segment MBM. The muscular surface values were computed from the predicted masks and corrected with the algorithm's estimated bias. Resulting mask prediction and surface prediction were assessed using Dice similarity coefficient (DSC) and root mean squared error (RMSE) scores respectively using ground truth masks as standards of reference.

RESULTS : A total of 1025 individual CT slices were used for training and validation and 500 additional axial CT slices were used for testing. The obtained mean DSC and RMSE on the test set were 0.97 and 3.7 cm2 respectively.

CONCLUSION : Deep learning methods using convolutional neural networks algorithm enable a robust and automated extraction of CT derived MBM for sarcopenia assessment, which could be implemented in a clinical workflow.

Blanc-Durand P, Schiratti J-B, Schutte K, Jehanno P, Herent P, Pigneur F, Lucidarme O, Benaceur Y, Sadate A, Luciani A, Ernst O, Rouchaud A, Creuze M, Dallongeville A, Banaste N, Cadi M, Bousaid I, Lassau N, Jegou S

2020-May-22

Convolutional neural networks (CNN), Deep learning, Muscular body bass, Sarcopenia, Tomography, X-ray computed

General General

Machine Learning-Based Predictive Farmland Optimization and Crop Monitoring System.

In Scientifica

E-agriculture is the integration of technology and digital mechanisms into agricultural processes for more efficient output. This study provided a machine learning-aided mobile system for farmland optimization, using various inputs such as location, crop type, soil type, soil pH, and spacing. Random forest algorithm and BigML were employed to analyze and classify datasets containing crop features that generated subclasses based on random crop feature parameters. The subclasses were further grouped into three main classes to match the crops using data from the companion crops. The study concluded that the approach aided decision making and also assisted in the design of a mobile application using Appery.io. This Appery.io then took in some user input parameters, thereby offering various optimization sets. It was also deduced that the system led to users' optimization of information when implemented on their farmlands.

Adebiyi Marion Olubunmi, Ogundokun Roseline Oluwaseun, Abokhai Aneoghena Amarachi

2020

Radiology Radiology

LOCALIZING IMAGE-BASED BIOMARKER REGRESSION WITHOUT TRAINING MASKS: A NEW APPROACH TO BIOMARKER DISCOVERY.

In Proceedings. IEEE International Symposium on Biomedical Imaging

Biomarker inference from biomedical images is one of the main tasks of medical image analysis. Standard techniques follow a segmentation-and-measure strategy, where the structure is first segmented and then the measurement is performed. Recent work has shown that such strategy could be replaced by a direct regression of the biomarker value in using regression networks. While achieving high correlation coefficients, such techniques operate as a 'black-box', not offering quality-control images. We present a methodology to regress the biomarker from the image while simultaneously computing the quality control image. Our proposed methodology does not require segmentation masks for training, but infers the segmentations directly from the pixels that used to compute the biomarker value. The network proposed consists of two steps: a segmentation method to an unknown reference and a summation method for the biomarker estimation. The network is optimized using a dual loss function, L2 for the biomarkers and an L1 to enforce sparsity. We showcase our methodology in the problem of pectoralis muscle area (PMA) and subcutaneous fat area (SFA) inference in a single slice from chest-CT images. We use a database of 7000 cases to which only the value of the biomarker is known for training and a test set of 3000 cases with both, biomarkers and segmentations. We achieve a correlation coefficient of 0.97 for PMA and 0.98 for SFA with respect to the reference standard. The average DICE coefficient is of 0.88 (PMA) and 0.89 (SFA). Comparing with standard segment-and-measure techniques, we achieve the same correlation for the biomarkers but smaller DICE coefficients in segmentation. Such is of little surprise, since segmentation networks are the upper limit of performance achievable, and we are not using segmentation masks for training. We can conclude that it is possible to infer segmentation masks from biomarker regression networks.

Cano-Espinosa Carlos, González Germán, Washko George R, Cazorla Miguel, Estépar Raúl San José

2019-Apr

Segmentation, biomarker regression, deep learning

General General

Child-Robot Relationship Formation: A Narrative Review of Empirical Research.

In International journal of social robotics

This narrative review aimed to elucidate which robot-related characteristics predict relationship formation between typically-developing children and social robots in terms of closeness and trust. Moreover, we wanted to know to what extent relationship formation can be explained by children's experiential and cognitive states during interaction with a robot. We reviewed 86 journal articles and conference proceedings published between 2000 and 2017. In terms of predictors, robots' responsiveness and role, as well as strategic and emotional interaction between robot and child, increased closeness between the child and the robot. Findings about whether robot features predict children's trust in robots were inconsistent. In terms of children's experiential and cognitive states during interaction with a robot, robot characteristics and interaction styles were associated with two experiential states: engagement and enjoyment/liking. The literature hardly addressed the impact of experiential and cognitive states on closeness and trust. Comparisons of children's interactions with robots, adults, and objects showed that robots are perceived as neither animate nor inanimate, and that they are entities with whom children will likely form social relationships. Younger children experienced more enjoyment, were less sensitive to a robot's interaction style, and were more prone to anthropomorphic tendencies and effects than older children. Tailoring a robot's sex to that of a child mainly appealed to boys.

van Straten Caroline L, Peter Jochen, Kühne Rinaldo

2020

Artificial intelligence, Automation, Child–robot interaction, Human–robot interaction, New-ontological-category hypothesis

Radiology Radiology

A Novel Radial Basis Neural Network-Leveraged Fast Training Method for Identifying Organs in MR Images.

In Computational and mathematical methods in medicine

We propose a new method for fast organ classification and segmentation of abdominal magnetic resonance (MR) images. Magnetic resonance imaging (MRI) is a new type of high-tech imaging examination fashion in recent years. Recognition of specific target areas (organs) based on MR images is one of the key issues in computer-aided diagnosis of medical images. Artificial neural network technology has made significant progress in image processing based on the multimodal MR attributes of each pixel in MR images. However, with the generation of large-scale data, there are few studies on the rapid processing of large-scale MRI data. To address this deficiency, we present a fast radial basis function artificial neural network (Fast-RBF) algorithm. The importance of our efforts is as follows: (1) The proposed algorithm achieves fast processing of large-scale image data by introducing the ε-insensitive loss function, the structural risk term, and the core-set principle. We apply this algorithm to the identification of specific target areas in MR images. (2) For each abdominal MRI case, we use four MR sequences (fat, water, in-phase (IP), and opposed-phase (OP)) and the position coordinates (x, y) of each pixel as the input of the algorithm. We use three classifiers to identify the liver and kidneys in the MR images. Experiments show that the proposed method achieves a higher precision in the recognition of specific regions of medical images and has better adaptability in the case of large-scale datasets than the traditional RBF algorithm.

Xu Min, Qian Pengjiang, Zheng Jiamin, Ge Hongwei, Muzic Raymond F

2020

General General

Fast Enhanced Exemplar-Based Clustering for Incomplete EEG Signals.

In Computational and mathematical methods in medicine

The diagnosis and treatment of epilepsy is a significant direction for both machine learning and brain science. This paper newly proposes a fast enhanced exemplar-based clustering (FEEC) method for incomplete EEG signal. The algorithm first compresses the potential exemplar list and reduces the pairwise similarity matrix. By processing the most complete data in the first stage, FEEC then extends the few incomplete data into the exemplar list. A new compressed similarity matrix will be constructed and the scale of this matrix is greatly reduced. Finally, FEEC optimizes the new target function by the enhanced α-expansion move method. On the other hand, due to the pairwise relationship, FEEC also improves the generalization of this algorithm. In contrast to other exemplar-based models, the performance of the proposed clustering algorithm is comprehensively verified by the experiments on two datasets.

Bi Anqi, Ying Wenhao, Zhao Lu

2020

Radiology Radiology

Multicenter Computer-Aided Diagnosis for Lymph Nodes Using Unsupervised Domain-Adaptation Networks Based on Cross-Domain Confounding Representations.

In Computational and mathematical methods in medicine

To achieve the robust high-performance computer-aided diagnosis systems for lymph nodes, CT images may be typically collected from multicenter data, which cause the isolated performance of the model based on different data source centers. The variability adaptation problem of lymph node data which is related to the problem of domain adaptation in deep learning differs from the general domain adaptation problem because of the typically larger CT image size and more complex data distributions. Therefore, domain adaptation for this problem needs to consider the shared feature representation and even the conditioning information of each domain so that the adaptation network can capture significant discriminative representations in a domain-invariant space. This paper extracts domain-invariant features based on a cross-domain confounding representation and proposes a cycle-consistency learning framework to encourage the network to preserve class-conditioning information through cross-domain image translations. Compared with the performance of different domain adaptation methods, the accurate rate of our method achieves at least 4.4% points higher under multicenter lymph node data. The pixel-level cross-domain image mapping and the semantic-level cycle consistency provided a stable confounding representation with class-conditioning information to achieve effective domain adaptation under complex feature distribution.

Qin RuoXi, Zhang Huike, Jiang LingYun, Qiao Kai, Hai Jinjin, Chen Jian, Xu Junling, Shi Dapeng, Yan Bin

2020

oncology Oncology

Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI.

In Computational and mathematical methods in medicine

Breast segmentation and mass detection in medical images are important for diagnosis and treatment follow-up. Automation of these challenging tasks can assist radiologists by reducing the high manual workload of breast cancer analysis. In this paper, deep convolutional neural networks (DCNN) were employed for breast segmentation and mass detection in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). First, the region of the breasts was segmented from the remaining body parts by building a fully convolutional neural network based on U-Net++. Using the method of deep learning to extract the target area can help to reduce the interference external to the breast. Second, a faster region with convolutional neural network (Faster RCNN) was used for mass detection on segmented breast images. The dataset of DCE-MRI used in this study was obtained from 75 patients, and a 5-fold cross validation method was adopted. The statistical analysis of breast region segmentation was carried out by computing the Dice similarity coefficient (DSC), Jaccard coefficient, and segmentation sensitivity. For validation of breast mass detection, the sensitivity with the number of false positives per case was computed and analyzed. The Dice and Jaccard coefficients and the segmentation sensitivity value for breast region segmentation were 0.951, 0.908, and 0.948, respectively, which were better than those of the original U-Net algorithm, and the average sensitivity for mass detection achieved 0.874 with 3.4 false positives per case.

Jiao Han, Jiang Xinhua, Pang Zhiyong, Lin Xiaofeng, Huang Yihua, Li Li

2020

General General

Prediction of Drug Side Effects with a Refined Negative Sample Selection Strategy.

In Computational and mathematical methods in medicine

Drugs are an important way to treat various diseases. However, they inevitably produce side effects, bringing great risks to human bodies and pharmaceutical companies. How to predict the side effects of drugs has become one of the essential problems in drug research. Designing efficient computational methods is an alternative way. Some studies paired the drug and side effect as a sample, thereby modeling the problem as a binary classification problem. However, the selection of negative samples is a key problem in this case. In this study, a novel negative sample selection strategy was designed for accessing high-quality negative samples. Such strategy applied the random walk with restart (RWR) algorithm on a chemical-chemical interaction network to select pairs of drugs and side effects, such that drugs were less likely to have corresponding side effects, as negative samples. Through several tests with a fixed feature extraction scheme and different machine-learning algorithms, models with selected negative samples produced high performance. The best model even yielded nearly perfect performance. These models had much higher performance than those without such strategy or with another selection strategy. Furthermore, it is not necessary to consider the balance of positive and negative samples under such a strategy.

Liang Haiyan, Chen Lei, Zhao Xian, Zhang Xiaolin

2020

General General

Synapse cell optimization and back-propagation algorithm implementation in a domain wall synapse based crossbar Neural Network for scalable on-chip learning.

In Nanotechnology

On-chip learning in spin orbit torque driven domain wall synapse based crossbar Fully Connected Neural Network (FCNN) has been shown to be extremely efficient in terms of speed and energy, when compared to training on a conventional computing unit or even on a crossbar FCNN based on other Non Volatile Memory devices. However there are issues with respect to scalability of the on-chip learning scheme in the domain wall synapse based FCNN. Unless the scheme is scalable, it won't be competitive with respect to training a neural network on a conventional computing unit for real applications. In this paper, we have proposed a modification in the standard gradient descent algorithm, used for training such FCNN, by including appropriate thresholding units. This leads to optimization of the synapse cell at each intersection of the crossbars and makes the system scalable. In order for the system to approximate a wide range of functions for data classification, hidden layers must be present and the backpropagation algorithm (extension of gradient descent algorithm for multi-layered FCNN) for training must be implemented on hardware. We have carried this out in this paper by employing an extra crossbar. Through a combination of micromagnetic simulations and SPICE circuit simulations, we hence show highly improved accuracy for domain wall syanpse based FCNN with a hidden layer compared to that without a hidden layer for different machine learning datasets.

Kaushik Divya, Sharda Janak, Bhowmik Debanjan

2020-May-26

domain wall synapse, neuromorphic computing, non Boolean computing, non Von Neumann computing, spintronics

General General

Neonatal EEG sleep stage classification based on deep learning and HMM.

In Journal of neural engineering ; h5-index 52.0

Automatic sleep stage scoring is of great importance for investigating sleep architecture during infancy. In this work, we introduce a novel multichannel approach based on deep learning networks and hidden Markov models (HMM) to improve the accuracy of sleep stage classification in term neonates. The classification performance was evaluated on quiet sleep (QS) and active sleep (AS) stages, each with two sub-states, using multichannel EEG data recorded from sixteen neonates with postmenstrual age of 38-40 weeks. A comprehensive set of linear and nonlinear features were extracted from thirty-second EEG segments. The feature space dimensionality was then reduced by using an evolutionary feature selection method called MGCACO (Modified Graph Clustering Ant Colony Optimization) based on the relevance and redundancy analysis. A bi-directional long-short time memory (BiLSTM) network was trained for sleep stage classification. The number of channels was optimized using the sequential forward selection method to reduce the spatial space. Finally, an HMM-based postprocessing stage was used to reduce false positives by incorporating the knowledge of transition probabilities between stages into the classification process. The method performance was evaluated using the K-fold (KFCV) and leave-one-out cross-validation (LOOCV) strategies. Using six-bipolar channels, our method achieved a mean kappa and an overall accuracy of 0.71-0.76 and 78.9%-82.4% using the KFCV and LOOCV strategies, respectively. The presented automatic sleep stage scoring method can be used to study the neurodevelopmental process and to diagnose brain abnormalities in term neonates.

Ghimatgar Hojat, Kazemi Kamran, Helfroush Mohammad Sadegh, Pillay Kirubin, Dereymaeker Anneleen, Jansen Katrien, De Vos Maarten, Aarabi Ardalan

2020-May-26

EEG, HMM, deep learning, feature selection, neonates, sleep stage classification

General General

How, When, and Why: High-Density Mapping of Atrial Fibrillation.

In Cardiac electrophysiology clinics

High-density (HD) mapping presents opportunities to enhance delineation of atrial fibrillation (AF) substrate, improve efficiency of the mapping procedure without sacrificing safety, and afford new mechanistic insights regarding AF. Innovations in hardware, software algorithms, and development of novel multielectrode catheters have allowed HD mapping to be feasible and reliable. Patients to particularly benefit from this technology are those with paroxysmal AF in setting of preexisting atrial scar, persistent AF, and AF in the setting of complex congenital heart disease. The future will bring refinements in automated HD mapping including evolution of noncontact methodologies and artificial intelligence to supplant current techniques.

Kodali Santhisri, Santangeli Pasquale

2020-Jun

Atrial fibrillation, Catheter ablation, High-density mapping

Public Health Public Health

Using Reports of Own and Others' Symptoms and Diagnosis on Social Media to Predict COVID-19 Case Counts: Observational Infoveillance Study in Mainland China.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : COVID-19 has affected more than 200 countries and territories worldwide. It poses an extraordinary challenge for public health systems, because screening and surveillance capacity-especially during the beginning of the outbreak-is often severely limited, fueling the outbreak as many patients unknowingly infect others.

OBJECTIVE : We present an effort to collect and analyze COVID-19 related posts on the popular Twitter-like social media site in China, Weibo. To our knowledge, this infoveillance study employs the largest, most comprehensive and fine-grained social media data to date to predict COVID-19 case counts in mainland China.

METHODS : We built a Weibo user pool of 250 million, approximately half of the entire monthly active Weibo user population. Using a comprehensive list of 167 keywords, we retrieved and analyzed around 15 million COVID-19 related posts from our user pool, from November 1, 2019 to March 31, 2020. We developed a machine learning classifier to identify "sick posts," which are reports of one's own and other people's symptoms and diagnosis related to COVID-19. Using officially reported case counts as the outcome, we then estimated the Granger causality of sick posts and other COVID-19 posts on daily case counts. For a subset of geotagged posts (3.10% of all retrieved posts), we also ran separate predictive models for Hubei province, the epicenter of the initial outbreak, and the rest of mainland China.

RESULTS : We found that reports of symptoms and diagnosis of COVID-19 significantly predicted daily case counts, up to 14 days ahead of official statistics, whereas other COVID-19 posts did not have similar predictive power. For the subset of geotagged posts, we found that the predictive pattern held true for both Hubei province and the rest of mainland China, regardless of unequal distribution of healthcare resources and outbreak timeline.

CONCLUSIONS : Public social media data can be usefully harnessed to predict infection cases and inform timely responses. Researchers and disease control agencies should pay close attention to the social media infosphere regarding COVID-19. On top of monitoring overall search and posting activities, leveraging machine learning approaches and theoretical understandings of information sharing behaviors is a promising approach to identifying true disease signals and improving the effectiveness of infoveillance.

CLINICALTRIAL :

Shen Cuihua, Chen Anfan, Luo Chen, Zhang Jingwen, Feng Bo, Liao Wang

2020-May-25

Pathology Pathology

Co-Heterogeneous and Adaptive Segmentation from Multi-Source and Multi-Phase CT Imaging Data: A Study on Pathological Liver and Lesion Segmentation

ArXiv Preprint

In medical imaging, organ/pathology segmentation models trained on current publicly available and fully-annotated datasets usually do not well-represent the heterogeneous modalities, phases, pathologies, and clinical scenarios encountered in real environments. On the other hand, there are tremendous amounts of unlabelled patient imaging scans stored by many modern clinical centers. In this work, we present a novel segmentation strategy, co-heterogenous and adaptive segmentation (CHASe), which only requires a small labeled cohort of single phase imaging data to adapt to any unlabeled cohort of heterogenous multi-phase data with possibly new clinical scenarios and pathologies. To do this, we propose a versatile framework that fuses appearance based semi-supervision, mask based adversarial domain adaptation, and pseudo-labeling. We also introduce co-heterogeneous training, which is a novel integration of co-training and hetero modality learning. We have evaluated CHASe using a clinically comprehensive and challenging dataset of multi-phase computed tomography (CT) imaging studies (1147 patients and 4577 3D volumes). Compared to previous state-of-the-art baselines, CHASe can further improve pathological liver mask Dice-Sorensen coefficients by ranges of $4.2\% \sim 9.4\%$, depending on the phase combinations: e.g., from $84.6\%$ to $94.0\%$ on non-contrast CTs.

Ashwin Raju, Chi-Tung Cheng, Yunakai Huo, Jinzheng Cai, Junzhou Huang, Jing Xiao, Le Lu, ChienHuang Liao, Adam P Harrison

2020-05-27

General General

Generalized norm for existence, uniqueness and stability of Hopfield neural networks with discrete and distributed delays.

In Neural networks : the official journal of the International Neural Network Society

In this paper, the existence, uniqueness and stability criteria of solutions for Hopfield neural networks with discrete and distributed delays (DDD HNNs) are investigated by the definitions of three kinds of generalized norm (ξ-norm). A general DDD HNN model is firstly introduced, where the discrete delays τpq(t) are asynchronous time-varying delays. Then, {ξ,1}-norm, {ξ,2}-norm and {ξ,∞}-norm are successively used to derive the existence, uniqueness and stability criteria of solutions for the DDD HNNs. In the proof of theorems, special functions and assumptions are given to deal with discrete and distributed delays. Furthermore, a corollary is concluded for the existence and stability criteria of solutions. The methods given in this paper can also be used to study the synchronization and μ-stability of different DDD NNs. Finally, two numerical examples and their simulation figures are given to illustrate the effectiveness of these results.

Wang Huamin, Wei Guoliang, Wen Shiping, Huang Tingwen

2020-May-20

-norm, Discrete-distributed delays, Exponential stability, Hopfield neural networks

General General

Uni-image: Universal image construction for robust neural model.

In Neural networks : the official journal of the International Neural Network Society

Deep neural networks have shown high performance in prediction, but they are defenseless when they predict on adversarial examples which are generated by adversarial attack techniques. In image classification, those attack techniques usually perturb the pixel of an image to fool the deep neural networks. To improve the robustness of the neural networks, many researchers have introduced several defense techniques against those attack techniques. To the best of our knowledge, adversarial training is one of the most effective defense techniques against the adversarial examples. However, the defense technique could fail against a semantic adversarial image that performs arbitrary perturbation to fool the neural networks, where the modified image semantically represents the same object as the original image. Against this background, we propose a novel defense technique, Uni-Image Procedure (UIP) method. UIP generates a universal-image (uni-image) from a given image, which can be a clean image or a perturbed image by some attacks. The generated uni-image preserves its own characteristics (i.e. color) regardless of the transformations of the original image. Note that those transformations include inverting the pixel value of an image, modifying the saturation, hue, and value of an image, etc. Our experimental results using several benchmark datasets show that our method not only defends well known adversarial attacks and semantic adversarial attack but also boosts the robustness of the neural network.

Ho Jiacang, Lee Byung-Gook, Kang Dae-Ki

2020-May-21

Adversarial machine learning, Defense technique, Image classification, Semantic adversarial example, Uni-Image Procedure

General General

Active source localization in wave guides based on machine learning.

In Ultrasonics

In the present work, an active source localization strategy is proposed. The presence of active sources in a waveguide can have several reasons, such as crack initiation or internal friction. In this study, the active source is represented by an impact event. A steel ball is dropped on an aluminum plate at different positions. Elastic waves are excited and will propagate through the plate. The wave response is acquired by a piezoelectric sensor network, which is attached to the plate. After performing numerical and physical experiments, enough data are collected in order to train an artificial neural network and a support vector machine. Those machine learning algorithms will predict the impact position based on the wave response of each sensor, while only numerical data from the finite element simulations are used to train both methods. After the training process is completed, the algorithms are applied to experimental data. A good agreement between reference and predicted results proves that the wave responses at the piezoelectric transducers contain sufficient information in order to localize the impact position precisely.

Hesser Daniel Frank, Kocur Georg Karl, Markert Bernd

2020-Apr-18

Artificial neural network, Computational intelligence, Guided elastic waves, Impact dynamics, Structural health monitoring

General General

CHEER: hierarCHical taxonomic classification for viral mEtagEnomic data via deep leaRning.

In Methods (San Diego, Calif.)

The fast accumulation of viral metagenomic data has contributed significantly to new RNA virus discovery. However, the short read size, complex composition, and large data size can all make taxonomic analysis difficult. In particular, commonly used alignment-based methods are not ideal choices for detecting new viral species. In this work, we present a novel hierarchical classification model named CHEER, which can conduct read-level taxonomic classification from order to genus for new species. By combining k-mer embedding-based encoding, hierarchically organized CNNs, and carefully trained rejection layer, CHEER is able to assign correct taxonomic labels for reads from new species. We tested CHEER on both simulated and real sequencing data. The results show that CHEER can achieve higher accuracy than popular alignment-based and alignment-free taxonomic assignment tools. The source code, scripts, and pre-trained parameters for CHEER are available via GitHub:https://github.com/KennthShang/CHEER.

Shang Jiayu, Sun Yanni

2020-May-23

Convolutional Neural Network, Deep learning, RNA virus, Taxonomic classification, Viral metagenomic data

General General

The interplay between multisensory integration and perceptual decision making.

In NeuroImage ; h5-index 117.0

Facing perceptual uncertainty, the brain combines information from different senses to make optimal perceptual decisions and to guide behavior. However, decision making has been investigated mostly in unimodal contexts. Thus, how the brain integrates multisensory information during decision making is still unclear. Two opposing, but not mutually exclusive, scenarios are plausible: either the brain thoroughly combines the signals from different modalities before starting to build a supramodal decision, or unimodal signals are integrated during decision formation. To answer this question, we devised a paradigm mimicking naturalistic situations where human participants were exposed to continuous cacophonous audiovisual inputs containing an unpredictable signal cue in one or two modalities and had to perform a signal detection task or a cue categorization task. First, model-based analyses of behavioral data indicated that multisensory integration takes place alongside perceptual decision making. Next, using supervised machine learning on concurrently recorded EEG, we identified neural signatures of two processing stages: sensory encoding and decision formation. Generalization analyses across experimental conditions and time revealed that multisensory cues were processed faster during both stages. We further established that acceleration of neural dynamics during sensory encoding and decision formation was directly linked to multisensory integration. Our results were consistent across both signal detection and categorization tasks. Taken together, the results revealed a continuous dynamic interplay between multisensory integration and decision making processes (mixed scenario), with integration of multimodal information taking place both during sensory encoding as well as decision formation.

Mercier Manuel R, Cappe Celine

2020-May-23

Drift Diffusion Model, EEG decoding, Multisensory integration, Perceptual decision making, Race model, Supervised machine learning

Surgery Surgery

Predicting Survival after Extracorporeal Membrane Oxygenation using Machine Learning.

In The Annals of thoracic surgery ; h5-index 58.0

BACKGROUND : Venous-arterial extracorporeal membrane oxygenation (VA-ECMO) undoubtedly saves many lives, but is associated with a high degree of patient morbidity, mortality, and resource utilization. We aimed to develop a machine learning algorithm to augment clinical decision making related to VA-ECMO.

METHODS : Patients supported by VA-ECMO at a single institution from May 2011 to October 2018 were retrospectively reviewed. Laboratory values from only the initial 48 hours of VA-ECMO support were used. Data were split into 70% for training, 15% validation and 15% withheld for testing. Feature importance was estimated and dimensionality reduction techniques were utilized. A deep neural network was trained to predict survival to discharge and the final model was assessed using the independent testing cohort. Model performance was compared to that of the SAVE score using a receiver operator characteristic curve.

RESULTS : Of the 282 eligible adult VA-ECMO patients, 117 (41%) survived to discharge. A total of 1.96 million laboratory values were extracted from the electronic medical record, from which 270 different summary variables were derived for each patient. The most important variables in predicting the primary outcome included lactate, age, total bilirubin, and creatinine. For the testing cohort, the final model achieved 82% overall accuracy and a greater area under the curve (AUC) than the SAVE score (0.92 vs 0.65, p=0.01) in predicting survival to discharge.

CONCLUSIONS : This proof of concept study demonstrates the potential for machine learning models to augment clinical decision making for VA-ECMO patients. Further development with multi-institutional data is warranted.

Ayers Brian, Wood Katherine, Gosev Igor, Prasad Sunil

2020-May-23

General General

The anti-ageing effects of a natural peptide discovered by Artificial Intelligence.

In International journal of cosmetic science

OBJECTIVE : As skin ages, impaired extracellular matrix (ECM) protein synthesis and increased action of degradative enzymes manifests as atrophy, wrinkling and laxity. There is mounting evidence for the functional role of exogenous peptides across many areas, including in offsetting the effects of cutaneous ageing. Here, using an artificial intelligence (AI) approach, we identified peptide RTE62G (pep_RTE62G), a naturally occurring, unmodified peptide with ECM stimulatory properties. The AI-predicted anti-ageing properties of pep_RTE62G were then validated through in vitro, ex vivo and proof of concept clinical testing.

METHODS : A deep learning approach was applied to unlock pep_RTE62G from a plant source, Pisum sativum (pea). Cell culture assays of human dermal fibroblasts (HDFs) and keratinocytes (HaCaTs) were subsequently used to evaluate the in vitro effect of pep_RTE62G. Distinct activities such as cell proliferation and ECM protein production properties were determined by ELISA assays. Cell migration was assessed using a wound healing assay, while ECM protein synthesis and gene expression were analysed respectively by immunofluorescence microscopy and PCR. Immunohistochemistry of human skin explants was employed to further investigate the induction of ECM proteins by pep_RTE62G ex vivo. Finally, the clinical effect of pep_RTE626 was evaluated in a proof of concept 28-day pilot study.

RESULTS : In vitro testing confirmed that pep_RTE62G is an effective multi-functional anti-ageing ingredient. In HaCaTs, pep_RTE62G treatment significantly increases both cellular proliferation and migration. Similarly, in HDFs, pep_RTE62G consistently induced the neosynthesis of ECM proteins elastin and collagen; effects that are upheld in human skin explants. Lastly, in our proof of concept clinical study, application of pep_RTE626 over 28 days demonstrated anti-wrinkle and collagen stimulatory potential.

CONCLUSION : pep_RTE62G represents a natural, unmodified peptide with AI predicted and experimentally validated anti-ageing properties. Our results affirm the utility of AI in the discovery of novel, functional topical ingredients.

Kennedy Kathy, Cal Roi, Casey Rory, Lopez Cyril, Adelfio Alessandro, Molloy Brendan, Wall Audrey M, Holton Thérèse A, Khaldi Nora

2020-May-26

Anti-ageing, Artificial Intelligence, Cell culture, Claim substantiation in vivo, ECM protein synthesis, Elisa, Proteomics, in vitro; Genomics

General General

Benchmarking predictions of MHC class I restricted T cell epitopes in a comprehensively studied model system.

In PLoS computational biology

T cell epitope candidates are commonly identified using computational prediction tools in order to enable applications such as vaccine design, cancer neoantigen identification, development of diagnostics and removal of unwanted immune responses against protein therapeutics. Most T cell epitope prediction tools are based on machine learning algorithms trained on MHC binding or naturally processed MHC ligand elution data. The ability of currently available tools to predict T cell epitopes has not been comprehensively evaluated. In this study, we used a recently published dataset that systematically defined T cell epitopes recognized in vaccinia virus (VACV) infected C57BL/6 mice (expressing H-2Db and H-2Kb), considering both peptides predicted to bind MHC or experimentally eluted from infected cells, making this the most comprehensive dataset of T cell epitopes mapped in a complex pathogen. We evaluated the performance of all currently publicly available computational T cell epitope prediction tools to identify these major epitopes from all peptides encoded in the VACV proteome. We found that all methods were able to improve epitope identification above random, with the best performance achieved by neural network-based predictions trained on both MHC binding and MHC ligand elution data (NetMHCPan-4.0 and MHCFlurry). Impressively, these methods were able to capture more than half of the major epitopes in the top N = 277 predictions within the N = 767,788 predictions made for distinct peptides of relevant lengths that can theoretically be encoded in the VACV proteome. These performance metrics provide guidance for immunologists as to which prediction methods to use, and what success rates are possible for epitope predictions when considering a highly controlled system of administered immunizations to inbred mice. In addition, this benchmark was implemented in an open and easy to reproduce format, providing developers with a framework for future comparisons against new tools.

Paul Sinu, Croft Nathan P, Purcell Anthony W, Tscharke David C, Sette Alessandro, Nielsen Morten, Peters Bjoern

2020-May-26

General General

Predicting host taxonomic information from viral genomes: A comparison of feature representations.

In PLoS computational biology

The rise in metagenomics has led to an exponential growth in virus discovery. However, the majority of these new virus sequences have no assigned host. Current machine learning approaches to predicting virus host interactions have a tendency to focus on nucleotide features, ignoring other representations of genomic information. Here we investigate the predictive potential of features generated from four different 'levels' of viral genome representation: nucleotide, amino acid, amino acid properties and protein domains. This more fully exploits the biological information present in the virus genomes. Over a hundred and eighty binary datasets for infecting versus non-infecting viruses at all taxonomic ranks of both eukaryote and prokaryote hosts were compiled. The viral genomes were converted into the four different levels of genome representation and twenty feature sets were generated by extracting k-mer compositions and predicted protein domains. We trained and tested Support Vector Machine, SVM, classifiers to compare the predictive capacity of each of these feature sets for each dataset. Our results show that all levels of genome representation are consistently predictive of host taxonomy and that prediction k-mer composition improves with increasing k-mer length for all k-mer based features. Using a phylogenetically aware holdout method, we demonstrate that the predictive feature sets contain signals reflecting both the evolutionary relationship between the viruses infecting related hosts, and host-mimicry. Our results demonstrate that incorporating a range of complementary features, generated purely from virus genome sequences, leads to improved accuracy for a range of virus host prediction tasks enabling computational assignment of host taxonomic information.

Young Francesca, Rogers Simon, Robertson David L

2020-May-26

Surgery Surgery

Visual Speech Recognition: Improving Speech Perception in Noise through Artificial Intelligence.

In Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery

OBJECTIVES : To compare speech perception (SP) in noise for normal-hearing (NH) individuals and individuals with hearing loss (IWHL) and to demonstrate improvements in SP with use of a visual speech recognition program (VSRP).

STUDY DESIGN : Single-institution prospective study.

SETTING : Tertiary referral center.

SUBJECTS AND METHODS : Eleven NH and 9 IWHL participants in a sound-isolated booth facing a speaker through a window. In non-VSRP conditions, SP was evaluated on 40 Bamford-Kowal-Bench speech-in-noise test (BKB-SIN) sentences presented by the speaker at 50 A-weighted decibels (dBA) with multiperson babble noise presented from 50 to 75 dBA. SP was defined as the percentage of words correctly identified. In VSRP conditions, an infrared camera was used to track 35 points around the speaker's lips during speech in real time. Lip movement data were translated into speech-text via an in-house developed neural network-based VSRP. SP was evaluated similarly in the non-VSRP condition on 42 BKB-SIN sentences, with the addition of the VSRP output presented on a screen to the listener.

RESULTS : In high-noise conditions (70-75 dBA) without VSRP, NH listeners achieved significantly higher speech perception than IWHL listeners (38.7% vs 25.0%, P = .02). NH listeners were significantly more accurate with VSRP than without VSRP (75.5% vs 38.7%, P < .0001), as were IWHL listeners (70.4% vs 25.0% P < .0001). With VSRP, no significant difference in SP was observed between NH and IWHL listeners (75.5% vs 70.4%, P = .15).

CONCLUSIONS : The VSRP significantly increased speech perception in high-noise conditions for NH and IWHL participants and eliminated the difference in SP accuracy between NH and IWHL listeners.

Raghavan Arun M, Lipschitz Noga, Breen Joseph T, Samy Ravi N, Kohlberg Gavriel D

2020-May-26

artificial intelligence, computer vision, hearing loss, lip reading, speech perception, speech-in-noise, visual speech recognition

General General

Reframing Telehealth: Regulation, Licensing, and Reimbursement in Connected Care.

In Obstetrics and gynecology clinics of North America

Complexity in regulation and reimbursement of telehealth across the United States yields inconsistent use and availability of services. Drivers of this variation stem from existing regulatory, licensing, and payment policy that was designed for face-to-face care. Emerging technology for connected care continues to outpace the rules that govern its use. This article explores the drivers of uncertainty around regulation and payment of remote care services, and provides a roadmap for fulfillment of the benefits of connected care.

McCauley Janet L, Swartz Anthony E

2020-Jun

Artificial intelligence, Connected care, Fee for service, Reimbursement, Telehealth, Telemedicine, Value-based care, Virtual care

General General

An up-to-date overview of computational polypharmacology in modern drug discovery.

In Expert opinion on drug discovery ; h5-index 34.0

INTRODUCTION : In recent years, computational polypharmacology has gained significant attention to study the promiscuous nature of drugs. Despite tremendous challenges, community-wide efforts have led to a variety of novel approaches for predicting drug polypharmacology. In particular, some rapid advances using machine learning and artificial intelligence have been reported with great success.

AREAS COVERED : In this article, the authors provide a comprehensive update on the current state-of-the-art polypharmacology approaches and their applications, focusing on those reports published after our 2017 review article. The authors particularly discuss some novel, groundbreaking concepts, and methods that have been developed recently and applied to drug polypharmacology studies.

EXPERT OPINION : Polypharmacology is evolving and novel concepts are being introduced to counter the current challenges in the field. However, major hurdles remain including incompleteness of high-quality experimental data, lack of in vitro and in vivo assays to characterize multi-targeting agents, shortage of robust computational methods, and challenges to identify the best target combinations and design effective multi-targeting agents. Fortunately, numerous national/international efforts including multi-omics and artificial intelligence initiatives as well as most recent collaborations on addressing the COVID-19 pandemic have shown significant promise to propel the field of polypharmacology forward.

Chaudhari Rajan, Fong Long Wolf, Tan Zhi, Huang Beibei, Zhang Shuxing

2020-May-26

Drug Polypharmacology, artificial Intelligence, deep Learning, drug Repurposing, molecular Promiscuity, multi-omics, multi-targeting Design, network Pharmacology, off-targets

General General

Generative Adversarial Networks Applied to Observational Health Data

ArXiv Preprint

Having been collected for its primary purpose in patient care, Observational Health Data (OHD) can further benefit patient well-being by sustaining the development of health informatics. However, the potential for secondary usage of OHD continues to be hampered by the fiercely private nature of patient-related data. Generative Adversarial Networks (GAN) have Generative Adversarial Networks (GAN) have recently emerged as a groundbreaking approach to efficiently learn generative models that produce realistic Synthetic Data (SD). However, the application of GAN to OHD seems to have been lagging in comparison to other fields. We conducted a review of GAN algorithms for OHD in the published literature, and report our findings here.

Georges-Filteau, Jeremy, Cirillo Elisa

2020-05-27

Pathology Pathology

Deep-Learning-Based Characterization of Tumor-Infiltrating Lymphocytes in Breast Cancers From Histopathology Images and Multiomics Data.

In JCO clinical cancer informatics

PURPOSE : Tumor-infiltrating lymphocytes (TILs) and their spatial characterizations on whole-slide images (WSIs) of histopathology sections have become crucial in diagnosis, prognosis, and treatment response prediction for different cancers. However, fully automatic assessment of TILs on WSIs currently remains a great challenge because of the heterogeneity and large size of WSIs. We present an automatic pipeline based on a cascade-training U-net to generate high-resolution TIL maps on WSIs.

METHODS : We present global cell-level TIL maps and 43 quantitative TIL spatial image features for 1,000 WSIs of The Cancer Genome Atlas patients with breast cancer. For more specific analysis, all the patients were divided into three subtypes, namely, estrogen receptor (ER)-positive, ER-negative, and triple-negative groups. The associations between TIL scores and gene expression and somatic mutation were examined separately in three breast cancer subtypes. Both univariate and multivariate survival analyses were performed on 43 TIL image features to examine the prognostic value of TIL spatial patterns in different breast cancer subtypes.

RESULTS : The TIL score was in strong association with immune response pathway and genes (eg, programmed death-1 and CLTA4). Different breast cancer subtypes showed TIL score in association with mutations from different genes suggesting that different genetic alterations may lead to similar phenotypes. Spatial TIL features that represent density and distribution of TIL clusters were important indicators of the patient outcomes.

CONCLUSION : Our pipeline can facilitate computational pathology-based discovery in cancer immunology and research on immunotherapy. Our analysis results are available for the research community to generate new hypotheses and insights on breast cancer immunology and development.

Lu Zixiao, Xu Siwen, Shao Wei, Wu Yi, Zhang Jie, Han Zhi, Feng Qianjin, Huang Kun

2020-May

General General

Machine Learning Algorithm Identifies an Antibiotic Vocabulary for Permeating Gram-Negative Bacteria.

In Journal of chemical information and modeling

Drug discovery faces a crisis. The industry has used up the "obvious" space in which to find novel drugs for biomedical applications, and productivity is declining. One strategy to combat this is rational approaches to expand the search space without relying on chemical intuition, to avoid rediscovery of similar spaces. In this work, we present proof-of-concept of an approach to rationally identify a "chemical vocabulary" related to a specific drug activity of interest without employing known rules. We focus on the pressing concern of multidrug resistance in Pseudomonas aeruginosa by searching for submolecules that promote compound entry into this bacterium. By synergizing theory, computation, and experiment, we validate our approach, explain the molecular mechanism behind identified fragments promoting compound entry, and select candidate compounds from an external library that display good permeation ability.

Mansbach Rachael A, Leus Inga V, Mehla Jitender, Lopez Cesar A, Walker John K, Rybenkov Valentin V, Hengartner Nicolas, Zgurskaya Helen I, Gnanakaran S

2020-May-26

General General

A New Robust Epigenetic Model for Forensic Age Prediction.

In Journal of forensic sciences

Forensic DNA phenotyping refers to an emerging field of forensic sciences aimed at the prediction of externally visible characteristics of unknown sample donors directly from biological materials. The aging process significantly affects most of the above characteristics making the development of a reliable method of age prediction very important. Today, the so-called "epigenetic clocks" represent the most accurate models for age prediction. Since they are technically not achievable in a typical forensic laboratory, forensic DNA technology has triggered efforts toward the simplification of these models. The present study aimed to build an epigenetic clock using a set of methylation markers of five different genes in a sample of the Italian population of different ages covering the whole span of adult life. In a sample of 330 subjects, 42 selected markers were analyzed with a machine learning approach for building a prediction model for age prediction. A ridge linear regression model including eight of the proposed markers was identified as the best performing model across a plethora of candidates. This model was tested on an independent sample of 83 subjects providing a median error of 4.5 years. In the present study, an epigenetic model for age prediction was validated in a sample of the Italian population. However, its applicability to advanced ages still represents the main limitation in forensic caseworks.

Montesanto Alberto, D’Aquila Patrizia, Lagani Vincenzo, Paparazzo Ersilia, Geracitano Silvana, Formentini Laura, Giacconi Robertina, Cardelli Maurizio, Provinciali Mauro, Bellizzi Dina, Passarino Giuseppe

2020-May-26

\nELOVL2\n, FDP, age prediction, automated machine learning, epigenetic clock, externally visible characteristics, methylation

Surgery Surgery

Is Deep Learning On Par with Human Observers for Detection of Radiographically Visible and Occult Fractures of the Scaphoid?

In Clinical orthopaedics and related research ; h5-index 71.0

BACKGROUND : Preliminary experience suggests that deep learning algorithms are nearly as good as humans in detecting common, displaced, and relatively obvious fractures (such as, distal radius or hip fractures). However, it is not known whether this also is true for subtle or relatively nondisplaced fractures that are often difficult to see on radiographs, such as scaphoid fractures.

QUESTIONS/PURPOSES : (1) What is the diagnostic accuracy, sensitivity, and specificity of a deep learning algorithm in detecting radiographically visible and occult scaphoid fractures using four radiographic imaging views? (2) Does adding patient demographic (age and sex) information improve the diagnostic performance of the deep learning algorithm? (3) Are orthopaedic surgeons better at diagnostic accuracy, sensitivity, and specificity compared with deep learning? (4) What is the interobserver reliability among five human observers and between human consensus and deep learning algorithm?

METHODS : We retrospectively searched the picture archiving and communication system (PACS) to identify 300 patients with a radiographic scaphoid series, until we had 150 fractures (127 visible on radiographs and 23 only visible on MRI) and 150 non-fractures with a corresponding CT or MRI as the reference standard for fracture diagnosis. At our institution, MRIs are usually ordered for patients with scaphoid tenderness and normal radiographs, and a CT with radiographically visible scaphoid fracture. We used a deep learning algorithm (a convolutional neural network [CNN]) for automated fracture detection on radiographs. Deep learning, an advanced subset of artificial intelligence, combines artificial neuronal layers to resemble a neuron cell. CNNs-essentially deep learning algorithms resembling interconnected neurons in the human brain-are most commonly used for image analysis. Area under the receiver operating characteristic curve (AUC) was used to evaluate the algorithm's diagnostic performance. An AUC of 1.0 would indicate perfect prediction, whereas 0.5 would indicate that a prediction is no better than a flip of a coin. The probability of a scaphoid fracture generated by the CNN, sex, and age were included in a multivariable logistic regression to determine whether this would improve the algorithm's diagnostic performance. Diagnostic performance characteristics (accuracy, sensitivity, and specificity) and reliability (kappa statistic) were calculated for the CNN and for the five orthopaedic surgeon observers in our study.

RESULTS : The algorithm had an AUC of 0.77 (95% CI 0.66 to 0.85), 72% accuracy (95% CI 60% to 84%), 84% sensitivity (95% CI 0.74 to 0.94), and 60% specificity (95% CI 0.46 to 0.74). Adding age and sex did not improve diagnostic performance (AUC 0.81 [95% CI 0.73 to 0.89]). Orthopaedic surgeons had better specificity (0.93 [95% CI 0.93 to 0.99]; p < 0.01), while accuracy (84% [95% CI 81% to 88%]) and sensitivity (0.76 [95% CI 0.70 to 0.82]; p = 0.29) did not differ between the algorithm and human observers. Although the CNN was less specific in diagnosing relatively obvious fractures, it detected five of six occult scaphoid fractures that were missed by all human observers. The interobserver reliability among the five surgeons was substantial (Fleiss' kappa = 0.74 [95% CI 0.66 to 0.83]), but the reliability between the algorithm and human observers was only fair (Cohen's kappa = 0.34 [95% CI 0.17 to 0.50]).

CONCLUSIONS : Initial experience with our deep learning algorithm suggests that it has trouble identifying scaphoid fractures that are obvious to human observers. Thirteen false positive suggestions were made by the CNN, which were correctly detected by the five surgeons. Research with larger datasets-preferably also including information from physical examination-or further algorithm refinement is merited.

LEVEL OF EVIDENCE : Level III, diagnostic study.

Langerhuizen David W G, Bulstra Anne Eva J, Janssen Stein J, Ring David, Kerkhoffs Gino M M J, Jaarsma Ruurd L, Doornberg Job N

2020-May-19

Ophthalmology Ophthalmology

Optical Coherence Tomography-based Diabetic Macula Edema Screening with Artificial Intelligence.

In Journal of the Chinese Medical Association : JCMA

BACKGROUND : Optical coherence tomography (OCT) is considered as a sensitive and non-invasive tool to evaluate the macular lesions. In patients with diabetes mellitus (DM), the existence of diabetic macular edema (DME) can cause significant vision impairment and further intravitreal injection (IVI) of anti-vascular endothelial growth factor (VEGF) is needed. However, the increasing number of DM patients makes it a big burden for clinicians to manually determine whether DME exists in the OCT images. The artificial intelligence (AI) now enormously applied to many medical territories may help reduce the burden on clinicians.

METHODS : We selected DME patients receiving IVI of anti-VEGF or corticosteroid at Taipei Veterans General Hospital in 2017. All macular cross-sectional scan OCT images were collected retrospectively from the eyes of these patients from January 2008 to July 2018. We further established AI models based on convolutional neural network architecture to determine whether the DM patients have DME by OCT images.

RESULTS : Based on the convolutional neural networks, InceptionV3 and VGG16, our AI system achieved a high DME diagnostic accuracy of 93.09% and 92.82%, respectively. The sensitivity of the VGG16 and InceptionV3 models were 96.48% and 95.15%. The specificity was corresponding to 86.67% and 89.63% for VGG16 and InceptionV3, respectively. We further developed an OCT-driven platform based on these AI models.

CONCLUSION : We successfully set up AI models to provide an accurate diagnosis of DME by OCT images. These models may assist clinicians in screening DME in DM patients in the future.

Hwang De-Kuang, Chou Yu-Bai, Lin Tai-Chi, Yang Hsin-Yu, Kao Zih-Kai, Kao Chung-Lan, Yang Yi-Ping, Chen Shih-Jen, Hsu Chih-Chien, Jheng Ying-Chun

2020-May-20

General General

Artificial neural network and bioavailability of the immunosuppression drug.

In Current opinion in organ transplantation ; h5-index 32.0

PURPOSE OF REVIEW : The success of organ transplant is determined by number of demographic, clinical, immunological and genetic variables. Artificial intelligence tools, such as artificial neural networks (ANNs) or classification and regression trees (CART) can handle multiple independent variables and predict the dependent variables by deducing the complex nonlinear relationships between variables.

RECENT FINDINGS : In the last two decades, several researchers employed these tools to identify donor-recipient matching pairs, to optimize immunosuppressant doses, to predict allograft survival and to minimize adverse drug reactions. These models showed better performance characteristics than the empirical dosing strategies in terms of sensitivity, specificity, overall accuracy, or area under the curve of receiver-operating characteristic curves. The performance of the models was dependent directly on the input variables. Recent studies identified protein biomarkers and pharmacogenetic determinants of immunosuppressants as additional variables that increase the precision in prediction. Accessibility of medical records, proper follow-up of transplant cases, deep understanding of pharmacokinetic and pharmacodynamic pathways of immunosuppressant drugs coupled with genomic and proteomic markers are essential in developing an effective artificial intelligence platform for transplantation.

SUMMARY : Artificial intelligence has a greater clinical utility both in pretransplantation and posttransplantation periods to get favourable clinical outcomes, thus ensuring successful graft survival.

Naushad Shaik Mohammad, Kutala Vijay Kumar

2020-May-20

General General

An Exploration Into the Use of a Chatbot for Patients With Inflammatory Bowel Diseases: Retrospective Cohort Study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The emergence of chatbots in health care is fast approaching. Data on the feasibility of chatbots for chronic disease management are scarce.

OBJECTIVE : This study aimed to explore the feasibility of utilizing natural language processing (NLP) for the categorization of electronic dialog data of patients with inflammatory bowel diseases (IBD) for use in the development of a chatbot.

METHODS : Electronic dialog data collected between 2013 and 2018 from a care management platform (UCLA eIBD) at a tertiary referral center for IBD at the University of California, Los Angeles, were used. Part of the data was manually reviewed, and an algorithm for categorization was created. The algorithm categorized all relevant dialogs into a set number of categories using NLP. In addition, 3 independent physicians evaluated the appropriateness of the categorization.

RESULTS : A total of 16,453 lines of dialog were collected and analyzed. We categorized 8324 messages from 424 patients into seven categories. As there was an overlap in these categories, their frequencies were measured independently as symptoms (2033/6193, 32.83%), medications (2397/6193, 38.70%), appointments (1518/6193, 24.51%), laboratory investigations (2106/6193, 34.01%), finance or insurance (447/6193, 7.22%), communications (2161/6193, 34.89%), procedures (617/6193, 9.96%), and miscellaneous (624/6193, 10.08%). Furthermore, in 95.0% (285/300) of cases, there were minor or no differences in categorization between the algorithm and the three independent physicians.

CONCLUSIONS : With increased adaptation of electronic health technologies, chatbots could have great potential in interacting with patients, collecting data, and increasing efficiency. Our categorization showcases the feasibility of using NLP in large amounts of electronic dialog for the development of a chatbot algorithm. Chatbots could allow for the monitoring of patients beyond consultations and potentially empower and educate patients and improve clinical outcomes.

Zand Aria, Sharma Arjun, Stokes Zack, Reynolds Courtney, Montilla Alberto, Sauk Jenny, Hommes Daniel

2020-May-26

artificial intelligence, chatbots, eHealth, inflammatory bowel diseases, natural language processing, telehealth

Surgery Surgery

Artificial Intelligence-Assisted System in Postoperative Follow-up of Orthopedic Patients: Exploratory Quantitative and Qualitative Study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Patient follow-up is an essential part of hospital ward management. With the development of deep learning algorithms, individual follow-up assignments might be completed by artificial intelligence (AI). We developed an AI-assisted follow-up conversational agent that can simulate the human voice and select an appropriate follow-up time for quantitative, automatic, and personalized patient follow-up. Patient feedback and voice information could be collected and converted into text data automatically.

OBJECTIVE : The primary objective of this study was to compare the cost-effectiveness of AI-assisted follow-up to manual follow-up of patients after surgery. The secondary objective was to compare the feedback from AI-assisted follow-up to feedback from manual follow-up.

METHODS : The AI-assisted follow-up system was adopted in the Orthopedic Department of Peking Union Medical College Hospital in April 2019. A total of 270 patients were followed up through this system. Prior to that, 2656 patients were followed up by phone calls manually. Patient characteristics, telephone connection rate, follow-up rate, feedback collection rate, time spent, and feedback composition were compared between the two groups of patients.

RESULTS : There was no statistically significant difference in age, gender, or disease between the two groups. There was no significant difference in telephone connection rate (manual: 2478/2656, 93.3%; AI-assisted: 249/270, 92.2%; P=.50) or successful follow-up rate (manual: 2301/2478, 92.9%; AI-assisted: 231/249, 92.8%; P=.96) between the two groups. The time spent on 100 patients in the manual follow-up group was about 9.3 hours. In contrast, the time spent on the AI-assisted follow-up was close to 0 hours. The feedback rate in the AI-assisted follow-up group was higher than that in the manual follow-up group (manual: 68/2656, 2.5%; AI-assisted: 28/270, 10.3%; P<.001). The composition of feedback was different in the two groups. Feedback from the AI-assisted follow-up group mainly included nursing, health education, and hospital environment content, while feedback from the manual follow-up group mostly included medical consultation content.

CONCLUSIONS : The effectiveness of AI-assisted follow-up was not inferior to that of manual follow-up. Human resource costs are saved by AI. AI can help obtain comprehensive feedback from patients, although its depth and pertinence of communication need to be improved.

Bian Yanyan, Xiang Yongbo, Tong Bingdu, Feng Bin, Weng Xisheng

2020-May-26

artificial intelligence, conversational agent, cost-effectiveness, follow-up

Public Health Public Health

Using Reports of Own and Others' Symptoms and Diagnosis on Social Media to Predict COVID-19 Case Counts: Observational Infoveillance Study in Mainland China.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : COVID-19 has affected more than 200 countries and territories worldwide. It poses an extraordinary challenge for public health systems, because screening and surveillance capacity-especially during the beginning of the outbreak-is often severely limited, fueling the outbreak as many patients unknowingly infect others.

OBJECTIVE : We present an effort to collect and analyze COVID-19 related posts on the popular Twitter-like social media site in China, Weibo. To our knowledge, this infoveillance study employs the largest, most comprehensive and fine-grained social media data to date to predict COVID-19 case counts in mainland China.

METHODS : We built a Weibo user pool of 250 million, approximately half of the entire monthly active Weibo user population. Using a comprehensive list of 167 keywords, we retrieved and analyzed around 15 million COVID-19 related posts from our user pool, from November 1, 2019 to March 31, 2020. We developed a machine learning classifier to identify "sick posts," which are reports of one's own and other people's symptoms and diagnosis related to COVID-19. Using officially reported case counts as the outcome, we then estimated the Granger causality of sick posts and other COVID-19 posts on daily case counts. For a subset of geotagged posts (3.10% of all retrieved posts), we also ran separate predictive models for Hubei province, the epicenter of the initial outbreak, and the rest of mainland China.

RESULTS : We found that reports of symptoms and diagnosis of COVID-19 significantly predicted daily case counts, up to 14 days ahead of official statistics, whereas other COVID-19 posts did not have similar predictive power. For the subset of geotagged posts, we found that the predictive pattern held true for both Hubei province and the rest of mainland China, regardless of unequal distribution of healthcare resources and outbreak timeline.

CONCLUSIONS : Public social media data can be usefully harnessed to predict infection cases and inform timely responses. Researchers and disease control agencies should pay close attention to the social media infosphere regarding COVID-19. On top of monitoring overall search and posting activities, leveraging machine learning approaches and theoretical understandings of information sharing behaviors is a promising approach to identifying true disease signals and improving the effectiveness of infoveillance.

CLINICALTRIAL :

Shen Cuihua, Chen Anfan, Luo Chen, Zhang Jingwen, Feng Bo, Liao Wang

2020-May-25

General General

A Self-Paced Regularization Framework for Partial-Label Learning.

In IEEE transactions on cybernetics

Partial-label learning (PLL) aims to solve the problem where each training instance is associated with a set of candidate labels, one of which is the correct label. Most PLL algorithms try to disambiguate the candidate label set, by either simply treating each candidate label equally or iteratively identifying the true label. Nonetheless, existing algorithms usually treat all labels and instances equally, and the complexities of both labels and instances are not taken into consideration during the learning stage. Inspired by the successful application of a self-paced learning strategy in the machine-learning field, we integrate the self-paced regime into the PLL framework and propose a novel self-paced PLL (SP-PLL) algorithm, which could control the learning process to alleviate the problem by ranking the priorities of the training examples together with their candidate labels during each learning iteration. Extensive experiments and comparisons with other baseline methods demonstrate the effectiveness and robustness of the proposed method.

Lyu Gengyu, Feng Songhe, Wang Tao, Lang Congyan

2020-May-18

General General

Estimation of causal effects of multiple treatments in observational studies with a binary outcome.

In Statistical methods in medical research

There is a dearth of robust methods to estimate the causal effects of multiple treatments when the outcome is binary. This paper uses two unique sets of simulations to propose and evaluate the use of Bayesian additive regression trees in such settings. First, we compare Bayesian additive regression trees to several approaches that have been proposed for continuous outcomes, including inverse probability of treatment weighting, targeted maximum likelihood estimator, vector matching, and regression adjustment. Results suggest that under conditions of non-linearity and non-additivity of both the treatment assignment and outcome generating mechanisms, Bayesian additive regression trees, targeted maximum likelihood estimator, and inverse probability of treatment weighting using generalized boosted models provide better bias reduction and smaller root mean squared error. Bayesian additive regression trees and targeted maximum likelihood estimator provide more consistent 95% confidence interval coverage and better large-sample convergence property. Second, we supply Bayesian additive regression trees with a strategy to identify a common support region for retaining inferential units and for avoiding extrapolating over areas of the covariate space where common support does not exist. Bayesian additive regression trees retain more inferential units than the generalized propensity score-based strategy, and shows lower bias, compared to targeted maximum likelihood estimator or generalized boosted model, in a variety of scenarios differing by the degree of covariate overlap. A case study examining the effects of three surgical approaches for non-small cell lung cancer demonstrates the methods.

Hu Liangyuan, Gu Chenyang, Lopez Michael, Ji Jiayi, Wisnivesky Juan

2020-May-25

Causal inference, generalized propensity score, inverse probability of treatment weighting, machine learning, matching

Public Health Public Health

Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review.

In Journal of medical systems ; h5-index 48.0

Coronaviruses (CoVs) are a large family of viruses that are common in many animal species, including camels, cattle, cats and bats. Animal CoVs, such as Middle East respiratory syndrome-CoV, severe acute respiratory syndrome (SARS)-CoV, and the new virus named SARS-CoV-2, rarely infect and spread among humans. On January 30, 2020, the International Health Regulations Emergency Committee of the World Health Organisation declared the outbreak of the resulting disease from this new CoV called 'COVID-19', as a 'public health emergency of international concern'. This global pandemic has affected almost the whole planet and caused the death of more than 315,131 patients as of the date of this article. In this context, publishers, journals and researchers are urged to research different domains and stop the spread of this deadly virus. The increasing interest in developing artificial intelligence (AI) applications has addressed several medical problems. However, such applications remain insufficient given the high potential threat posed by this virus to global public health. This systematic review addresses automated AI applications based on data mining and machine learning (ML) algorithms for detecting and diagnosing COVID-19. We aimed to obtain an overview of this critical virus, address the limitations of utilising data mining and ML algorithms, and provide the health sector with the benefits of this technique. We used five databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus and performed three sequences of search queries between 2010 and 2020. Accurate exclusion criteria and selection strategy were applied to screen the obtained 1305 articles. Only eight articles were fully evaluated and included in this review, and this number only emphasised the insufficiency of research in this important area. After analysing all included studies, the results were distributed following the year of publication and the commonly used data mining and ML algorithms. The results found in all papers were discussed to find the gaps in all reviewed papers. Characteristics, such as motivations, challenges, limitations, recommendations, case studies, and features and classes used, were analysed in detail. This study reviewed the state-of-the-art techniques for CoV prediction algorithms based on data mining and ML assessment. The reliability and acceptability of extracted information and datasets from implemented technologies in the literature were considered. Findings showed that researchers must proceed with insights they gain, focus on identifying solutions for CoV problems, and introduce new improvements. The growing emphasis on data mining and ML techniques in medical fields can provide the right environment for change and improvement.

Albahri A S, Hamid Rula A, Alwan Jwan K, Al-Qays Z T, Zaidan A A, Zaidan B B, Albahri A O S, AlAmoodi A H, Khlaf Jamal Mawlood, Almahdi E M, Thabet Eman, Hadi Suha M, Mohammed K I, Alsalem M A, Al-Obaidi Jameel R, Madhloom H T

2020-May-25

Artificial Intelligence, Biological Data Mining, COVID-19, Coronaviruses, MERS-CoV, Machine Learning, SARS-CoV-2

General General

Precisely Predicting Acute Kidney Injury with Convolutional Neural Network Based on Electronic Health Record Data

ArXiv Preprint

The incidence of Acute Kidney Injury (AKI) commonly happens in the Intensive Care Unit (ICU) patients, especially in the adults, which is an independent risk factor affecting short-term and long-term mortality. Though researchers in recent years highlight the early prediction of AKI, the performance of existing models are not precise enough. The objective of this research is to precisely predict AKI by means of Convolutional Neural Network on Electronic Health Record (EHR) data. The data sets used in this research are two public Electronic Health Record (EHR) databases: MIMIC-III and eICU database. In this study, we take several Convolutional Neural Network models to train and test our AKI predictor, which can precisely predict whether a certain patient will suffer from AKI after admission in ICU according to the last measurements of the 16 blood gas and demographic features. The research is based on Kidney Disease Improving Global Outcomes (KDIGO) criteria for AKI definition. Our work greatly improves the AKI prediction precision, and the best AUROC is up to 0.988 on MIMIC-III data set and 0.936 on eICU data set, both of which outperform the state-of-art predictors. And the dimension of the input vector used in this predictor is much fewer than that used in other existing researches. Compared with the existing AKI predictors, the predictor in this work greatly improves the precision of early prediction of AKI by using the Convolutional Neural Network architecture and a more concise input vector. Early and precise prediction of AKI will bring much benefit to the decision of treatment, so it is believed that our work is a very helpful clinical application.

Yu Wang, JunPeng Bao, JianQiang Du, YongFeng Li

2020-05-27

General General

GreenSea: Visual Soccer Analysis Using Broad Learning System.

In IEEE transactions on cybernetics

Modern soccer increasingly places trust in visual analysis and statistics rather than only relying on the human experience. However, soccer is an extraordinarily complex game that no widely accepted quantitative analysis methods exist. The statistics collection and visualization are time consuming which result in numerous adjustments. To tackle this issue, we developed GreenSea, a visual-based assessment system designed for soccer game analysis, tactics, and training. The system uses a broad learning system (BLS) to train the model in order to avoid the time-consuming issue that traditional deep learning may suffer. Users are able to apply multiple views of a soccer game, and visual summarization of essential statistics using advanced visualization and animation that are available. A marking system trained by BLS is designed to perform quantitative analysis. A novel recurrent discriminative BLS (RDBLS) is proposed to carry out long-term tracking. In our RDBLS, the structure is adjusted to have better performance on the binary classification problem of the discriminative model. Several experiments are carried out to verify that our proposed RDBLS model can outperform the standard BLS and other methods. Two studies were conducted to verify the effectiveness of our GreenSea. The first study was on how GreenSea assists a youth training coach to assess each trainee's performance for selecting most potential players. The second study was on how GreenSea was used to help the U20 Shanghai soccer team coaching staff analyze games and make tactics during the 13th National Games. Our studies have shown the usability of GreenSea and the values of our system to both amateur and expert users.

Sheng Bin, Li Ping, Zhang Yuhan, Mao Lijuan, Chen C L Philip

2020-May-21

General General

Resonant Machine Learning Based on Complex Growth Transform Dynamical Systems.

In IEEE transactions on neural networks and learning systems

Traditional energy-based learning models associate a single energy metric to each configuration of variables involved in the underlying optimization process. Such models associate the lowest energy state with the optimal configuration of variables under consideration and are thus inherently dissipative. In this article, we propose an energy-efficient learning framework that exploits structural and functional similarities between a machine-learning network and a general electrical network satisfying Tellegen's theorem. In contrast to the standard energy-based models, the proposed formulation associates two energy components, namely, active and reactive energy with the network. The formulation ensures that the network's active power is dissipated only during the process of learning, whereas the reactive power is maintained to be zero at all times. As a result, in steady state, the learned parameters are stored and self-sustained by electrical resonance determined by the network's nodal inductances and capacitances. Based on this approach, this article introduces three novel concepts: 1) a learning framework where the network's active-power dissipation is used as a regularization for a learning objective function that is subjected to zero total reactive-power constraint; 2) a dynamical system based on complex-domain, continuous-time growth transforms that optimizes the learning objective function and drives the network toward electrical resonance under steady-state operation; and 3) an annealing procedure that controls the tradeoff between active-power dissipation and the speed of convergence. As a representative example, we show how the proposed framework can be used for designing resonant support vector machines (SVMs), where the support vectors correspond to an LC network with self-sustained oscillations. We also show that this resonant network dissipates less active power compared with its non-resonant counterpart.

Chatterjee Oindrila, Chakrabartty Shantanu

2020-May-18

General General

Reachable Set Estimation for Neural Network Control Systems: A Simulation-Guided Approach.

In IEEE transactions on neural networks and learning systems

The vulnerability of artificial intelligence (AI) and machine learning (ML) against adversarial disturbances and attacks significantly restricts their applicability in safety-critical systems including cyber-physical systems (CPS) equipped with neural network components at various stages of sensing and control. This article addresses the reachable set estimation and safety verification problems for dynamical systems embedded with neural network components serving as feedback controllers. The closed-loop system can be abstracted in the form of a continuous-time sampled-data system under the control of a neural network controller. First, a novel reachable set computation method in adaptation to simulations generated out of neural networks is developed. The reachability analysis of a class of feedforward neural networks called multilayer perceptrons (MLPs) with general activation functions is performed in the framework of interval arithmetic. Then, in combination with reachability methods developed for various dynamical system classes modeled by ordinary differential equations, a recursive algorithm is developed for over-approximating the reachable set of the closed-loop system. The safety verification for neural network control systems can be performed by examining the emptiness of the intersection between the over-approximation of reachable sets and unsafe sets. The effectiveness of the proposed approach has been validated with evaluations on a robotic arm model and an adaptive cruise control system.

Xiang Weiming, Tran Hoang-Dung, Yang Xiaodong, Johnson Taylor T

2020-May-14

Radiology Radiology

Differentiation of Benign from Malignant Pulmonary Nodules by Using a Convolutional Neural Network to Determine Volume Change at Chest CT.

In Radiology ; h5-index 91.0

Background Deep learning may help to improve computer-aided detection of volume (CADv) measurement of pulmonary nodules at chest CT. Purpose To determine the efficacy of a deep learning method for improving CADv for measuring the solid and ground-glass opacity (GGO) volumes of a nodule, doubling time (DT), and the change in volume at chest CT. Materials and Methods From January 2014 to December 2016, patients with pulmonary nodules at CT were retrospectively reviewed. CADv without and with a convolutional neural network (CNN) automatically determined total nodule volume change per day and DT. Area under the curves (AUCs) on a per-nodule basis and diagnostic accuracy on a per-patient basis were compared among all indexes from CADv with and without CNN for differentiating benign from malignant nodules. Results The CNN training set was 294 nodules in 217 patients, the validation set was 41 nodules in 32 validation patients, and the test set was 290 nodules in 188 patients. A total of 170 patients had 290 nodules (mean size ± standard deviation, 11 mm ± 5; range, 4-29 mm) diagnosed as 132 malignant nodules and 158 benign nodules. There were 132 solid nodules (46%), 106 part-solid nodules (36%), and 52 ground-glass nodules (18%). The test set results showed that the diagnostic performance of the CNN with CADv for total nodule volume change per day was larger than DT of CADv with CNN (AUC, 0.94 [95% confidence interval {CI}: 0.90, 0.96] vs 0.67 [95% CI: 0.60, 0.74]; P < .001) and CADv without CNN (total nodule volume change per day: AUC, 0.69 [95% CI: 0.62, 0.75]; P < .001; DT: AUC, 0.58 [95% CI: 0.51, 0.65]; P < .001). The accuracy of total nodule volume change per day of CADv with CNN was significantly higher than that of CADv without CNN (P < .001) and DT of both methods (P < .001). Conclusion Convolutional neural network is useful for improving accuracy of computer-aided detection of volume measurement and nodule differentiation capability at CT for patients with pulmonary nodules. © RSNA, 2020 Online supplemental material is available for this article.

Ohno Yoshiharu, Aoyagi Kota, Yaguchi Atsushi, Seki Shinichiro, Ueno Yoshiko, Kishida Yuji, Takenaka Daisuke, Yoshikawa Takeshi

2020-May-26

General General

An up-to-date overview of computational polypharmacology in modern drug discovery.

In Expert opinion on drug discovery ; h5-index 34.0

INTRODUCTION : In recent years, computational polypharmacology has gained significant attention to study the promiscuous nature of drugs. Despite tremendous challenges, community-wide efforts have led to a variety of novel approaches for predicting drug polypharmacology. In particular, some rapid advances using machine learning and artificial intelligence have been reported with great success.

AREAS COVERED : In this article, the authors provide a comprehensive update on the current state-of-the-art polypharmacology approaches and their applications, focusing on those reports published after our 2017 review article. The authors particularly discuss some novel, groundbreaking concepts, and methods that have been developed recently and applied to drug polypharmacology studies.

EXPERT OPINION : Polypharmacology is evolving and novel concepts are being introduced to counter the current challenges in the field. However, major hurdles remain including incompleteness of high-quality experimental data, lack of in vitro and in vivo assays to characterize multi-targeting agents, shortage of robust computational methods, and challenges to identify the best target combinations and design effective multi-targeting agents. Fortunately, numerous national/international efforts including multi-omics and artificial intelligence initiatives as well as most recent collaborations on addressing the COVID-19 pandemic have shown significant promise to propel the field of polypharmacology forward.

Chaudhari Rajan, Fong Long Wolf, Tan Zhi, Huang Beibei, Zhang Shuxing

2020-May-26

Drug Polypharmacology, artificial Intelligence, deep Learning, drug Repurposing, molecular Promiscuity, multi-omics, multi-targeting Design, network Pharmacology, off-targets

Radiology Radiology

Functional connectome contractions in temporal lobe epilepsy: Microstructural underpinnings and predictors of surgical outcome.

In Epilepsia

OBJECTIVE : Temporal lobe epilepsy (TLE) is the most common drug-resistant epilepsy in adults. Although it is commonly related to hippocampal pathology, increasing evidence suggests structural changes beyond the mesiotemporal lobe. Functional anomalies and their link to underlying structural alterations, however, remain incompletely understood.

METHODS : We studied 30 drug-resistant TLE patients and 57 healthy controls using multimodal magnetic resonance imaging (MRI) analyses. All patients had histologically verified hippocampal sclerosis and underwent postoperative imaging to outline the extent of their surgical resection. Our analysis leveraged a novel resting-state functional MRI framework that parameterizes functional connectivity distance, consolidating topological and physical properties of macroscale brain networks. Functional findings were integrated with morphological and microstructural metrics, and utility for surgical outcome prediction was assessed using machine learning techniques.

RESULTS : Compared to controls, TLE patients showed connectivity distance reductions in temporoinsular and prefrontal networks, indicating topological segregation of functional networks. Testing for morphological and microstructural associations, we observed that functional connectivity contractions occurred independently from TLE-related cortical atrophy but were mediated by microstructural changes in the underlying white matter. Following our imaging study, all patients underwent an anterior temporal lobectomy as a treatment of their seizures, and postsurgical seizure outcome was determined at a follow-up at least 1 year after surgery. Using a regularized supervised machine learning paradigm with fivefold cross-validation, we demonstrated that patient-specific functional anomalies predicted postsurgical seizure outcome with 76 ± 4% accuracy, outperforming classifiers operating on clinical and structural imaging features.

SIGNIFICANCE : Our findings suggest connectivity distance contractions as a macroscale substrate of TLE. Functional topological isolation may represent a microstructurally mediated network mechanism that tilts the balance toward epileptogenesis in affected networks and that may assist in patient-specific surgical prognostication.

Larivière Sara, Weng Yifei, Vos de Wael Reinder, Royer Jessica, Frauscher Birgit, Wang Zhengge, Bernasconi Andrea, Bernasconi Neda, Schrader Dewi V, Zhang Zhiqiang, Bernhardt Boris C

2020-May-26

connectivity distance, mesiotemporal lobe, networks, neuroimaging

Radiology Radiology

Placental MRI: Effect of maternal position and uterine contractions on placental BOLD MRI measurements.

In Placenta ; h5-index 40.0

INTRODUCTION : Before using blood-oxygen-level-dependent magnetic resonance imaging (BOLD MRI) during maternal hyperoxia as a method to detect individual placental dysfunction, it is necessary to understand spatiotemporal variations that represent normal placental function. We investigated the effect of maternal position and Braxton-Hicks contractions on estimates obtained from BOLD MRI of the placenta during maternal hyperoxia.

METHODS : For 24 uncomplicated singleton pregnancies (gestational age 27-36 weeks), two separate BOLD MRI datasets were acquired, one in the supine and one in the left lateral maternal position. The maternal oxygenation was adjusted as 5 min of room air (21% O2), followed by 5 min of 100% FiO2. After datasets were corrected for signal non-uniformities and motion, global and regional BOLD signal changes in R2* and voxel-wise Time-To-Plateau (TTP) in the placenta were measured. The overall placental and uterine volume changes were determined across time to detect contractions.

RESULTS : In mothers without contractions, increases in global placental R2* in the supine position were larger compared to the left lateral position with maternal hyperoxia. Maternal position did not alter global TTP but did result in regional changes in TTP. 57% of the subjects had Braxton-Hicks contractions and 58% of these had global placental R2* decreases during the contraction.

CONCLUSION : Both maternal position and Braxton-Hicks contractions significantly affect global and regional changes in placental R2* and regional TTP. This suggests that both factors must be taken into account in analyses when comparing placental BOLD signals over time within and between individuals.

Abaci Turk Esra, Abulnaga S Mazdak, Luo Jie, Stout Jeffrey N, Feldman Henry A, Turk Ata, Gagoski Borjan, Wald Lawrence L, Adalsteinsson Elfar, Roberts Drucilla J, Bibbo Carolina, Robinson Julian N, Golland Polina, Grant P Ellen, Barth William H

2020-Apr-22

BOLD MRI, Braxton-Hicks contraction, Maternal hyperoxia, Maternal position, Placental MRI

Surgery Surgery

Complication rates among women undergoing preventive mastectomy: An Austrian registry.

In The breast journal

Germline variations in the BRCA-1 and BRCA-2 genes are associated with an increased risk of breast cancer. These variants are found in 5% of all breast cancer cases. Prophylactic mastectomy is the most effective risk-reducing method and shows high rates of patient satisfaction and acceptance. We established a registry of Austrian BRCA-1 and BRCA-2 mutation carriers who had undergone mastectomy for oncologic or prophylactic reasons. Data were collected on the type of operation, complications, and type of reconstructive surgery for patients between 2014 and 2017. The complication rate in patients with nipple-sparing mastectomy was significantly lower (23.1%) than in those with other types of mastectomies (60.7%; P = .005). In patients with implant-based breast reconstruction, subpectoral placement was associated with a significantly higher rate of complications than prepectoral placement (P = .025). Median implant volume was 350 cc (range: 155-650 cc), and a 100-cc increase was associated with doubling of the odds of a complication (regression coefficient = 0.007); based on this finding, some surgeons may decide on using smaller implants. In summary, we identified significant associations between the risk of complications and surgical characteristics, and found host factors like diabetes, BMI, and smoking among Austrian patients with BRCA-1 and BRCA-2 variants.

Leser Carmen, Deutschmann Christine, Dorffner Georg, Gschwantler-Kaulich Daphne, Castillo Deirdre Maria, Abayev Sara, Stübler Madeleine, Reitsamer Roland, Singer Christian

2020-May-25

BRCA, breast reconstruction, complications, prophylactic mastectomy, registry

General General

Integrative species delimitation reveals cryptic diversity in the southern Appalachian Antrodiaetus unicolor (Araneae: Antrodiaetidae) species complex.

In Molecular ecology

Although species delimitation can be highly contentious, the development of reliable methods to accurately ascertain species boundaries is an imperative step in cataloguing and describing Earth's quickly disappearing biodiversity. Spider species delimitation remains largely based on morphological characters; however, many mygalomorph spider populations are morphologically indistinguishable from each other yet have considerable molecular divergence. The focus of our study, the Antrodiaetus unicolor species complex containing two sympatric species, exhibits this pattern of relative morphological stasis with considerable genetic divergence across its distribution. A past study using two molecular markers, COI and 28S, revealed that A. unicolor is paraphyletic with respect to A. microunicolor. To better investigate species boundaries in the complex, we implement the cohesion species concept and employ multiple lines of evidence for testing genetic exchangeability and ecological interchangeability. Our integrative approach includes extensively sampling homologous loci across the genome using a RADseq approach (3RAD), assessing population structure across their geographic range using multiple genetic clustering analyses that include STRUCTURE, PCA, and a recently developed unsupervised machine learning approach (Variational Autoencoder). We evaluate ecological similarity by using large-scale ecological data for niche-based distribution modeling. Based on our analyses, we conclude that this complex has at least one additional species as well as confirm species delimitations based on previous less comprehensive approaches. Our study demonstrates the efficacy of genomic-scale data for recognizing cryptic species, suggesting that species delimitation with one data type, whether one mitochondrial gene or morphology, may underestimate true species diversity in morphologically homogenous taxa with low vagility.

Newton Lacie G, Starrett James, Hendrixson Brent E, Derkarabetian Shahan, Bond Jason E

2020-May-26

Public Health Public Health

Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review.

In Journal of medical systems ; h5-index 48.0

Coronaviruses (CoVs) are a large family of viruses that are common in many animal species, including camels, cattle, cats and bats. Animal CoVs, such as Middle East respiratory syndrome-CoV, severe acute respiratory syndrome (SARS)-CoV, and the new virus named SARS-CoV-2, rarely infect and spread among humans. On January 30, 2020, the International Health Regulations Emergency Committee of the World Health Organisation declared the outbreak of the resulting disease from this new CoV called 'COVID-19', as a 'public health emergency of international concern'. This global pandemic has affected almost the whole planet and caused the death of more than 315,131 patients as of the date of this article. In this context, publishers, journals and researchers are urged to research different domains and stop the spread of this deadly virus. The increasing interest in developing artificial intelligence (AI) applications has addressed several medical problems. However, such applications remain insufficient given the high potential threat posed by this virus to global public health. This systematic review addresses automated AI applications based on data mining and machine learning (ML) algorithms for detecting and diagnosing COVID-19. We aimed to obtain an overview of this critical virus, address the limitations of utilising data mining and ML algorithms, and provide the health sector with the benefits of this technique. We used five databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus and performed three sequences of search queries between 2010 and 2020. Accurate exclusion criteria and selection strategy were applied to screen the obtained 1305 articles. Only eight articles were fully evaluated and included in this review, and this number only emphasised the insufficiency of research in this important area. After analysing all included studies, the results were distributed following the year of publication and the commonly used data mining and ML algorithms. The results found in all papers were discussed to find the gaps in all reviewed papers. Characteristics, such as motivations, challenges, limitations, recommendations, case studies, and features and classes used, were analysed in detail. This study reviewed the state-of-the-art techniques for CoV prediction algorithms based on data mining and ML assessment. The reliability and acceptability of extracted information and datasets from implemented technologies in the literature were considered. Findings showed that researchers must proceed with insights they gain, focus on identifying solutions for CoV problems, and introduce new improvements. The growing emphasis on data mining and ML techniques in medical fields can provide the right environment for change and improvement.

Albahri A S, Hamid Rula A, Alwan Jwan K, Al-Qays Z T, Zaidan A A, Zaidan B B, Albahri A O S, AlAmoodi A H, Khlaf Jamal Mawlood, Almahdi E M, Thabet Eman, Hadi Suha M, Mohammed K I, Alsalem M A, Al-Obaidi Jameel R, Madhloom H T

2020-May-25

Artificial Intelligence, Biological Data Mining, COVID-19, Coronaviruses, MERS-CoV, Machine Learning, SARS-CoV-2

General General

[Risk communication: a social construction.]

In Recenti progressi in medicina

The technological developments of artificial intelligence have created predictive models capable of improving prognostic accuracy compared to traditional methods. However, there are many uncertainties about their effective applicability in clinical reality, due to the lack of rigorous methodological studies. These new perspectives can offer the starting point for a reflection on risk communication, taking for example a very frequent situation in daily practice, cardiovascular risk, describing ancient but still current concepts.

Collecchia Giampaolo

2020-May

General General

Precision psychiatry with immunological and cognitive biomarkers: a multi-domain prediction for the diagnosis of bipolar disorder or schizophrenia using machine learning.

In Translational psychiatry ; h5-index 60.0

Precision psychiatry is attracting increasing attention lately as a recognized priority. One of the goals of precision psychiatry is to develop tools capable of aiding a clinically informed psychiatric diagnosis objectively. Cognitive, inflammatory and immunological factors are altered in both bipolar disorder (BD) and schizophrenia (SZ), however, most of these alterations do not respect diagnostic boundaries from a phenomenological perspective and possess great variability in different individuals with the same phenotypic diagnosis and, consequently, none so far has proven to have the ability of reliably aiding in the differential diagnosis of BD and SZ. We developed a probabilistic multi-domain data integration model consisting of immune and inflammatory biomarkers in peripheral blood and cognitive biomarkers using machine learning to predict diagnosis of BD and SZ. A total of 416 participants, being 323, 372, and 279 subjects for blood, cognition and combined biomarkers analysis, respectively. Our multi-domain model performances for the BD vs. control (sensitivity 80% and specificity 71%) and for the SZ vs. control (sensitivity 84% and specificity 81%) pairs were high in general, however, our multi-domain model had only moderate performance for the differential diagnosis of BD and SZ (sensitivity 71% and specificity 73%). In conclusion, our results show that the diagnosis of BD and of SZ, and that the differential diagnosis of BD and SZ can be predicted with possible clinical utility by a computational machine learning algorithm employing blood and cognitive biomarkers, and that their integration in a multi-domain outperforms algorithms based in only one domain. Independent studies are needed to validate these findings.

Fernandes Brisa S, Karmakar Chandan, Tamouza Ryad, Tran Truyen, Yearwood John, Hamdani Nora, Laouamri Hakim, Richard Jean-Romain, Yolken Robert, Berk Michael, Venkatesh Svetha, Leboyer Marion

2020-May-24

Radiology Radiology

Multimodal Image Analysis for Assessing Multiple Sclerosis and Future Prospects Powered by Artificial Intelligence.

In Seminars in ultrasound, CT, and MR

The purpose of this paper is to serve as a template for greater understanding for the practicing radiologist about key steps to perform multimodality computer analysis of MRI images, specifically in multiple sclerosis patients. With this understanding, radiologists will be better equipped about how best to process and analyze MRI imaging data and obtain accurate quantitative information for MS patient evaluation. A secondary intent of this article is to improve radiologist understanding of how artificial intelligence will be employed in the future for better patient stratification, and for evaluation of response to therapy in both clinical care and drug trials.

Kim Minjeong, Jewells Valerie

2020-Jun

General General

Interleukin-6 deficiency exacerbates Huntington's disease model phenotypes.

In Molecular neurodegeneration ; h5-index 49.0

Huntington's disease (HD) is an incurable neurodegenerative disorder caused by CAG trinucleotide expansions in the huntingtin gene. Markers of both systemic and CNS immune activation and inflammation have been widely noted in HD and mouse models of HD. In particular, elevation of the pro-inflammatory cytokine interleukin-6 (IL-6) is the earliest reported marker of immune activation in HD, and this elevation has been suggested to contribute to HD pathogenesis. To test the hypothesis that IL-6 deficiency would be protective against the effects of mutant huntingtin, we generated R6/2 HD model mice that lacked IL-6. Contrary to our prediction, IL-6 deficiency exacerbated HD-model associated behavioral phenotypes. Single nuclear RNA Sequencing (snRNA-seq) analysis of striatal cell types revealed that IL-6 deficiency led to the dysregulation of various genes associated with synaptic function, as well as the BDNF receptor Ntrk2. These data suggest that IL-6 deficiency exacerbates the effects of mutant huntingtin through dysregulation of genes of known relevance to HD pathobiology in striatal neurons, and further suggest that modulation of IL-6 to a level that promotes proper regulation of genes associated with synaptic function may hold promise as an HD therapeutic target.

Wertz Mary H, Pineda S Sebastian, Lee Hyeseung, Kulicke Ruth, Kellis Manolis, Heiman Myriam

2020-May-24

Huntington’s disease, Interleukin-6, snRNA-seq

General General

Automatization and improvement of μCT analysis for murine lung disease models using a deep learning approach.

In Respiratory research ; h5-index 45.0

BACKGROUND : One of the main diagnostic tools for lung diseases in humans is computed tomography (CT). A miniaturized version, micro-CT (μCT) is utilized to examine small rodents including mice. However, fully automated threshold-based segmentation and subsequent quantification of severely damaged lungs requires visual inspection and manual correction.

METHODS : Here we demonstrate the use of densitometry on regions of interest (ROI) in automatically detected portions of the lung, thus avoiding the need for lung segmentation. Utilizing deep learning approaches, the middle part of the lung is found in a μCT-stack and a ROI is placed in the left and the right lobe.

RESULTS : The intensity values within the ROIs of the μCT images were collected and subsequently used for the calculation of different lung-related parameters, such as mean lung attenuation (MLA), mode, full width at half maximum (FWHM), and skewness. For validation, the densitometric approach was correlated with histological readouts (Ashcroft Score, Mean Linear Intercept).

CONCLUSION : We here show an automated tool that allows rapid and in-depth analysis of μCT scans of different murine models of lung disease.

Birk Gerald, Kästle Marc, Tilp Cornelia, Stierstorfer Birgit, Klee Stephan

2020-May-24

Pathology Pathology

Pathology features and the results of treatment of two cases of posterior choroidal leiomyoma.

In BMC ophthalmology

BACKGROUND : Posterior choroidal leiomyoma is an extremely rare tumor, to our knowledge, less than 10 cases reported in the literature. The definite diagnosis can be confirmed by immunohistochemistry, and local resection is preferable to enucleation for the posterior choroidal leiomyoma.

CASE PRESENTATION : Two adult Asian women presented with progressive vision loss in their right eyes. Ophthalmic examination revealed an amelanotic dome-shaped choroidal mass located in the fundus with yellowish exudative retinal detachment. Clinical differential diagnosis of a nonpigmented choroidal neoplasm mainly includes amelanotic melanoma, atypical hemangioma, metastatic carcinoma, as well as the rare posterior choroidal leiomyoma. Considering the choroidal lesion was more likely to be a benign tumor, then we performed the treatment of local resection by pars plana vitrectomy and the histopathological examination confirmed the diagnosis of choroidal leiomyoma. The best corrected visual acuity of the patients was more than 20/100 on 6-month follow-up.

CONCLUSIONS : From these two posterior choroidal neoplasm cases, we were able to demonstrate local resection by the 23 to 25-gauge mircoinvasive vitrectomy for excision of intraocular tumors is a feasible treatment for choroidal leiomyoma.

Zhou Nan, Wei Wenbin, Xu Xiaolin

2020-May-24

Case report, Local resection, Microinvasive vitrectomy, Pathology features, Posterior choroidal leiomyoma

General General

RintC: fast and accuracy-aware decomposition of distributions of RNA secondary structures with extended logsumexp.

In BMC bioinformatics

BACKGROUND : Analysis of secondary structures is essential for understanding the functions of RNAs. Because RNA molecules thermally fluctuate, it is necessary to analyze the probability distributions of their secondary structures. Existing methods, however, are not applicable to long RNAs owing to their high computational complexity. Additionally, previous research has suffered from two numerical difficulties: overflow and significant numerical errors.

RESULT : In this research, we reduced the computational complexity of calculating the landscape of the probability distribution of secondary structures by introducing a maximum-span constraint. In addition, we resolved numerical computation problems through two techniques: extended logsumexp and accuracy-guaranteed numerical computation. We analyzed the stability of the secondary structures of 16S ribosomal RNAs at various temperatures without overflow. The results obtained are consistent with previous research on thermophilic bacteria, suggesting that our method is applicable in thermal stability analysis. Furthermore, we quantitatively assessed numerical stability using our method..

CONCLUSION : These results demonstrate that the proposed method is applicable to long RNAs..

Takizawa Hiroki, Iwakiri Junichi, Asai Kiyoshi

2020-May-24

Accuracy-guaranteed numerical computation, Dynamic programming, Interval arithmetic, RNA secondary structure, Ribosomal RNA.

General General

Predicting left ventricular contractile function via Gaussian process emulation in aortic-banded rats.

In Philosophical transactions. Series A, Mathematical, physical, and engineering sciences

Cardiac contraction is the result of integrated cellular, tissue and organ function. Biophysical in silico cardiac models offer a systematic approach for studying these multi-scale interactions. The computational cost of such models is high, due to their multi-parametric and nonlinear nature. This has so far made it difficult to perform model fitting and prevented global sensitivity analysis (GSA) studies. We propose a machine learning approach based on Gaussian process emulation of model simulations using probabilistic surrogate models, which enables model parameter inference via a Bayesian history matching (HM) technique and GSA on whole-organ mechanics. This framework is applied to model healthy and aortic-banded hypertensive rats, a commonly used animal model of heart failure disease. The obtained probabilistic surrogate models accurately predicted the left ventricular pump function (R2 = 0.92 for ejection fraction). The HM technique allowed us to fit both the control and diseased virtual bi-ventricular rat heart models to magnetic resonance imaging and literature data, with model outputs from the constrained parameter space falling within 2 SD of the respective experimental values. The GSA identified Troponin C and cross-bridge kinetics as key parameters in determining both systolic and diastolic ventricular function. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.

Longobardi S, Lewalle A, Coveney S, Sjaastad I, Espe E K S, Louch W E, Musante C J, Sher A, Niederer S A

2020-Jun-12

Gaussian process, aortic-banded rat, global sensitivity analysis, history matching, three-dimensional bi-ventricular model

General General

Twitter discussions and concerns about COVID-19 pandemic: Twitter data analysis using a machine learning approach

ArXiv Preprint

The objective of the study is to examine coronavirus disease (COVID-19) related discussions, concerns, and sentiments that emerged from tweets posted by Twitter users. We collected 22 million Twitter messages related to the COVID-19 pandemic using a list of 25 hashtags such as "coronavirus," "COVID-19," "quarantine" from March 1 to April 21 in 2020. We used a machine learning approach, Latent Dirichlet Allocation (LDA), to identify popular unigram, bigrams, salient topics and themes, and sentiments in the collected Tweets. Popular unigrams included "virus," "lockdown," and "quarantine." Popular bigrams included "COVID-19," "stay home," "corona virus," "social distancing," and "new cases." We identified 13 discussion topics and categorized them into different themes, such as "Measures to slow the spread of COVID-19," "Quarantine and shelter-in-place order in the U.S.," "COVID-19 in New York," "Virus misinformation and fake news," "A need for a vaccine to stop the spread," "Protest against the lockdown," and "Coronavirus new cases and deaths." The dominant sentiments for the spread of coronavirus were anticipation that measures that can be taken, followed by a mixed feeling of trust, anger, and fear for different topics. The public revealed a significant feeling of fear when they discussed the coronavirus new cases and deaths. The study concludes that Twitter continues to be an essential source for infodemiology study by tracking rapidly evolving public sentiment and measuring public interests and concerns. Already emerged pandemic fear, stigma, and mental health concerns may continue to influence public trust when there occurs a second wave of COVID-19 or a new surge of the imminent pandemic. Hearing and reacting to real concerns from the public can enhance trust between the healthcare systems and the public as well as prepare for a future public health emergency.

Jia Xue, Junxiang Chen, Ran Hu, Chen Chen, ChengDa Zheng, Tingshao Zhu

2020-05-26

Radiology Radiology

Response Score of Deep Learning for Out-of-Distribution Sample Detection of Medical Images.

In Journal of biomedical informatics ; h5-index 55.0

Deep learning Convolutional Neural Networks have achieved remarkable performance in a variety of classification tasks. The data-driven nature of deep learning indicates that a model behaves in response to the data used to train the model, and the quality of datasets may lead to substantial influence on the model's performance, especially when dealing with complicated clinical images. In this paper, we propose a simple and novel method to investigate and quantify a deep learning model's response with respect to a given sample, allowing us to detect out-of-distribution samples based on a newly proposed metric, ResponseScore. The key idea is that samples belonging to different classes may have different degrees of influence on a model. Wequantify the resulting consequence of a single sample to a trained-model and relate the quantitative measure of the consequence (by the Response Score) to detect the out-of-distribution samples. The proposed method can find multiple applications such as 1) recognizing abnormal samples, 2) detecting mixed-domain data, and 3) identifying mislabeled data. We present extensive experiments on the three different applications using four biomedical imaging datasets. Experimental results show that our method exhibits remarkable performance and outperforms the compared methods.

Gao Long, Wu Shandong

2020-May-22

Anomaly detection, Data quality, Deep learning, Medical image analysis, Out-of-distribution detection

General General

Encoder-Decoder CNN Models for Automatic Tracking of Tongue Contours in Real-time Ultrasound Data.

In Methods (San Diego, Calif.)

One application of medical ultrasound imaging is to visualize and characterize human tongue shape and motion in real-time to study healthy or impaired speech production. Due to the low-contrast characteristic and noisy nature of ultrasound images, it requires knowledge about the tongue structure and ultrasound data interpretation for users to recognize tongue locations and gestures easily. Moreover, quantitative analysis of tongue motion needs the tongue contour to be extracted, tracked and visualized instead of the whole tongue region. Manual tongue contour extraction is a cumbersome, subjective, and error-prone task. Furthermore, it is not a feasible solution for real-time applications where the tongue contour moves rapidly with nuance gestures. This paper presents two new deep neural networks (named BowNet models) that benefit from the ability of global prediction of encoding-decoding fully convolutional neural networks and the capability of full-resolution extraction of dilated convolutions. Both qualitatively and quantitatively studies over datasets from two ultrasound machines disclosed the outstanding performances of the proposed deep learning models in terms of performance speed and robustness. Experimental results also revealed a significant improvement in the accuracy of prediction maps due to the better exploration and exploitation ability of the proposed network models.

Hamed Mozaffari M, Lee Won-Sook

2020-May-22

Automatic ultrasound tongue contour extraction, Deep Dilated Convolutional Neural Network, Real-time tongue contour tracking, Semantic image segmentation

Public Health Public Health

Evidence that the Kennedy and polyamine pathways are dysregulated in human brain in cases of dementia with Lewy bodies.

In Brain research

Disruptions of brain metabolism are considered integral to the pathogenesis of dementia, but thus far little is known of how dementia with Lewy bodies (DLB) impacts the brain metabolome. DLB is less well known than other neurodegenerative diseases such as Alzheimer's and Parkinson's disease which is perhaps why it is under-investigated. This exploratory study aimed to address current knowledge gaps in DLB research and search for potentially targetable biochemical pathways for therapeutics. It also aimed to better understand metabolic similarities and differences with other dementias. Combined metabolomic analyses of 1H NMR and tandem mass spectrometry of neocortical post-mortem brain tissue (Brodmann region 7) from autopsy confirmed cases of DLB (n=15) were compared with age/gender-matched, non-cognitively impaired healthy controls (n=30). Following correction for multiple comparisons, only 2 metabolites from a total of 219 measured compounds significantly differed. Putrescine was suppressed (55.4%) in DLB and O-phosphocholine was elevated (52.5%). We identified a panel of 5 metabolites (PC aa C38:4, O-Phosphocholine, putrescine, 4-Aminobutyrate, and SM C16:0) capable of accurately discriminating between DLB and control subjects. Deep Learning (DL) provided the best predictive model following 10-fold cross validation (AUROC (95% CI) = 0.80 (0.60-1.0)) with sensitivity and specificity equal to 0.92 and 0.88, respectively. Altered brain levels of putrescine and O-phosphocholine indicate that the Kennedy pathway and polyamine metabolism are perturbed in DLB. These are accompanied by a consistent underlying trend of lipid dysregulation. As yet it is unclear whether these are a cause or consequence of DLB onset.

Akyol Sumeyya, Yilmaz Ali, Joon Oh Kyung, Ugur Zafer, Aydas Buket, McGuinness Bernadette, Passmore Peter, Kehoe Patrick G, Maddens Michael, Green Brian D, Graham Stewart F

2020-May-22

(1)H NMR, Dementia with Lewy Bodies, brain, metabolic pathways, metabolomics, targeted mass spectrometry

General General

A note on the interpretation of tree-based regression models.

In Biometrical journal. Biometrische Zeitschrift

Tree-based models are a popular tool for predicting a response given a set of explanatory variables when the regression function is characterized by a certain degree of complexity. Sometimes, they are also used to identify important variables and for variable selection. We show that if the generating model contains chains of direct and indirect effects, then the typical variable importance measures suggest selecting as important mainly the background variables, which have a strong indirect effect, disregarding the variables that directly influence the response. This is attributable mainly to the variable choice in the first steps of the algorithm selecting the splitting variable and to the greedy nature of such search. This pitfall could be relevant when using tree-based algorithms for understanding the underlying generating process, for population segmentation and for causal inference.

Gottard Anna, Vannucci Giulia, Marchetti Giovanni Maria

2020-May-25

interpretable machine learning, marginal and conditional dependence, underlying explanatory process, variable importance, variable selection bias

General General

An AI approach to COVID-19 infection risk assessment in virtual visits: a case report.

In Journal of the American Medical Informatics Association : JAMIA

OBJECTIVE : In an effort to improve the efficiency of computer algorithms applied to screening for COVID-19 testing, we used natural language processing (NLP) and artificial intelligence (AI)-based methods with unstructured patient data collected through telehealth visits.

METHODS : After segmenting and parsing documents, we conducted analysis of overrepresented words in patient symptoms. We then developed a word embedding-based convolutional neural network for predicting COVID-19 test results based on patients' self-reported symptoms.

RESULTS : Text analytics revealed that concepts such as "smell" and "taste" were more prevalent than expected in patients testing positive. As a result, screening algorithms were adapted to include these symptoms. The deep learning model yielded an AUC of 0.729 for predicting positive results and was subsequently applied to prioritize testing appointment scheduling.

DISCUSSION : Informatics tools such as NLP and AI methods can have significant clinical impacts when applied to data streams early in the development of clinical systems for outbreak response.

Obeid Jihad S, Davis Matthew, Turner Matthew, Meystre Stephane M, Heider Paul M, Lenert Leslie A

2020-May-25

AI, COVID-19, artificial intelligence, risk assessment, text analytics

General General

HECNet: a hierarchical approach to Enzyme Function Classification using a Siamese Triplet Network.

In Bioinformatics (Oxford, England)

MOTIVATION : Understanding an enzyme's function is one of the most crucial problem domains in computational biology. Enzymes are a key component in all organisms and many industrial processes as they help in fighting diseases and speed up essential chemical reactions. They have wide applications and therefore, the discovery of new enzymatic proteins can accelerate biological research and commercial productivity. Biological experiments, to determine an enzyme's function, are time-consuming and resource expensive.

RESULTS : In this study, we propose a novel computational approach to predict an enzyme's function up to the fourth level of the Enzyme Commission (EC) Number. Many studies have attempted to predict an enzyme's function. Yet, no approach has properly tackled the fourth and final level of the EC number. The fourth level holds great significance as it gives us the most specific information of how an enzyme performs its function. Our method uses innovative deep learning approaches along with an efficient hierarchical classification scheme to predict an enzyme's precise function. On a dataset of 11,353 enzymes and 402 classes, we achieved a hierarchical accuracy and Macro-F1 score of 91.2% and 81.9%, respectively, on the 4th level. Moreover, our method can be used to predict the function of enzyme isoforms with considerable success. This methodology is broadly applicable for genome-wide prediction that can subsequently lead to automated annotation of enzyme databases and the identification of better/cheaper enzymes for commercial activities.

AVAILABILITY : The web-server can be freely accessed at http://hecnet.cbrlab.org/.

SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.

Memon Safyan Aman, Khan Kinaan Aamir, Naveed Hammad

2020-May-25

Public Health Public Health

TaxoNN: Ensemble of Neural Networks on Stratified Microbiome Data for Disease Prediction.

In Bioinformatics (Oxford, England)

MOTIVATION : Research supports the potential use of microbiome as a predictor of some diseases. Motivated by the findings that microbiome data is complex in nature and there is an inherent correlation due to hierarchical taxonomy of microbial Operational Taxonomic Units (OTUs), we propose a novel machine learning method incorporating a stratified approach to group OTUs into phylum clusters. Convolutional Neural Networks (CNNs) were used to train within each of the clusters individually. Further, through an ensemble learning approach, features obtained from each cluster were then concatenated to improve prediction accuracy. Our two-step approach comprising of stratification prior to combining multiple CNNs, aided in capturing the relationships between OTUs sharing a phylum efficiently, as compared to using a single CNN ignoring OTU correlations.

RESULTS : We used simulated datasets containing 168 OTUs in 200 cases and 200 controls for model testing. Thirty-two OTUs, potentially associated with risk of disease were randomly selected and interactions between three OTUs were used to introduce non-linearity. We also implemented this novel method in two human microbiome studies: (i) cirrhosis with 118 cases, 114 controls; (ii) type 2 diabetes with 170 cases, 174 controls; to demonstrate the model's effectiveness. Extensive experimentation and comparison against conventional machine learning techniques yielded encouraging results. We obtained mean AUC values of 0.88, 0.92, 0.75, showing a consistent increment (5%, 3%, 7%) in simulations, cirrhosis and type 2 diabetes data respectively, against the next best performing method, Random Forest.

AVAILABILITY : https://github.com/divya031090/TaxoNN_OTU.

Sharma Divya, Paterson Andrew D, Xu Wei

2020-May-25

oncology Oncology

How Will Artificial Intelligence Affect Patient-Clinician Relationships?

In AMA journal of ethics

Artificial intelligence (AI) could improve the efficiency and accuracy of health care delivery, but how will AI influence the patient-clinician relationship? While many suggest that AI might improve the patient-clinician relationship, various underlying assumptions will need to be addressed to bring these potential benefits to fruition. Will off-loading tedious work result in less time spent on administrative burden during patient visits? If so, will clinicians use this extra time to engage relationally with their patients? Moreover, given the desire and opportunity, will clinicians have the ability to engage in effective relationship building with their patients? In order for the best-case scenario to become a reality, clinicians and technology developers must recognize and address these assumptions during the development of AI and its implementation in health care.

Nagy Matthew, Sisk Bryan

2020-May-01

General General

Revealing False Positive Features in Epileptic EEG Identification.

In International journal of neural systems

Feature selection plays a vital role in the detection and discrimination of epileptic seizures in electroencephalogram (EEG) signals. The state-of-the-art EEG classification techniques commonly entail the extraction of the multiple features that would be fed into classifiers. For some techniques, the feature selection strategies have been used to reduce the dimensionality of the entire feature space. However, most of these approaches focus on the performance of classifiers while neglecting the association between the feature and the EEG activity itself. To enhance the inner relationship between the feature subset and the epileptic EEG task with a promising classification accuracy, we propose a machine learning-based pipeline using a novel feature selection algorithm built upon a knockoff filter. First, a number of temporal, spectral, and spatial features are extracted from the raw EEG signals. Second, the proposed feature selection algorithm is exploited to obtain the optimal subgroup of features. Afterwards, three classifiers including [Formula: see text]-nearest neighbor (KNN), random forest (RF) and support vector machine (SVM) are used. The experimental results on the Bonn dataset demonstrate that the proposed approach outperforms the state-of-the-art techniques, with accuracy as high as 99.93% for normal and interictal EEG discrimination and 98.95% for interictal and ictal EEG classification. Meanwhile, it has achieved satisfactory sensitivity (95.67% in average), specificity (98.83% in average), and accuracy (98.89% in average) over the Freiburg dataset.

Lian Jian, Shi Yunfeng, Zhang Yan, Jia Weikuan, Fan Xiaojun, Zheng Yuanjie

2020-May-23

Epileptic seizure, classification, electroencephalogram, feature selection

General General

An AI approach to COVID-19 infection risk assessment in virtual visits: a case report.

In Journal of the American Medical Informatics Association : JAMIA

OBJECTIVE : In an effort to improve the efficiency of computer algorithms applied to screening for COVID-19 testing, we used natural language processing (NLP) and artificial intelligence (AI)-based methods with unstructured patient data collected through telehealth visits.

METHODS : After segmenting and parsing documents, we conducted analysis of overrepresented words in patient symptoms. We then developed a word embedding-based convolutional neural network for predicting COVID-19 test results based on patients' self-reported symptoms.

RESULTS : Text analytics revealed that concepts such as "smell" and "taste" were more prevalent than expected in patients testing positive. As a result, screening algorithms were adapted to include these symptoms. The deep learning model yielded an AUC of 0.729 for predicting positive results and was subsequently applied to prioritize testing appointment scheduling.

DISCUSSION : Informatics tools such as NLP and AI methods can have significant clinical impacts when applied to data streams early in the development of clinical systems for outbreak response.

Obeid Jihad S, Davis Matthew, Turner Matthew, Meystre Stephane M, Heider Paul M, Lenert Leslie A

2020-May-25

AI, COVID-19, artificial intelligence, risk assessment, text analytics

Radiology Radiology

Towards computer-aided severity assessment: training and validation of deep neural networks for geographic extent and opacity extent scoring of chest X-rays for SARS-CoV-2 lung disease severity

ArXiv Preprint

Background: A critical step in effective care and treatment planning for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the assessment of the severity of disease progression. Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity, with two important assessment metrics being extent of lung involvement and degree of opacity. In this proof-of-concept study, we assess the feasibility of computer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deep learning system. Materials and Methods: Data consisted of 130 CXRs from SARS-CoV-2 positive patient cases from the Cohen study. Geographic extent and opacity extent were scored by two board-certified expert chest radiologists (with 20+ years of experience) and a 2nd-year radiology resident. The deep neural networks used in this study are based on a COVID-Net network architecture. 100 versions of the network were independently learned (50 to perform geographic extent scoring and 50 to perform opacity extent scoring) using random subsets of CXRs from the Cohen study, and evaluated the networks using stratified Monte Carlo cross-validation experiments. Findings: The deep neural networks yielded R$^2$ of 0.673 $\pm$ 0.004 and 0.636 $\pm$ 0.002 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively, in stratified Monte Carlo cross-validation experiments. The best performing networks achieved R$^2$ of 0.865 and 0.746 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively. Interpretation: The results are promising and suggest that the use of deep neural networks on CXRs could be an effective tool for computer-aided assessment of SARS-CoV-2 lung disease severity, although additional studies are needed before adoption for routine clinical use.

Alexander Wong, Zhong Qiu Lin, Linda Wang, Audrey G. Chung, Beiyi Shen, Almas Abbasi, Mahsa Hoshmand-Kochi, Timothy Q. Duong

2020-05-26

Radiology Radiology

Towards computer-aided severity assessment: training and validation of deep neural networks for geographic extent and opacity extent scoring of chest X-rays for SARS-CoV-2 lung disease severity

ArXiv Preprint

Background: A critical step in effective care and treatment planning for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the assessment of the severity of disease progression. Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity, with two important assessment metrics being extent of lung involvement and degree of opacity. In this proof-of-concept study, we assess the feasibility of computer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deep learning system. Materials and Methods: Data consisted of 130 CXRs from SARS-CoV-2 positive patient cases from the Cohen study. Geographic extent and opacity extent were scored by two board-certified expert chest radiologists (with 20+ years of experience) and a 2nd-year radiology resident. The deep neural networks used in this study are based on a COVID-Net network architecture. 100 versions of the network were independently learned (50 to perform geographic extent scoring and 50 to perform opacity extent scoring) using random subsets of CXRs from the Cohen study, and evaluated the networks using stratified Monte Carlo cross-validation experiments. Findings: The deep neural networks yielded R$^2$ of 0.673 $\pm$ 0.004 and 0.636 $\pm$ 0.002 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively, in stratified Monte Carlo cross-validation experiments. The best performing networks achieved R$^2$ of 0.865 and 0.746 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively. Interpretation: The results are promising and suggest that the use of deep neural networks on CXRs could be an effective tool for computer-aided assessment of SARS-CoV-2 lung disease severity, although additional studies are needed before adoption for routine clinical use.

Alexander Wong, Zhong Qiu Lin, Linda Wang, Audrey G. Chung, Beiyi Shen, Almas Abbasi, Mahsa Hoshmand-Kochi, Timothy Q. Duong

2020-05-26

General General

High-Throughput Image-Based Aggresome Quantification.

In SLAS discovery : advancing life sciences R & D

Aggresomes are subcellular perinuclear structures where misfolded proteins accumulate by retrograde transport on microtubules. Different methods are available to monitor aggresome formation, but they are often laborious, time-consuming, and not quantitative. Proteostat is a red fluorescent molecular rotor dye, which becomes brightly fluorescent when it binds to protein aggregates. As this reagent was previously validated to detect aggresomes, we have miniaturized its use in 384-well plates and developed a method for high-throughput imaging and quantification of aggresomes. Two different image analysis methods, including one with machine learning, were evaluated. They lead to similar robust data to quantify cells having aggresome, with satisfactory Z' factor values and reproducible EC50 values for compounds known to induce aggresome formation, like proteasome inhibitors. We demonstrated the relevance of this phenotypic assay by screening a chemical library of 1280 compounds to find aggresome modulators. We obtained hits that present similarities in their structural and physicochemical properties. Interestingly, some of them were previously described to modulate autophagy, which could explain their effect on aggresome structures. In summary, we have optimized and validated the Proteostat detection reagent to easily measure aggresome formation in a miniaturized, automated, quantitative, and high-content assay. This assay can be used at low, middle, or high throughput to quantify changes in aggresome formation that could help in the understanding of chemical compound activity in pathologies such as protein misfolding disorders or cancer.

Lesire Laetitia, Chaput Ludovic, Cruz De Casas Paulina, Rousseau Fanny, Piveteau Catherine, Dumont Julie, Pointu David, Déprez Benoît, Leroux Florence

2020-May-25

aggresome, approved drug library, high-content screening, image analysis, phenotypic screening

Pathology Pathology

Multilevel approach to male fertility by machine learning highlights a hidden link between haematological and spermatogenetic cells.

In Andrology ; h5-index 36.0

BACKGROUND : Male infertility represents a complex clinical condition requiring an accurate multilevel assessment, in which machine learning (ML) technology, combining large data series in nonlinear and highly interactive ways, could be innovatively applied.

METHODS : A longitudinal, observational, retrospective, big data study was carried out, applying for the first time the ML in the context of male infertility. A large database including all semen samples collected between 2010 and 2016 was generated, together with blood biochemical examinations, environmental temperature and air pollutants exposure. First, the database was analysed with principal component analysis (PCA) and multivariable linear regression analyses. Second, classification analyses were performed, in which patients were a priori classified according to semen parameters. Third, ML algorithms were applied in a training phase (80% of the entire database) and in a tuning phase (20% of the dataset). Finally, conventional statistical analyses were applied considering semen parameters and those other variables extracted during ML.

RESULTS : The final database included 4,239 patients, aggregating semen analyses, blood and environmental parameters. Classification analyses were able to recognize oligozoospermic, teratozoospermic, asthenozoospermic and patients with altered semen parameters (0.58 accuracy, 0.58 sensitivity and 0.57 specificity). ML algorithms detected three haematological variables, i.e. lymphocytes number, erythrocyte distribution and mean globular volume, significantly related to semen parameters (0.69 accuracy, 0.78 sensitivity and 0.41 specificity).

CONCLUSION : This is the first ML application to male fertility, detecting potential mathematical algorithms able to describe patients' semen characteristics changes. In this setting, a possible hidden link between testicular and haematopoietic tissues was suggested, according to their similar proliferative properties.

Santi Daniele, Spaggiari Giorgia, Casonati Andrea, Casarini Livio, Grassi Roberto, Vecchi Barbara, Roli Laura, De Santis Maria Cristina, Orlando Giovanna, Gravotta Enrica, Baraldi Enrica, Setti Monica, Trenti Tommaso, Simoni Manuela

2020-May-25

Big data, Infertility, Machine learning, Male infertility

oncology Oncology

An Adversarial Machine Learning Framework and Biomechanical Model Guided Approach for Computing 3D Lung Tissue Elasticity from End-Expiration 3DCT.

In Medical physics ; h5-index 59.0

** : Lung elastography aims at measuring the lung parenchymal tissue elasticity for applications ranging from diagnostic purposes to biomechanically-guided deformations. Characterizing the lung tissue elasticity requires 4D lung motion as an input, which is currently estimated by deformably registering 4DCT datasets. Since 4DCT imaging is widely used only in a radiotherapy treatment setup, there is a need to predict the elasticity distribution in the absence of 4D imaging for applications within and outside of radiotherapy domain.

METHODS : In this paper, we present a machine learning based method that predicts the 3D lung tissue elasticity distribution for a given end-expiration 3DCT. The method to predict the lung tissue elasticity from an end-expiration 3DCT employed a deep neural network that predicts the tissue elasticity for the given CT dataset. For training and validation purposes, we employed 5DCT datasets and a finite element biomechanical lung model. The 5DCT model was first used to generate end-expiration lung geometry, which was taken as the source lung geometry for biomechanical modeling. The deformation vector field pointing from end-expiration to end-inhalation was computed from the 5DCT model and taken as input in order to solve for the lung tissue elasticity. An inverse elasticity estimation process was employed, where we iteratively solved for the lung elasticity distribution until the model reproduced the ground truth deformation vector field. The machine learning process uses a specific type of learning process, namely a constrained Generalized Adversarial Neural Network (cGAN) that learned the lung tissue elasticity in a supervised manner. The biomechanically estimated tissue elasticity together with the end-exhalation CT was the input for the supervised learning. The trained cGAN generated the elasticity from a given breathhold CT image. The elasticity estimated was validated in two approaches. In the first approach, a L2-norm based direct comparison was employed between the estimated elasticity and the ground truth elasticity. In the second approach, we generate a synthetic 4DCT using a lung biomechanical model and the estimated elasticity and compare the deformations with the ground-truth 4D deformations using 3 image similarity metrics: Mutual Information (MI), Structured Similarity Index (SSIM), and Normalized Cross Correlation (NCC).

RESULTS : The results show that a cGAN based machine learning approach was effective in computing the lung tissue elasticity given end-expiration CT datasets. For the training data set, we obtained a learning accuracy of 0.44+/- 0.2 KPa. For the validation dataset, consisting of 13 4D datasets, we were able to obtain an accuracy of 0.87 +/- 0.4 KPa. These results show that the cGAN generated elasticity correlates well with that of the underlying ground-truth elasticity. We then integrated the estimated elasticity with the biomechanical model and applied the same boundary conditions in order to generate the end inhalation CT. The cGAN generated images were very similar to that of the original end inhalation CT. The average value of the MI is 1.77 indicating the high local symmetricity between the ground truth and the cGAN elasticity generated end inhalation CT data. The average value of the structural similarity for the 13 patients was observed to be 0.89 indicating the high structural integrity of the cGAN elasticity generated end inhalation CT. Finally, the average NCC value of 0.97 indicates that potential variations in the contrast and brightness of the cGAN elasticity generated end inhalation CT and the ground-truth end inhalation CT.

CONCLUSION : The cGAN generated lung tissue elasticity given an end-expiration CT image can be computed in near real-time. Using the lung tissue elasticity along with a biomechanical model, 4D lung deformations can be generated from a given end-expiration CT images within clinically acceptable numerical accuracy.

Santhanam Anand P, Stiehl Brad, Lauria Michael, Hasse Katelyn, Barjaktarevic Igor, Goldin Jonathan, Low Daniel A

2020-May-25

4DCT, biomechanical modeling, deep learning, lung elastography

General General

Determining the key childhood and adolescent risk factors for future BPD symptoms using regularized regression: comparison to depression and conduct disorder.

In Journal of child psychology and psychiatry, and allied disciplines

OBJECTIVE : Research has yielded factors considered critical to risk for borderline personality disorder (BPD). Yet, these factors overlap and are relevant to other disorders, like depression and conduct disorder (CD). Regularized regression, a machine learning approach, was developed to allow identification of the most important variables in large datasets with correlated predictors. We aimed to identify critical predictors of BPD symptoms in late adolescence (ages 16-18) and determine the specificity of factors to BPD versus disorders with putatively similar etiology.

METHOD : We used a prospective longitudinal dataset (n = 2,450) of adolescent girls assessed on a range of clinical, psychosocial, and demographic factors, highlighted by previous research on BPD. Predictors were grouped by developmental periods: late childhood (8-10) and early (11-13) and mid-adolescence (14-15), yielding 128 variables from 41 constructs. The same variables were used in models predicting depression and CD symptoms.

RESULTS : The best-fitting model for BPD symptoms included 19 predictors and explained 33.2% of the variance. Five constructs - depressive and anxiety symptoms, self-control, harsh punishment, and poor social and school functioning - accounted for most of the variance explained. BPD was differentiated from CD by greater problems with mood and anxiety in BPD and differences in parenting risk factors. Whereas the biggest parenting risk for BPD was a punitive style of parenting, CD was predicted by both punitive and disengaged styles. BPD was differentiated from MDD by greater social problems and poor behavioral control in BPD.

CONCLUSIONS : The best predictors of BPD symptoms in adolescence are features suggesting complex comorbidity, affective activation, and problems with self-control. Though some risk factors were non-specific (e.g., inattention), the disorders were distinguished in clinically significant ways.

Beeney Joseph E, Forbes Erika E, Hipwell Alison E, Nance Melissa, Mattia Alexis, Lawless Joely M, Banihashemi Layla, Stepp Stephanie D

2020-May-25

Risk factors, borderline personality disorder, comorbidity, longitudinal studies, machine learning

oncology Oncology

The Use of Artificial Intelligence and Deep Machine Learning in Oncologic Histopathology.

In Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology

BACKGROUND : Recently, there has been a momentous drive to apply advanced artificial intelligence (AI) technologies to diagnostic medicine. The introduction of AI has provided vast new opportunities to improve healthcare and has introduced a new wave of heightened precision in oncologic pathology. The impact of AI on oncologic pathology has now become apparent and its use with respect to oral oncology is still in the nascent stage.

DISCUSSION : A foundational overview of AI classification systems used in medicine and a review of common terminology used in machine learning and computational pathology will be presented. This paper provides a focused review on the recent advances in AI and deep learning in oncologic histopathology and oral oncology. In addition, specific emphasis on recent studies that have applied these technologies to oral cancer prognostication will also be discussed.

CONCLUSION : Machine and deep learning methods designed to enhance prognostication of oral cancer have been proposed with much of the work focused on prediction models on patient survival and locoregional recurrences in patients with oral squamous cell carcinomas (OSCC). Few studies have explored machine learning methods on OSCC digital histopathologic images. It is evident that further research at the whole slide image-level is needed and future collaborations with computer scientists may progress the field of oral oncology.

Sultan Ahmed S, Elgharib Mohamed A, Tavares Tiffany, Jessri Maryam, Basile John R

2020-May-24

Artificial Intelligence, Computational Pathology, Deep Learning, Digital Pathology, Head and Neck Cancer, Machine Learning, Oncologic Histopathology

General General

From real-world patient data to individualized treatment effects using machine learning: Current and future methods to address underlying challenges.

In Clinical pharmacology and therapeutics

Clinical decision making needs to be supported by evidence that treatments are beneficial to individual patients. While randomized control trials are the gold standard for testing and introducing new drugs, due to the focus on specific questions with respect to establishing efficacy and safety versus standard treatment, they do not provide a full characterization of the heterogeneity in the final intended treatment population. Conversely, real-world observational data, such as electronic health records contain large amounts of clinical information about heterogeneous patients and their response to treatments. In this paper, we introduce the main opportunities and challenges in using observational data for training machine learning methods to estimate individualized treatment effects and make treatment recommendations. We describe the modelling choices of the state-of-the-art machine learning methods for causal inference, developed for estimating treatment effects both in the cross-section and longitudinal settings. Additionally, we highlight future research directions that could lead to achieving the full potential of leveraging electronic health records and machine learning for making individualized treatment recommendations. We also discuss how experimental data from randomized control trials and Pharmacometric and Quantitative Systems Pharmacology approaches can be used to not only improve machine learning methods, but also provide ways for validating them. These future research directions will require us to collaborate across the scientific disciplines to incorporate models based on randomized control trials and known disease processes, physiology and pharmacology into these machine learning models based on electronic health records to fully optimize the opportunity these data present.

Bica Ioana, Alaa Ahmed M, Lambert Craig, van der Schaar Mihaela

2020-May-24

causal inference, electronic health records, individualized dose-response estimation, individualized treatment effects, machine learning

Cardiology Cardiology

Brief Report: Can a Composite Heart Rate Variability Biomarker Shed New Insights About Autism Spectrum Disorder in School-Aged Children?

In Journal of autism and developmental disorders ; h5-index 76.0

Several studies show altered heart rate variability (HRV) in autism spectrum disorder (ASD), but findings are neither universal nor specific to ASD. We apply a set of linear and nonlinear HRV measures-including phase rectified signal averaging-to segments of resting ECG data collected from school-age children with ASD, age-matched typically developing controls, and children with other psychiatric conditions characterized by altered HRV (conduct disorder, depression). We use machine learning to identify time, frequency, and geometric signal-analytical domains that are specific to ASD (receiver operating curve area = 0.89). This is the first study to differentiate children with ASD from other disorders characterized by altered HRV. Despite a small cohort and lack of external validation, results warrant larger prospective studies.

Frasch Martin G, Shen Chao, Wu Hau-Tieng, Mueller Alexander, Neuhaus Emily, Bernier Raphael A, Kamara Dana, Beauchaine Theodore P

2020-May-24

Biomarker, Electrocardiogram, Heart rate variability

Radiology Radiology

Independent attenuation correction of whole body [18F]FDG-PET using a deep learning approach with Generative Adversarial Networks.

In EJNMMI research

BACKGROUND : Attenuation correction (AC) of PET data is usually performed using a second imaging for the generation of attenuation maps. In certain situations however-when CT- or MR-derived attenuation maps are corrupted or CT acquisition solely for the purpose of AC shall be avoided-it would be of value to have the possibility of obtaining attenuation maps only based on PET information. The purpose of this study was to thus develop, implement, and evaluate a deep learning-based method for whole body [18F]FDG-PET AC which is independent of other imaging modalities for acquiring the attenuation map.

METHODS : The proposed method is investigated on whole body [18F]FDG-PET data using a Generative Adversarial Networks (GAN) deep learning framework. It is trained to generate pseudo CT images (CTGAN) based on paired training data of non-attenuation corrected PET data (PETNAC) and corresponding CT data. Generated pseudo CTs are then used for subsequent PET AC. One hundred data sets of whole body PETNAC and corresponding CT were used for training. Twenty-five PET/CT examinations were used as test data sets (not included in training). On these test data sets, AC of PET was performed using the acquired CT as well as CTGAN resulting in the corresponding PET data sets PETAC and PETGAN. CTGAN and PETGAN were evaluated qualitatively by visual inspection and by visual analysis of color-coded difference maps. Quantitative analysis was performed by comparison of organ and lesion SUVs between PETAC and PETGAN.

RESULTS : Qualitative analysis revealed no major SUV deviations on PETGAN for most anatomic regions; visually detectable deviations were mainly observed along the diaphragm and the lung border. Quantitative analysis revealed mean percent deviations of SUVs on PETGAN of - 0.8 ± 8.6% over all organs (range [- 30.7%, + 27.1%]). Mean lesion SUVs showed a mean deviation of 0.9 ± 9.2% (range [- 19.6%, + 29.2%]).

CONCLUSION : Independent AC of whole body [18F]FDG-PET is feasible using the proposed deep learning approach yielding satisfactory PET quantification accuracy. Further clinical validation is necessary prior to implementation in clinical routine applications.

Armanious Karim, Hepp Tobias, Küstner Thomas, Dittmann Helmut, Nikolaou Konstantin, La Fougère Christian, Yang Bin, Gatidis Sergios

2020-May-24

Attenuation correction, Deep learning, PET, Whole body

General General

Prediction of a Cell-Class-Specific Mouse Mesoconnectome Using Gene Expression Data.

In Neuroinformatics

Reconstructing brain connectivity at sufficient resolution for computational models designed to study the biophysical mechanisms underlying cognitive processes is extremely challenging. For such a purpose, a mesoconnectome that includes laminar and cell-class specificity would be a major step forward. We analyzed the ability of gene expression patterns to predict cell-class and layer-specific projection patterns and assessed the functional annotations of the most predictive groups of genes. To achieve our goal we used publicly available volumetric gene expression and connectivity data and we trained computational models to learn and predict cell-class and layer-specific axonal projections using gene expression data. Predictions were done in two ways, namely predicting projection strengths using the expression of individual genes and using the co-expression of genes organized in spatial modules, as well as predicting binary forms of projection. For predicting the strength of projections, we found that ridge (L2-regularized) regression had the highest cross-validated accuracy with a median r2 score of 0.54 which corresponded for binarized predictions to a median area under the ROC value of 0.89. Next, we identified 200 spatial gene modules using a dictionary learning and sparse coding approach. We found that these modules yielded predictions of comparable accuracy, with a median r2 score of 0.51. Finally, a gene ontology enrichment analysis of the most predictive gene groups resulted in significant annotations related to postsynaptic function. Taken together, we have demonstrated a prediction workflow that can be used to perform multimodal data integration to improve the accuracy of the predicted mesoconnectome and support other neuroscience use cases.

Timonidis Nestor, Bakker Rembrandt, Tiesinga Paul

2020-May-24

Axonal projection, Cellularly resolved connectome, Connectomics, Dictionary learning, Gene expression, Gene ontology enrichment analysis, Machine learning, Mouse brain, Predictive models, ROC analysis, Ridge regression, Sparse coding, Spatial gene co-expression

General General

Identifying barley pan-genome sequence anchors using genetic mapping and machine learning.

In TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik

We identified 1.844 million barley pan-genome sequence anchors from 12,306 genotypes using genetic mapping and machine learning. There is increasing evidence that genes from a given crop genotype are far to cover all genes in that species; thus, building more comprehensive pan-genomes is of great importance in genetic research and breeding. Obtaining a thousand-genotype scale pan-genome using deep-sequencing data is currently impractical for species like barley which has a huge and highly repetitive genome. To this end, we attempted to identify barley pan-genome sequence anchors from a large quantity of genotype-by-sequencing (GBS) datasets by combining genetic mapping and machine learning algorithms. Based on the GBS sequences from 11,166 domesticated and 1140 wild barley genotypes, we identified 1.844 million pan-genome sequence anchors. Of them, 532,253 were identified as presence/absence variation (PAV) tags. Through aligning these PAV tags to the genome of hulless barley genotype Zangqing320, our analysis resulted in a validation of 83.6% of them from the domesticated genotypes and 88.6% from the wild barley genotypes. Association analyses against flowering time, plant height and kernel size showed that the relative importance of the PAV and non-PAV tags varied for different traits. The pan-genome sequence anchors based on GBS tags can facilitate the construction of a comprehensive pan-genome and greatly assist various genetic studies including identification of structural variation, genetic mapping and breeding in barley.

Gao Shang, Wu Jinran, Stiller Jiri, Zheng Zhi, Zhou Meixue, Wang You-Gan, Liu Chunji

2020-May-24

Radiology Radiology

Sequential vessel segmentation via deep channel attention network.

In Neural networks : the official journal of the International Neural Network Society

Accurately segmenting contrast-filled vessels from X-ray coronary angiography (XCA) image sequence is an essential step for the diagnosis and therapy of coronary artery disease. However, developing automatic vessel segmentation is particularly challenging due to the overlapping structures, low contrast and the presence of complex and dynamic background artifacts in XCA images. This paper develops a novel encoder-decoder deep network architecture which exploits the several contextual frames of 2D+t sequential images in a sliding window centered at current frame to segment 2D vessel masks from the current frame. The architecture is equipped with temporal-spatial feature extraction in encoder stage, feature fusion in skip connection layers and channel attention mechanism in decoder stage. In the encoder stage, a series of 3D convolutional layers are employed to hierarchically extract temporal-spatial features. Skip connection layers subsequently fuse the temporal-spatial feature maps and deliver them to the corresponding decoder stages. To efficiently discriminate vessel features from the complex and noisy backgrounds in the XCA images, the decoder stage effectively utilizes channel attention blocks to refine the intermediate feature maps from skip connection layers for subsequently decoding the refined features in 2D ways to produce the segmented vessel masks. Furthermore, Dice loss function is implemented to train the proposed deep network in order to tackle the class imbalance problem in the XCA data due to the wide distribution of complex background artifacts. Extensive experiments by comparing our method with other state-of-the-art algorithms demonstrate the proposed method's superior performance over other methods in terms of the quantitative metrics and visual validation. To facilitate the reproductive research in XCA community, we publicly release our dataset and source codes at https://github.com/Binjie-Qin/SVS-net.

Hao Dongdong, Ding Song, Qiu Linwei, Lv Yisong, Fei Baowei, Zhu Yueqi, Qin Binjie

2020-May-13

Channel attention blocks, Class imbalance, Deep learning, Vessel segmentation, X-ray coronary angiography, temporal–spatial features

Ophthalmology Ophthalmology

Deep learning automated detection of reticular pseudodrusen from fundus autofluorescence images or color fundus photographs in AREDS2.

In Ophthalmology ; h5-index 90.0

PURPOSE : To develop and evaluate deep learning models for detecting reticular pseudodrusen (RPD) using fundus autofluorescence (FAF) images or, alternatively, color fundus photographs (CFP), in the context of age-related macular degeneration (AMD).

DESIGN : Application of deep learning models to the Age-Related Eye Disease Study 2 (AREDS2) dataset.

PARTICIPANTS : 11,535 FAF and 11,535 CFP images from longitudinal follow-up of 2,450 participants in the AREDS2 dataset. Gold standard labels were derived from human expert reading center grading of the FAF images and transferred to the corresponding CFP images.

METHODS : A deep learning model was trained to detect RPD in eyes with intermediate to late AMD, using FAF images (FAF model). Using label transfer from FAF to corresponding CFP images, a second deep learning model was trained to detect RPD from CFP (CFP model). Model performance was compared with that of four ophthalmologists using a random subset from the full test set.

MAIN OUTCOME MEASURES : Area under the curve (AUC); kappa; accuracy; F1-score.

RESULTS : On the full test set, the FAF model had AUC 0.939 (95% confidence interval 0.927-0.950), kappa 0.718 (0.685-0.751), accuracy 0.899 (0.887-0.911), and F1-score 0.783 (0.755-0.809). The CFP model had equivalent values of 0.832 (0.812-0.851), 0.470 (0.426-0.511), 0.809 (0.793-0.825), 0.593 (0.557-0.627), respectively. The FAF model demonstrated superior performance to four ophthalmologists on the random subset, showing higher kappa of 0.789 (0.675-0.875) versus range 0.367-0.756, higher accuracy 0.937 (0.907-0.963) versus range 0.696-0.933, and higher F1-score 0.828 (0.725-0.898) versus range 0.539-0.795. The CFP model demonstrated substantially superior performance to four ophthalmologists on the random subset, showing higher kappa 0.471 (0.330-0.606) versus range 0.105-0.180, higher accuracy 0.844 (0.798-0.886) versus range 0.717-0.814, and higher F1-score 0.565 (0.434-0.674) versus range 0.217-0.314.

CONCLUSIONS : Deep learning-enabled automated detection of RPD presence from FAF images achieved a high level of accuracy, equal or superior to that of ophthalmologists. Automated RPD detection using CFP achieved a lower accuracy that still surpassed that of ophthalmologists. Deep learning models can assist, and even augment, the detection of this clinically important, AMD-associated lesion.

Keenan Tiarnan D, Chen Qingyu, Peng Yifan, Domalpally Amitha, Agrón Elvira, Hwang Christopher K, Thavikulwat Alisa T, Lee Debora H, Li Daniel, Wong Wai T, Lu Zhiyong, Chew Emily Y

2020-May-21

General General

Teleological generics.

In Cognition

Certain "generic" generalizations concern functions and purposes, e.g., cars are for driving. Some functional properties yield unacceptable teleological generics: for instance, cars are for parking seems false even though people park cars as often as they drive them. No theory of teleology in philosophy or psychology can explain what makes teleological generics acceptable. However, a recent theory (Prasada, 2017; Prasada & Dillingham, 2006; Prasada, Khemlani, Leslie, & Glucksberg, 2013) argues that a certain type of mental representation - a "principled" connection between a kind and a property - licenses generic generalizations. The account predicts that people should accept teleological generics that describe kinds and properties linked by a principled connection. Under the analysis, car bears a principled connection to driving (a car's primary purpose) and a non-principled connection to parking (an incidental consequence of driving). We report four experiments that tested and corroborated the theory's predictions, and we describe a regression analysis that rules out alternative accounts. We conclude by showing how the theory we developed can serve as the foundation for a general theory of teleological thinking.

Korman Joanna, Khemlani Sangeet

2020-May-21

Concepts, Formal explanations, Generics, Principled connections, Teleology

Pathology Pathology

An interpretable automated detection system for FISH-based HER2 oncogene amplification testing in histo-pathological routine images of breast and gastric cancer diagnostics

ArXiv Preprint

Histo-pathological diagnostics are an inherent part of the everyday work but are particularly laborious and associated with time-consuming manual analysis of image data. In order to cope with the increasing diagnostic case numbers due to the current growth and demographic change of the global population and the progress in personalized medicine, pathologists ask for assistance. Profiting from digital pathology and the use of artificial intelligence, individual solutions can be offered (e.g. detect labeled cancer tissue sections). The testing of the human epidermal growth factor receptor 2 (HER2) oncogene amplification status via fluorescence in situ hybridization (FISH) is recommended for breast and gastric cancer diagnostics and is regularly performed at clinics. Here, we develop an interpretable, deep learning (DL)-based pipeline which automates the evaluation of FISH images with respect to HER2 gene amplification testing. It mimics the pathological assessment and relies on the detection and localization of interphase nuclei based on instance segmentation networks. Furthermore, it localizes and classifies fluorescence signals within each nucleus with the help of image classification and object detection convolutional neural networks (CNNs). Finally, the pipeline classifies the whole image regarding its HER2 amplification status. The visualization of pixels on which the networks' decision occurs, complements an essential part to enable interpretability by pathologists.

Sarah Schmell, Falk Zakrzewski, Walter de Back, Martin Weigert, Uwe Schmidt, Torsten Wenke, Silke Zeugner, Robert Mantey, Christian Sperling, Ingo Roeder, Pia Hoenscheid, Daniela Aust, Gustavo Baretton

2020-05-25

Public Health Public Health

Supervised Mixture of Experts Models for Population Health.

In Methods (San Diego, Calif.)

We propose a machine learning driven approach to derive insights from observational healthcare data to improve public health outcomes. Our goal is to simultaneously identify patient subpopulations with differing health risks and to find those risk factors within each subpopulation. We develop two supervised mixture of experts models: a Supervised Gaussian Mixture model (SGMM) for general features and a Supervised Bernoulli Mixture model (SBMM) tailored to binary features. We demonstrate the two approaches on an analysis of high cost drivers of Medicaid expenditures for inpatient stays. We focus on the three diagnostic categories that accounted for the highest percentage of inpatient expenditures in New York State (NYS) in 2016. When compared with state-of-the-art learning methods (random forests, boosting, neural networks), our approaches provide comparable prediction performance while also extracting insightful subpopulation structure and risk factors. For problems with binary features the proposed SBMM provides as good or better performance than alternative methods while offering insightful explanations. Our results indicate the promise of such approaches for extracting population health insights from electronic health care records.

Shou Xiao, Mavroudeas Georgios, Magdon-Ismail Malik, Figueroa Jose, Kuruzovich Jason N, Bennett Kristin P

2020-May-21

Medical Informatics, Public Health, Subpopulation Detection, Supervised Clustering Learning, Supervised Mixture Models

Pathology Pathology

JSSR: A Joint Synthesis, Segmentation, and Registration System for 3D Multi-Modal Image Alignment of Large-scale Pathological CT Scans

ArXiv Preprint

Multi-modal image registration is a challenging problem yet important clinical task in many real applications and scenarios. For medical imaging based diagnosis, deformable registration among different image modalities is often required in order to provide complementary visual information, as the first step. During the registration, the semantic information is the key to match homologous points and pixels. Nevertheless, many conventional registration methods are incapable to capture the high-level semantic anatomical dense correspondences. In this work, we propose a novel multi-task learning system, JSSR, based on an end-to-end 3D convolutional neural network that is composed of a generator, a register and a segmentor, for the tasks of synthesis, registration and segmentation, respectively. This system is optimized to satisfy the implicit constraints between different tasks unsupervisedly. It first synthesizes the source domain images into the target domain, then an intra-modal registration is applied on the synthesized images and target images. Then we can get the semantic segmentation by applying segmentors on the synthesized images and target images, which are aligned by the same deformation field generated by the registers. The supervision from another fully-annotated dataset is used to regularize the segmentors. We extensively evaluate our JSSR system on a large-scale medical image dataset containing 1,485 patient CT imaging studies of four different phases (i.e., 5,940 3D CT scans with pathological livers) on the registration, segmentation and synthesis tasks. The performance is improved after joint training on the registration and segmentation tasks by $0.9\%$ and $1.9\%$ respectively from a highly competitive and accurate baseline. The registration part also consistently outperforms the conventional state-of-the-art multi-modal registration methods.

Fengze Liu, Jingzheng Cai, Yuankai Huo, Le Lu, Adam P Harrison

2020-05-25

General General

TOP: A Deep Mixture Representation Learning Method for Boosting Molecular Toxicity Prediction.

In Methods (San Diego, Calif.)

At the early stages of the drug discovery, molecule toxicity prediction is crucial to excluding drug candidates that are likely to fail in clinical trials. In this paper, we presented a novel molecular representation method and developed a corresponding deep learning-based framework called TOP (the abbreviation of TOxicity Prediction). TOP integrates specifically designed data preprocessing methods, an RNN based on bidirectional gated recurrent unit (BiGRU), and fully connected neural networks for end-to-end molecular representation learning and chemical toxicity prediction. TOP can automatically learn a mixed molecular representation from not only SMILES contextual information that describes the molecule structure, but also physiochemical properties. Therefore, TOP can overcome the drawbacks of existing methods that use either of them, thus greatly promotes toxicity prediction accuracy. We conducted extensive experiments over 14 classic toxicity prediction tasks on three different benchmark datasets, including balanced and imbalanced ones. The results show that, with the help of the novel molecular representation method, TOP significantly outperforms not only three baseline machine learning methods, but also five state-of-the-art methods.

Peng Yuzhong, Zhang Ziqiao, Jiang Qizhi, Guan Jihong, Zhou Shuigeng

2020-May-21

Deep learning, Drug screening, Molecular representation, Toxicity prediction

Pathology Pathology

Keypoints Localization for Joint Vertebra Detection and Fracture Severity Quantification

ArXiv Preprint

Vertebral body compression fractures are reliable early signs of osteoporosis. Though these fractures are visible on Computed Tomography (CT) images, they are frequently missed by radiologists in clinical settings. Prior research on automatic methods of vertebral fracture classification proves its reliable quality; however, existing methods provide hard-to-interpret outputs and sometimes fail to process cases with severe abnormalities such as highly pathological vertebrae or scoliosis. We propose a new two-step algorithm to localize the vertebral column in 3D CT images and then to simultaneously detect individual vertebrae and quantify fractures in 2D. We train neural networks for both steps using a simple 6-keypoints based annotation scheme, which corresponds precisely to current medical recommendation. Our algorithm has no exclusion criteria, processes 3D CT in 2 seconds on a single GPU, and provides an intuitive and verifiable output. The method approaches expert-level performance and demonstrates state-of-the-art results in vertebrae 3D localization (the average error is 1 mm), vertebrae 2D detection (precision is 0.99, recall is 1), and fracture identification (ROC AUC at the patient level is 0.93).

Maxim Pisov, Vladimir Kondratenko, Alexey Zakharov, Alexey Petraikin, Victor Gombolevskiy, Sergey Morozov, Mikhail Belyaev

2020-05-25

oncology Oncology

Developing an Improved Statistical Approach for Survival Estimation in Bone Metastases Management: The Bone Metastases Ensemble Trees for Survival (BMETS) Model.

In International journal of radiation oncology, biology, physics

BACKGROUND : To determine if a machine learning approach optimizes survival estimation for patients with symptomatic bone metastases (SBM), we developed the Bone Metastases Ensemble Trees for Survival (BMETS) to predict survival using 27 prognostic covariates. To establish relative clinical utility, we compared BMETS to two simpler Cox regression models used in this setting.

METHODS AND MATERIALS : For 492 bone sites in 397 patients evaluated for palliative radiation therapy (RT) for SBM from 1/2007-1/2013, data for 27 clinical variables were collected. These covariates and the primary outcome of time from consultation to death were used to build BMETS utilizing random survival forests. We then performed Cox regressions as per two validated models: Chow's 3-item (C-3) and Westhoff's 2-item (W-2) tools. Model performance was assessed using cross-validation procedures and measured by time-dependent area under the curve (tAUC) for all three models. For temporal validation, a separate dataset comprised of 104 bone sites treated in 85 patients in 2018 was used to estimate tAUC from BMETS.

RESULTS : Median survival was 6.3 months. Variable importance was greatest for performance status, blood cell counts, recent systemic therapy type, and receipt of concurrent non-bone palliative RT. tAUC at 3-, 6-, and 12-months was 0.83, 0.81, and 0.81, respectively, suggesting excellent discrimination of BMETS across post-consultation time points. BMETS outperformed simpler models at each time, with respective tAUC at each time of 0.78, 0.76, and 0.74 for the C-3 model and 0.80, 0.78, and 0.77 for the W-2 model. For the temporal validation set, respective tAUC was similarly high at 0.86, 0.82, and 0.78.

CONCLUSIONS : For patients with SBM, BMETS improved survival predictions versus simpler traditional models. Model performance was maintained when applied to a temporal validation set. To facilitate clinical use, we developed a web platform for data entry and display of BMETS predicted survival probabilities.

Alcorn Sara R, Fiksel Jacob, Wright Jean L, Elledge Christen R, Smith Thomas J, Perng Powell, Saleemi Sarah, McNutt Todd, DeWeese Theodore L, Zeger Scott

2020-May-21

General General

Automating the Surveillance of Mosquito Vectors from Trapped Specimens Using Computer Vision Techniques

ArXiv Preprint

Among all animals, mosquitoes are responsible for the most deaths worldwide. Interestingly, not all types of mosquitoes spread diseases, but rather, a select few alone are competent enough to do so. In the case of any disease outbreak, an important first step is surveillance of vectors (i.e., those mosquitoes capable of spreading diseases). To do this today, public health workers lay several mosquito traps in the area of interest. Hundreds of mosquitoes will get trapped. Naturally, among these hundreds, taxonomists have to identify only the vectors to gauge their density. This process today is manual, requires complex expertise/ training, and is based on visual inspection of each trapped specimen under a microscope. It is long, stressful and self-limiting. This paper presents an innovative solution to this problem. Our technique assumes the presence of an embedded camera (similar to those in smart-phones) that can take pictures of trapped mosquitoes. Our techniques proposed here will then process these images to automatically classify the genus and species type. Our CNN model based on Inception-ResNet V2 and Transfer Learning yielded an overall accuracy of 80% in classifying mosquitoes when trained on 25,867 images of 250 trapped mosquito vector specimens captured via many smart-phone cameras. In particular, the accuracy of our model in classifying Aedes aegypti and Anopheles stephensi mosquitoes (both of which are deadly vectors) is amongst the highest. We present important lessons learned and practical impact of our techniques towards the end of the paper.

Mona Minakshi, Pratool Bharti, Willie B. McClinton III, Jamshidbek Mirzakhalov, Ryan M. Carney, Sriram Chellappan

2020-05-25

General General

Multi-modal Neuroimaging Feature Fusion for Diagnosis of Alzheimer's Disease.

In Journal of neuroscience methods

BACKGROUND : Compared with single-modal neuroimages classification of AD, multi-modal classification can achieve better performance by fusing different information. Exploring synergy among various multi-modal neuroimages is contributed to identifying the pathological process of neurological disorders. However, it is still problematic to effectively exploit multi-modal information since the lack of an effective fusion method.

NEW METHOD : In this paper, we propose a deep multi-modal fusion network based on the attention mechanism, which can selectively extract features from MRI and PET branches and suppress irrelevant information. In the attention model, the fusion ratio of each modality is assigned automatically according to the importance of the data. A hierarchical fusion method is adopted to ensure the effectiveness of Multi-modal Fusion.

RESULTS : Evaluating the model on the ADNI dataset, the experimental results show that it outperforms the state-of-the-art methods. In particular, the final classification results of the NC/AD, SMCI/PMCI and Four-Class are 95.21%, 89.79%, and 86.15%, respectively.

COMPARISON WITH EXISTING METHODS : Different from the early fusion and the late fusion, the hierarchical fusion method contributes to learning the synergy between the multi-modal data. Compared with some other prominent algorithms, the attention model enables our network to focus on the regions of interest and effectively fuse the multi-modal data.

CONCLUSION : Benefit from the hierarchical structure with attention model, the proposed network is capable of exploiting low-level and high-level features extracted from the multi-modal data and improving the accuracy of AD diagnosis. Results show its promising performance.

Zhang Tao, Shi Mingyang

2020-May-21

Alzheimer’s Disease, Attention Model, Classification, Deep Learning, Multi-modal Fusion

General General

Factor Analysis of Mixed Data for Anomaly Detection

ArXiv Preprint

Anomaly detection aims to identify observations that deviate from the typical pattern of data. Anomalous observations may correspond to financial fraud, health risks, or incorrectly measured data in practice. We show detecting anomalies in high-dimensional mixed data is enhanced through first embedding the data then assessing an anomaly scoring scheme. We focus on unsupervised detection and the continuous and categorical (mixed) variable case. We propose a kurtosis-weighted Factor Analysis of Mixed Data for anomaly detection, FAMDAD, to obtain a continuous embedding for anomaly scoring. We illustrate that anomalies are highly separable in the first and last few ordered dimensions of this space, and test various anomaly scoring experiments within this subspace. Results are illustrated for both simulated and real datasets, and the proposed approach (FAMDAD) is highly accurate for high-dimensional mixed data throughout these diverse scenarios.

Matthew Davidow, David S. Matteson

2020-05-25

oncology Oncology

Challenges of Developing a Natural Language Processing Method with Electronic Health Records to Identify Persons with Chronic Mobility Disability.

In Archives of physical medicine and rehabilitation ; h5-index 61.0

OBJECTIVE : To assess the utility of applying natural language processing (NLP) to electronic health records (EHRs) to identify individuals with chronic mobility disability.

DESIGN : We used EHRs from the Research Patient Data Repository, which contains EHRs from a large Massachusetts health care delivery system. This analysis was part of a larger study assessing the effects of disability on diagnosis of colorectal cancer. We applied NLP text extraction software to longitudinal EHRs of colorectal cancer patients to identify persons who use a wheelchair (our indicator of mobility disability for this analysis). We manually reviewed the clinical notes identified by NLP using directed content analysis to identify true cases using wheelchairs, duration or chronicity of use, and documentation quality.

SETTING : EHRs from large health care delivery system PARTICIPANTS: Patients 21-75 years old who were newly diagnosed with colorectal cancer between 2005-2017.

INTERVENTIONS : Not applicable MAIN OUTCOME MEASURE(S): Confirmation of patients' chronic wheelchair use in NLP-flagged notes; quality of disability documentation RESULTS: We identified 14,877 patients with colorectal cancer with 303,182 associated clinical notes. NLP screening identified 1,482 (0.5%) notes that contained 1+ wheelchair-associated keyword. These notes were associated with 420 patients (2.8% of colorectal cancer population). Of the 1,482 notes, 286 (19.3%, representing 105 patients, 0.7% of the total) contained documentation of reason for wheelchair use and duration. Directed content analysis identified three themes concerning disability documentation: (1) wheelchair keywords used in specific EHR contexts; (2) reason for wheelchair not clearly stated; and (3) duration of wheelchair use not consistently documented.

CONCLUSIONS : NLP offers an option to screen for patients with chronic mobility disability in much less time than required by manual chart review. Nonetheless, manual chart review must confirm that flagged patients have chronic mobility disability (are not false positives). Notes, however, often have inadequate disability documentation.

Agaronnik Nicole, Lindvall Charlotta, El-Jawahri Areej, He Wei, Iezzoni Lisa

2020-May-21

electronic health records, functional status, machine learning, medical record documentation, mobility disability, natural language processing

General General

Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text

Second Edition of Emotion Measurement, 2020

Recent advances in machine learning have led to computer systems that are human-like in behaviour. Sentiment analysis, the automatic determination of emotions in text, is allowing us to capitalize on substantial previously unattainable opportunities in commerce, public health, government policy, social sciences, and art. Further, analysis of emotions in text, from news to social media posts, is improving our understanding of not just how people convey emotions through language but also how emotions shape our behaviour. This article presents a sweeping overview of sentiment analysis research that includes: the origins of the field, the rich landscape of tasks, challenges, a survey of the methods and resources used, and applications. We also discuss discuss how, without careful fore-thought, sentiment analysis has the potential for harmful outcomes. We outline the latest lines of research in pursuit of fairness in sentiment analysis.

Saif M. Mohammad

2020-05-25

General General

Stability of RNA sequences derived from the coronavirus genome in human cells.

In Biochemical and biophysical research communications

Most viruses inhibit the innate immune system and/or the RNA degradation processes of host cells to construct an advantageous intracellular environment for their survival. Characteristic RNA sequences within RNA virus genomes or RNAs transcribed from DNA virus genomes contribute toward this inhibition. In this study, we developed a method called "Fate-seq" to comprehensively identify the RNA sequences derived from RNA and DNA viruses, contributing RNA stability in the cells. We examined the stabilization activity of 5,924 RNA fragments derived from 26 different viruses (16 RNA viruses and 10 DNA viruses) using next-generation sequencing of these RNAs fused 3' downstream of GFP reporter RNA. With the Fate-seq approach, we detected multiple virus-derived RNA sequences that stabilized GFP reporter RNA, including sequences derived from severe acute respiratory syndrome-related coronavirus (SARS-CoV). Comparative genomic analysis revealed that these RNA sequences and their predicted secondary structures are highly conserved between SARS-CoV and the novel coronavirus, SARS-CoV-2, which is responsible for the global outbreak of the coronavirus-associated disease that emerged in December 2019 (COVID-19). These sequences have the potential to enhance the stability of viral RNA genomes, thereby augmenting viral replication efficiency and virulence.

Wakida Hiroyasu, Kawata Kentaro, Yamaji Yuta, Hattori Emi, Tsuchiya Takaho, Wada Youichiro, Ozaki Haruka, Akimitsu Nobuyoshi

2020-May-06

COVID-19, Functional sequence, RNA stability, SARS-CoV, SARS-CoV-2, Virus

Radiology Radiology

The efficiency of deep learning algorithms for detecting anatomical reference points on radiological images of the head profile

ArXiv Preprint

In this article we investigate the efficiency of deep learning algorithms in solving the task of detecting anatomical reference points on radiological images of the head in lateral projection using a fully convolutional neural network and a fully convolutional neural network with an extended architecture for biomedical image segmentation - U-Net. A comparison is made for the results of detection anatomical reference points for each of the selected neural network architectures and their comparison with the results obtained when orthodontists detected anatomical reference points. Based on the obtained results, it was concluded that a U-Net neural network allows performing the detection of anatomical reference points more accurately than a fully convolutional neural network. The results of the detection of anatomical reference points by the U-Net neural network are closer to the average results of the detection of reference points by a group of orthodontists.

Konstantin Dobratulin, Andrey Gaidel, Irina Aupova, Anna Ivleva, Aleksandr Kapishnikov, Pavel Zelter

2020-05-25

Radiology Radiology

Predictors of Successful First-Pass Thrombectomy with a Balloon Guide Catheter: Results of a Decision Tree Analysis.

In Translational stroke research ; h5-index 39.0

Complete recanalization after a single retrieval maneuver is an interventional goal in acute ischemic stroke and an independent factor for good clinical outcome. Anatomical biomarkers for predicting clot removal difficulties have not been comprehensively analyzed and await unused. We retrospectively evaluated 200 consecutive patients who suffered acute stroke and occlusion of the anterior circulation and were treated with mechanical thrombectomy through a balloon guide catheter (BGC). The primary objective was to evaluate the influence of carotid tortuosity and BGC positioning on the one-pass Modified Thrombolysis in Cerebral Infarction Scale (mTICI) 3 rate, and secondarily, the influence of communicating arteries on the angiographic results. After the first-pass mTICI 3, recanalization fell from 51 to 13%. The regression models and decision tree (supervised machine learning) results concurred: carotid tortuosity was the main constraint on efficacy, reducing the likelihood of mTICI 3 after one pass to 30%. BGC positioning was relevant only in carotid arteries without elongation: BGCs located in the distal internal carotid artery (ICA) had a 70% probability of complete recanalization after one pass, dropping to 43% if located in the proximal ICA. These findings demonstrate that first-pass mTICI 3 is influenced by anatomical and interventional factors capable of being anticipated, enabling the BGC technique to be adapted to patient's anatomy to enhance effectivity.

Velasco Gonzalez Aglaé, Görlich Dennis, Buerke Boris, Münnich Nico, Sauerland Cristina, Rusche Thilo, Faldum Andreas, Heindel Walter

2020-May-23

Carotid arteries, Circle of Willis, Stroke, Suction, Thrombectomy

General General

Advancing evidence-based healthcare facility design: a systematic literature review.

In Health care management science

Healthcare facility design is a complex process that brings together diverse stakeholders and ideally aligns operational, environmental, experiential, clinical, and organizational objectives. The challenges inherent in facility design arise from the dynamic and complex nature of healthcare itself, and the growing accountability to the quadruple aims of enhancing patient experience, improving population health, reducing costs, and improving staff work life. Many healthcare systems and design practitioners are adopting an evidence-based approach to facility design, defined broadly as basing decisions about the built environment on credible and rigorous research and linking facility design to quality outcomes. Studies focused on architectural options and concepts in the evidence-based design literature have largely employed observation, surveys, post-occupancy study, space syntax analysis, or have been retrospective in nature. Fewer studies have explored layout optimization frameworks, healthcare layout modeling, applications of artificial intelligence, and layout robustness. These operations research/operations management approaches are highly valuable methods to inform healthcare facility design process in its earliest stages and measure performance in quantitative terms, yet they are currently underutilized. A primary objective of this paper is to begin to bridge this gap. This systematic review summarizes 65 evidence-based research studies related to facility layout and planning concepts published from 2008 through 2018, and categorizes them by methodology, area of focus, typology, and metrics of interest. The review identifies gaps in the existing literature and proposes solutions to advance evidence-based healthcare facility design. This work is the first of its kind to review the facility design literature across the disciplines of evidence-based healthcare design research, healthcare systems engineering, and operations research/operations management. The review suggests areas for future study that will enhance evidence-based healthcare facility designs through the integration of operations research and management science methods.

Halawa Farouq, Madathil Sreenath Chalil, Gittler Alice, Khasawneh Mohammad T

2020-May-24

Evidence-based design, Facility layout, Layout optimization, Literature review, Operations research

Radiology Radiology

Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images.

In Diagnostics (Basel, Switzerland)

We aimed to evaluate an artificial intelligence (AI) system that can detect and diagnose lesions of maximum intensity projection (MIP) in dynamic contrast-enhanced (DCE) breast magnetic resonance imaging (MRI). We retrospectively gathered MIPs of DCE breast MRI for training and validation data from 30 and 7 normal individuals, 49 and 20 benign cases, and 135 and 45 malignant cases, respectively. Breast lesions were indicated with a bounding box and labeled as benign or malignant by a radiologist, while the AI system was trained to detect and calculate possibilities of malignancy using RetinaNet. The AI system was analyzed using test sets of 13 normal, 20 benign, and 52 malignant cases. Four human readers also scored these test data with and without the assistance of the AI system for the possibility of a malignancy in each breast. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were 0.926, 0.828, and 0.925 for the AI system; 0.847, 0.841, and 0.884 for human readers without AI; and 0.889, 0.823, and 0.899 for human readers with AI using a cutoff value of 2%, respectively. The AI system showed better diagnostic performance compared to the human readers (p = 0.002), and because of the increased performance of human readers with the assistance of the AI system, the AUC of human readers was significantly higher with than without the AI system (p = 0.039). Our AI system showed a high performance ability in detecting and diagnosing lesions in MIPs of DCE breast MRI and increased the diagnostic performance of human readers.

Adachi Mio, Fujioka Tomoyuki, Mori Mio, Kubota Kazunori, Kikuchi Yuka, Xiaotong Wu, Oyama Jun, Kimura Koichiro, Oda Goshi, Nakagawa Tsuyoshi, Uetake Hiroyuki, Tateishi Ukihide

2020-May-20

artificial intelligence, breast imaging, convolutional neural network, deep learning, magnetic resonance imaging, object detection

General General

Identification of Potential Oral Microbial Biomarkers for the Diagnosis of Periodontitis.

In Journal of clinical medicine

Periodontitis is a chronic and multifactorial inflammatory disease that can lead to tooth loss. At present, the diagnosis for periodontitis is primarily based on clinical examination and radiographic parameters. Detecting the periodontal pathogens at the subgingival plaque requires skilled professionals to collect samples. Periodontal pathogens are also detected on various mucous membranes in patients with periodontitis. In this study, we characterized the oral microbiome profiles from buccal mucosa and supragingival space in a total of 272 healthy subjects as a control group, and periodontitis patients as a disease group. We identified 13 phyla, 193 genera, and 527 species and determined periodontitis-associated taxa. Porphyromonasgingivalis, Tannerellaforsythia, Treponemadenticolar, Filifactoralocis, Porphyromonasendodontalis, Fretibacteriumfastiosum and Peptostreptococcus species were significantly increased in both the buccal mucosa and the supragingival space in periodontitis patients. The identified eight periodontitis-associated bacterial species were clinically validated in an independent cohort. We generated the prediction model based on the oral microbiome profiles using five machine learning algorithms, and validated its capability in predicting the status of patients with periodontitis. The results showed that the oral microbiome profiles from buccal mucosa and supragingival space can represent the microbial composition of subgingival plaque and further be utilized to identify potential microbial biomarkers for the diagnosis of periodontitis. Besides, bacterial community interaction network analysis found distinct patterns associated with dysbiosis in periodontitis. In summary, we have identified oral bacterial species from buccal and supragingival sites which can predict subgingival bacterial composition and can be used for early diagnosis of periodontitis. Therefore, our study provides an important basis for developing easy and noninvasive methods to diagnose and monitor periodontitis.

Na Hee Sam, Kim Si Yeong, Han Hyejung, Kim Hyun-Joo, Lee Ju-Youn, Lee Jae-Hyung, Chung Jin

2020-May-20

Bioinformatics, Biomarkers, Microbiome, Oral bacteria, Periodontal disease(s)/periodontitis

General General

Deep Learning Based Automatic Malaria Parasite Detection from Blood Smear and its Smartphone Based Application.

In Diagnostics (Basel, Switzerland)

Malaria is a life-threatening disease that is spread by the Plasmodium parasites. It is detected by trained microscopists who analyze microscopic blood smear images. Modern deep learning techniques may be used to do this analysis automatically. The need for the trained personnel can be greatly reduced with the development of an automatic accurate and efficient model. In this article, we propose an entirely automated Convolutional Neural Network (CNN) based model for the diagnosis of malaria from the microscopic blood smear images. A variety of techniques including knowledge distillation, data augmentation, Autoencoder, feature extraction by a CNN model and classified by Support Vector Machine (SVM) or K-Nearest Neighbors (KNN) are performed under three training procedures named generaltraining, distillationtraining and autoencodertraining to optimize and improve the model accuracy and inference performance. Our deep learning-based model can detect malarial parasites from microscopic images with an accuracy of 99.23% while requiring just over 4600 floating point operations. For practical validation of model efficiency, we have deployed the miniaturized model in different mobile phones and a server-backed web application. Data gathered from these environments show that the model can be used to perform inference under 1 s per sample in both offline (mobile only) and online (web application) mode, thus engendering confidence that such models may be deployed for efficient practical inferential systems.

Fuhad K M Faizullah, Tuba Jannat Ferdousey, Sarker Md Rabiul Ali, Momen Sifat, Mohammed Nabeel, Rahman Tanzilur

2020-May-20

Autoencoder, CNN, Plasmodium parasites, blood smear, data augmentation, deep learning, floating point operations, inference performance, knowledge distillation, microscopic

General General

Deep Learning-Based Morphological Classification of Human Sperm Heads.

In Diagnostics (Basel, Switzerland)

Human infertility is considered as a serious disease of the reproductive system that affects more than 10% of couples across the globe and over 30% of the reported cases are related to men. The crucial step in the assessment of male infertility and subfertility is semen analysis that strongly depends on the sperm head morphology, i.e., the shape and size of the head of a spermatozoon. However, in medical diagnosis, the morphology of the sperm head is determined manually, and heavily depends on the expertise of the clinician. Moreover, this assessment as well as the morphological classification of human sperm heads are laborious and non-repeatable, and there is also a high degree of inter and intra-laboratory variability in the results. In order to overcome these problems, we propose a specialized convolutional neural network (CNN) architecture to accurately classify human sperm heads based on sperm images. It is carefully designed with several layers, and multiple filter sizes, but fewer filters and parameters to improve efficiency and effectiveness. It is demonstrated that our proposed architecture outperforms state-of-the-art methods, exhibiting 88% recall on the SCIAN dataset in the total agreement setting and 95% recall on the HuSHeM dataset for the classification of human sperm heads. Our proposed method shows the potential of deep learning to surpass embryologists in terms of reliability, throughput, and accuracy.

Iqbal Imran, Mustafa Ghulam, Ma Jinwen

2020-May-20

classification, convolutional neural network (CNN), deep learning, infertility, sperm head morphology

General General

GPS-PBS: A Deep Learning Framework to Predict Phosphorylation Sites that Specifically Interact with Phosphoprotein-Binding Domains.

In Cells

Protein phosphorylation is essential for regulating cellular activities by modifying substrates at specific residues, which frequently interact with proteins containing phosphoprotein-binding domains (PPBDs) to propagate the phosphorylation signaling into downstream pathways. Although massive phosphorylation sites (p-sites) have been reported, most of their interacting PPBDs are unknown. Here, we collected 4458 known PPBD-specific binding p-sites (PBSs), considerably improved our previously developed group-based prediction system (GPS) algorithm, and implemented a deep learning plus transfer learning strategy for model training. Then, we developed a new online service named GPS-PBS, which can hierarchically predict PBSs of 122 single PPBD clusters belonging to two groups and 16 families. By comparison, GPS-PBS achieved a highly competitive accuracy against other existing tools. Using GPS-PBS, we predicted 371,018 mammalian p-sites that potentially interact with at least one PPBD, and revealed that various PPBD-containing proteins (PPCPs) and protein kinases (PKs) can simultaneously regulate the same p-sites to orchestrate important pathways, such as the PI3K-Akt signaling pathway. Taken together, we anticipate GPS-PBS can be a great help for further dissecting phosphorylation signaling networks.

Guo Yaping, Ning Wanshan, Jiang Peiran, Lin Shaofeng, Wang Chenwei, Tan Xiaodan, Yao Lan, Peng Di, Xue Yu

2020-May-20

PPBD-specific binding p-site, deep learning, phosphoprotein-binding domain, phosphorylation site, protein kinase, protein phosphorylation

General General

A Data-Driven Learning Method for Constitutive Modeling: Application to Vascular Hyperelastic Soft Tissues.

In Materials (Basel, Switzerland)

We address the problem of machine learning of constitutive laws when large experimental deviations are present. This is particularly important in soft living tissue modeling, for instance, where large patient-dependent data is found. We focus on two aspects that complicate the problem, namely, the presence of an important dispersion in the experimental results and the need for a rigorous compliance to thermodynamic settings. To address these difficulties, we propose to use, respectively, Topological Data Analysis techniques and a regression over the so-called General Equation for the Nonequilibrium Reversible-Irreversible Coupling (GENERIC) formalism (M. Grmela and H. Ch. Oettinger, Dynamics and thermodynamics of complex fluids. I. Development of a general formalism. Phys. Rev. E 56, 6620, 1997). This allows us, on one hand, to unveil the true "shape" of the data and, on the other, to guarantee the fulfillment of basic principles such as the conservation of energy and the production of entropy as a consequence of viscous dissipation. Examples are provided over pseudo-experimental and experimental data that demonstrate the feasibility of the proposed approach.

González David, García-González Alberto, Chinesta Francisco, Cueto Elías

2020-May-18

GENERIC, computational modeling, hyperelasticity, machine learning, manifold learning, soft living tissues, topological data analysis

General General

3D shape analysis of grass silica short cell phytoliths (GSSCP): a new method for fossil classification and analysis of shape evolution.

In The New phytologist

Fossil grass silica short cell phytoliths (GSSCP) have been used to reconstruct the biogeography of Poaceae, untangle crop domestication history, and detect past vegetation shifts. These inferences depend on accurately identifying the clade to which the fossils belong. Patterns of GSSCP shape and size variation across the family have not been established and current classification methods are subjective or based on a two-dimensional view that ignores important 3D shape variation. Focusing on Poaceae subfamilies Anomochlooideae, Pharoideae, Pueliodieae, Bambusoideae, and Oryzoideae we observed in-situ GSSCP to establish their orientation and imaged isolated GSSCP using confocal microscopy to produce three-dimensional models. 3D geometric morphometrics was used to analyze GSSCP shape and size. Classification models were applied to GSSCP from Eocene sediments from Nebraska, USA and Anatolia, Turkey. There were significant shape differences between nearly all recognized GSSCP morphotypes and between clades with shared morphotypes. Most of the Eocene GSSCP were classified as woody bamboos with some distinctive Nebraska GSSCP classified as herbaceous bamboos. 3D morphometrics hold great promise for GSSCP classification. It accounts for the complete GSSCP shape, automates size measurements, and accommodates the complete range of morphotypes within a single analytical framework.

Gallaher Timothy J, Akbar Sultan Z, Klahs Phillip C, Marvet Claire R, Senske Ashly M, Clark Lynn G, Strömberg Caroline A E

2020-May-23

Microfossils, Poaceae, archaeology, machine learning, paleobotany, paleoecology, phytoliths

General General

Classifying heterogeneous presentations of PTSD via the default mode, central executive, and salience networks with machine learning.

In NeuroImage. Clinical

Intrinsic connectivity networks (ICNs), including the default mode network (DMN), the central executive network (CEN), and the salience network (SN) have been shown to be aberrant in patients with posttraumatic stress disorder (PTSD). The purpose of the current study was to a) compare ICN functional connectivity between PTSD, dissociative subtype PTSD (PTSD+DS) and healthy individuals; and b) to examine the use of multivariate machine learning algorithms in classifying PTSD, PTSD+DS, and healthy individuals based on ICN functional activation. Our neuroimaging dataset consisted of resting-state fMRI scans from 186 participants [PTSD (n = 81); PTSD + DS (n = 49); and healthy controls (n = 56)]. We performed group-level independent component analyses to evaluate functional connectivity differences within each ICN. Multiclass Gaussian Process Classification algorithms within PRoNTo software were then used to predict the diagnosis of PTSD, PTSD+DS, and healthy individuals based on ICN functional activation. When comparing the functional connectivity of ICNs between PTSD, PTSD+DS and healthy controls, we found differential patterns of connectivity to brain regions involved in emotion regulation, in addition to limbic structures and areas involved in self-referential processing, interoception, bodily self-consciousness, and depersonalization/derealization. Machine learning algorithms were able to predict with high accuracy the classification of PTSD, PTSD+DS, and healthy individuals based on ICN functional activation. Our results suggest that alterations within intrinsic connectivity networks may underlie unique psychopathology and symptom presentation among PTSD subtypes. Furthermore, the current findings substantiate the use of machine learning algorithms for classifying subtypes of PTSD illness based on ICNs.

Nicholson Andrew A, Harricharan Sherain, Densmore Maria, Neufeld Richard W J, Ros Tomas, McKinnon Margaret C, Frewen Paul A, Théberge Jean, Jetly Rakesh, Pedlar David, Lanius Ruth A

2020-Apr-22

oncology Oncology

Staging and grading of oral squamous cell carcinoma: An update.

In Oral oncology

Oral squamous cell carcinoma (OSCC) is a common malignancy of the head and neck region. OSCC has a relatively low survival rate and the incidence of the disease is increasing in some geographic areas. Staging and grading of OSCC are established prerequisites for management, as they influence risk stratification and are the first step toward personalized treatment. The current AJCC/UICC TNM staging (8th edition, 2017) of OSCC has included significant modifications through the incorporation of depth of invasion in the T stage and extracapsular spread/extranodal extension in the N stage. Further modifications for AJCC 8 have been suggested. On the other hand, the World Health Organization (WHO) classification (4th edition, 2017) still endorses a simple, differentiation-based histopathologic grading system of OSCC (despite its low prognostic value) and ignores factors such as tumor growth pattern and dissociation, stromal reactions (desmoplasia, local immune response), and tumor-stroma ratio. The various controversies and possible developments of the current staging and grading criteria of OSCC are briefly discussed in this update together with possible applications of artificial intelligence in the context of screening and risk stratification.

Almangush Alhadi, Mäkitie Antti A, Triantafyllou Asterios, de Bree Remco, Strojan Primož, Rinaldo Alessandra, Hernandez-Prera Juan C, Suárez Carlos, Kowalski Luiz P, Ferlito Alfio, Leivo Ilmo

2020-May-20

Grading, Oral squamous cell carcinoma (OSCC), Prognosis, Staging

General General

Impulsive synchronization of coupled delayed neural networks with actuator saturation and its application to image encryption.

In Neural networks : the official journal of the International Neural Network Society

The actuator of any physical control systems is constrained by amplitude and energy, which causes the control systems to be inevitably affected by actuator saturation. In this paper, impulsive synchronization of coupled delayed neural networks with actuator saturation is presented. A new controller is designed to introduce actuator saturation term into impulsive controller. Based on sector nonlinearity model approach, impulsive controls with actuator saturation and with partial actuator saturation are studied, respectively, and some effective sufficient conditions are obtained. Numerical simulation is presented to verify the validity of the theoretical analysis results. Finally, the impulsive synchronization is applied to image encryption. The experimental results show that the proposed image encryption system has high security properties.

Ouyang Deqiang, Shao Jie, Jiang Haijun, Nguang Sing Kiong, Shen Heng Tao

2020-May-15

Actuator saturation, Coupled neural networks, Exponential synchronization, Image encryption, Impulsive control, Time-varying delays

General General

Training memristor-based multilayer neuromorphic networks with SGD, momentum and adaptive learning rates.

In Neural networks : the official journal of the International Neural Network Society

Neural networks implemented with traditional hardware face inherent limitation of memory latency. Specifically, the processing units like GPUs, FPGAs, and customized ASICs, must wait for inputs to read from memory and outputs to write back. This motivates memristor-based neuromorphic computing in which the memory units (i.e., memristors) have computing capabilities. However, training a memristor-based neural network is difficult since memristors work differently from CMOS hardware. This paper proposes a new training approach that enables prevailing neural network training techniques to be applied for memristor-based neuromorphic networks. Particularly, we introduce momentum and adaptive learning rate to the circuit training, both of which are proven methods that significantly accelerate the convergence of neural network parameters. Furthermore, we show that this circuit can be used for neural networks with arbitrary numbers of layers, neurons, and parameters. Simulation results on four classification tasks demonstrate that the proposed circuit achieves both high accuracy and fast speed. Compared with the SGD-based training circuit, on the WBC data set, the training speed of our circuit is increased by 37.2% while the accuracy is only reduced by 0.77%. On the MNIST data set, the new circuit even leads to improved accuracy.

Yan Zheng, Chen Jiadong, Hu Rui, Huang Tingwen, Chen Yiran, Wen Shiping

2020-May-07

Adaptive learning rate, Memristor, Neural network

General General

Multi-projection of unequal dimension optimal transport theory for Generative Adversary Networks.

In Neural networks : the official journal of the International Neural Network Society

As a major step forward in machine learning, generative adversarial networks (GANs) employ the Wasserstein distance as a metric between the generative distribution and target data distribution, and thus can be viewed as optimal transport (OT) problems to reflect the underlying geometry of the probability distribution. However, the unequal dimensions between the source random distribution and the target data, result in often instability in the training processes, and lack of diversity in the generative images. To resolve the challenges, we propose here a multiple-projection approach, to project the source and target probability measures into multiple different low-dimensional subspaces. Moreover, we show that the original problem can be transformed into a variant multi-marginal OT problem, and we provide the explicit properties of the solutions. In addition, we employ parameterized approximation for the objective, and study the corresponding differentiability and convergence properties, ensuring that the problem can indeed be computed.

Lin Judy Yangjun, Guo Shaoyan, Xie Longhan, Xu Gu

2020-May-04

Generative adversarial networks, Multi-projection, Optimal transport, Unequal dimension

General General

The emerging roles of artificial intelligence in cancer drug development and precision therapy.

In Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie

Artificial intelligence (AI) has strong logical reasoning ability and independent learning ability, which can simulate the thinking process of the human brain. AI technologies such as machine learning can profoundly optimize the existing mode of anticancer drug research. But at present AI also has its relative limitation. In this paper, the development of artificial intelligence technology such as deep learning and machine learning in anticancer drug research is reviewed. At the same time, we look forward to the future of AI.

Liang Guosheng, Fan Wenguo, Luo Hui, Zhu Xiao

2020-May-20

Anticancer therapy, Artificial intelligence, Drug development, Precision therapy

General General

Feature Importance of Stabilised Rammed Earth Components Affecting the Compressive Strength Calculated with Explainable Artificial Intelligence Tools.

In Materials (Basel, Switzerland)

Cement-stabilized rammed earth (CSRE) is a sustainable construction material. The use of it allows for economizing on the cost of a structure. These two properties of CSRE are based on the fact that the soil used for the rammed mixture is usually dug close to the construction site, so it has random characteristics. That is the reason for the lack of widely accepted prescriptions for CSRE mixture, which could ascertain high enough compressive strength. Therefore, assessing which components of CSRE have the highest impact on its compressive strength becomes an important issue. There are three machine learning regression tools, i.e., artificial neural networks, decision tree, and random forest, used for predicting the compressive strength based on the relative content of CSRE composites (clay, silt, sand, gravel, cement, and water content). The database consisted of 434 samples of CSRE, which were prepared and crushed for testing purposes. Relatively low prediction errors of aforementioned models allowed for the use of explainable artificial intelligence tools (drop-out loss, mean squared error reduction, accumulated local effect) to rank the influence of the ingredients on the dependent variable-the compressive strength. Consistent results from all above-mentioned methods are discussed and compared to some statistical analysis of selected features. This innovative approach, helpful in designing the construction material is a solid base for reliable conclusions.

Anysz Hubert, Brzozowski Łukasz, Kretowicz Wojciech, Narloch Piotr

2020-May-18

artificial inteligence, cement stabilized rammed earth, features importance ranking, multivariate regression, rammed earth, random forest

General General

BBMRI-ERIC's contributions to research and knowledge exchange on COVID-19.

In European journal of human genetics : EJHG

During the COVID-19 pandemic, the European biobanking infrastructure is in a unique position to preserve valuable biological material complemented with detailed data for future research purposes. Biobanks can be either integrated into healthcare, where preservation of the biological material is a fork in clinical routine diagnostics and medical treatment processes or they can also host prospective cohorts or material related to clinical trials. The paper discussed objectives of BBMRI-ERIC, the European research infrastructure established to facilitate access to quality-defined biological materials and data for research purposes, with respect to the COVID-19 crisis: (a) to collect information on available European as well as non-European COVID-19-relevant biobanking resources in BBMRI-ERIC Directory and to facilitate access to these via BBMRI-ERIC Negotiator platform; (b) to help harmonizing guidelines on how data and biological material is to be collected to maximize utility for future research, including large-scale data processing in artificial intelligence, by participating in activities such as COVID-19 Host Genetics Initiative; (c) to minimize risks for all involved parties dealing with (potentially) infectious material by developing recommendations and guidelines; (d) to provide a European-wide platform of exchange in relation to ethical, legal, and societal issues (ELSI) specific to the collection of biological material and data during the COVID-19 pandemic.

Holub Petr, Kozera Lukasz, Florindi Francesco, van Enckevort Esther, Swertz Morris, Reihs Robert, Wutte Andrea, Valík Dalibor, Mayrhofer Michaela Th

2020-May-22

Surgery Surgery

A Predictive-Modeling Based Screening Tool for Prolonged Opioid Use after Surgical Management of Low Back and Lower Extremity Pain.

In The spine journal : official journal of the North American Spine Society

BACKGROUND CONTEXT : Outpatient postoperative pain management in spine patients, specifically involving the use of opioids, demonstrates significant variability.

PURPOSE : Using preoperative risk factors and 30-day postoperative opioid prescribing patterns, we developed models for predicting long-term opioid use in patients after elective spine surgery.

STUDY DESIGN/SETTING : This retrospective cohort study utilizes inpatient, outpatient, and pharmaceutical data from MarketScan databases (Truven Health).

PATIENT SAMPLE : In all, 19,317 patients who were newly diagnosed with low back or lower extremity pain (LBP or LEP) between 2008 and 2015 and underwent thoracic or lumbar surgery within one year after diagnosis were enrolled. Some patients initiated opioids after diagnosis but all patients were opioid-naïve prior to the diagnosis.

OUTCOME MEASURES : Long-term opioid use was defined as filling ≥180 days of opioids within one year after surgery.

METHODS : Using demographic variables, medical and psychiatric comorbidities, preoperative opioid use, and 30-day postoperative opioid use, we generated seven models on 80% of the dataset and tested the models on the remaining 20%. We used three regression-based models (full logistic regression, stepwise logistic regression, least absolute shrinkage and selection operator [LASSO]), support vector machine, two tree-based models (random forest, stochastic gradient boosting), and time-varying convolutional neural network. Area under the curve (AUC), Brier index, sensitivity, and calibration curves were used to assess the discrimination and calibration of the models.

RESULTS : We identified 903 (4.6%) of patients who met criteria for long-term opioid use. The regression-based models demonstrated the highest AUC, ranging from 0.835 to 0.847, and relatively high sensitivities, predicting between 74.9-76.5% of the long-term opioid use patients in the test dataset. The three strongest positive predictors of long-term opioid use were high preoperative opioid use (OR 2.70; 95% CI 2.27-3.22), number of days with active opioid prescription between postoperative days 15-30 (OR 1.10; 95% CI 1.07-1.12), and number of dosage increases between postoperative day 15-30 (OR 1.71, 95% CI 1.41-2.08). The strongest negative predictors were number of dosage decreases in the 30-day postoperative period.

CONCLUSIONS : We evaluated several predictive models for postoperative long-term opioid use in a large cohort of patients with LBP or LEP who underwent surgery. A regression-based model with high sensitivity and AUC is provided online to screen patients for high risk of long-term opioid use based on preoperative risk factors and opioid prescription patterns in the first 30 days after surgery. It is hoped that this work will improve identification of patients at high risk of prolonged opioid use and enable early intervention and counseling.

Zhang Yi, Fatemi Parastou, Medress Zachary, Azad Tej D, Veeravagu Anand, Desai Atman, Ratliff John K

2020-May-20

machine learning, postoperative pain, predictive model, prolonged opioid use, screening tool, spine surgery

Radiology Radiology

Multi-instance deep learning of ultrasound imaging data for pattern classification of congenital abnormalities of the kidney and urinary tract in children.

In Urology ; h5-index 45.0

OBJECTIVE : To reliably and quickly diagnose children with posterior urethral valves (PUV), we developed a multi-instance deep learning method to automate image analysis.

METHODS : We built a robust pattern classifier to distinguish 86 children with PUV from 71 children with mild unilateral hydronephrosis based on ultrasound images (3504 in sagittal view and 2558 in transverse view) obtained during routine clinical care.

RESULTS : The multi-instance deep learning classifier performed better than classifiers built on either single sagittal images or single transverse images. Particularly, the deep learning classifiers built on single images in the sagittal view and single images in the transverse view obtained area under the receiver operating characteristic curve (AUC) values of 0.796±0.064 and 0.815±0.071, respectively. AUC values of the multi-instance deep learning classifiers built on images in the sagittal and transverse views with mean pooling operation were 0.949±0.035 and 0.954±0.033, respectively. The multi-instance deep learning classifiers built on images in both the sagittal and transverse views with a mean pooling operation obtained an AUC of 0.961±0.026 with a classification rate of 0.925±0.060, specificity of 0.986±0.032, and sensitivity of 0.873±0.120, respectively. Discriminative regions of the kidney located using classification activation map demonstrated that the deep learning techniques could identify meaningful anatomical features from ultrasound images.

CONCLUSION : The multi-instance deep learning method provides an automatic and accurate means to extract informative features from ultrasound images and discriminate infants with PUV from male children with unilateral hydronephrosis.

Yin Shi, Peng Qinmu, Li Hongming, Zhang Zhengqiang, You Xinge, Fischer Katherine, Furth Susan L, Fan Yong, Tasian Gregory E

2020-May-20

Multi-instance deep learning, kidney, posterior urethral valves, ultrasound images

General General

Foundational Challenges for Advancing the Field and Discipline of Risk Analysis.

In Risk analysis : an official publication of the Society for Risk Analysis

Risk analysis as a field and discipline is about concepts, principles, approaches, methods, and models for understanding, assessing, communicating, managing, and governing risk. The foundation of this field and discipline has been subject to continuous discussion since its origin some 40 years ago with the establishment of the Society for Risk Analysis and the Risk Analysis journal. This article provides a perspective on critical foundational challenges that this field and discipline faces today, for risk analysis to develop and have societal impact. Topics discussed include fundamental questions important for defining the risk field, discipline, and science; the multidisciplinary and interdisciplinary features of risk analysis; the interactions and dependencies with other sciences; terminology and fundamental principles; and current developments and trends, such as the use of artificial intelligence.

Aven Terje, Flage Roger

2020-May-23

Risk field, risk analysis foundation, risk science

Public Health Public Health

Development of a machine learning-based multimode diagnosis system for lung cancer.

In Aging ; h5-index 49.0

As an emerging technology, artificial intelligence has been applied to identify various physical disorders. Here, we developed a three-layer diagnosis system for lung cancer, in which three machine learning approaches including decision tree C5.0, artificial neural network (ANN) and support vector machine (SVM) were involved. The area under the curve (AUC) was employed to evaluate their decision powers. In the first layer, the AUCs of C5.0, ANN and SVM were 0.676, 0.736 and 0.640, ANN was better than C5.0 and SVM. In the second layer, ANN was similar with SVM but superior to C5.0 supported by the AUCs of 0.804, 0.889 and 0.825. Much higher AUCs of 0.908, 0.910 and 0.849 were identified in the third layer, where the highest sensitivity of 94.12% was found in C5.0. These data proposed a three-layer diagnosis system for lung cancer: ANN was used as a broad-spectrum screening subsystem basing on 14 epidemiological data and clinical symptoms, which was firstly adopted to screen high-risk groups; then, combining with additional 5 tumor biomarkers, ANN was used as an auxiliary diagnosis subsystem to determine the suspected lung cancer patients; C5.0 was finally employed to confirm lung cancer patients basing on 22 CT nodule-based radiomic features.

Duan Shuyin, Cao Huimin, Liu Hong, Miao Lijun, Wang Jing, Zhou Xiaolei, Wang Wei, Hu Pingzhao, Qu Lingbo, Wu Yongjun

2020-May-23

lung cancer, machine learning, multidimensional variables, multimode diagnosis

General General

Deep Learning Approach for Imputation of Missing Values in Actigraphy Data: Algorithms Development Study.

In JMIR mHealth and uHealth

BACKGROUND : Data collected by an accelerometer device worn on the wrist or waist can provide objective measurements for studies related to physical activity. However, some portion of the data cannot be used because of missing values. In previous studies, statistical methods have been applied to impute missing values on the basis of statistical assumptions. Deep learning algorithms, however, can learn features from the data themselves without any assumptions and may outperform previous approaches in imputation tasks.

OBJECTIVE : The aim of this study was to impute missing values in accelerometer data using a deep learning approach that performs better than conventional approaches.

METHODS : To develop an imputation model for missing values in accelerometer data, a denoising convolutional autoencoder was adopted. We trained and tested our deep learning-based imputation model with the National Health and Nutrition Examination Survey (NHANES) dataset and validated it with the external Korea National Health and Nutrition Examination Survey (KNHANES) and the Korean Chronic Cerebrovascular Disease Oriented Biobank (KCCDB) datasets. The partial root mean squared error (PRMSE) and partial mean absolute error (PMAE) of the imputed parts were used for a performance comparison with previous approaches (mean imputation, zero-inflated Poisson [ZIP] regression, and Bayesian regression).

RESULTS : Our model exhibited a PRMSE of 839.3 counts per minute (cpm) and PMAE of 431.1 cpm, whereas mean imputation showed a PRMSE of 1,053.2 cpm and PMAE of 545.4 cpm, the ZIP model achieved a PRMSE of 1,255.6 cpm and PMAE of 508.6 cpm, and Bayesian regression showed a PRMSE of 924.5 cpm and PMAE of 605.8 cpm.

CONCLUSIONS : In this study, the proposed deep learning model for imputing missing values in accelerometer activity data performed better than the other methods.

CLINICALTRIAL :

Jang Jong-Hwan, Choi Junggu, Roh Hyun Woong, Son Sang Joon, Hong Chang Hyung, Kim Eun Young, Kim Tae Young, Yoon Dukyong

2020-May-18

General General

Improving blood glucose level predictability using machine learning.

In Diabetes/metabolism research and reviews

AIMS : This study was designed to improve blood glucose (BG) level predictability and future hypoglycemic and hyperglycemic event alerts through a novel patient-specific supervised-machine-learning (SML) analysis of glucose level based on a continuous-glucose-monitoring system (CGM) that needs no human intervention, and minimizes false-positive alerts.

MATERIALS AND METHODS : The CGM data over 7-50 nonconsecutive days from 11 type-1 diabetic patients aged 18-39 with a mean HbA1C of 7.5 ± 1.2% were analyzed using four SML models. The algorithm was constructed to choose the best-fit model for each patient. Several statistical parameters were calculated to aggregate the magnitudes of the prediction errors.

RESULTS : The personalized solutions provided by the algorithm were effective in predicting glucose levels 30 minutes after the last measurement. The average root-mean-square-error was 20.48 mg/dL and the average absolute-mean-error was 15.36 mg/dL when the best-fit-model was selected for each patient. Using the best-fit-model, the true-positive-hypoglycemia-prediction-rate was 64%, whereas the false-positive- rate was 4.0%, and the false-negative-rate was 0.015%. Similar results were found even when only CGM samples below 70 were considered. The true-positive-hyperglycemia-prediction-rate was 61%.

CONCLUSIONS : State-of-the-art SML tools are effective in predicting the glucose level values of patients with type-1diabetes and notifying these patients of future hypoglycemic and hyperglycemic events, thus improving glycemic control. The algorithm can be used to improve the calculation of the basal insulin rate and bolus insulin, and suitable for a closed loop "artificial pancreas" system. The algorithm provides a personalized medical solution that can successfully identify the best-fit method for each patient. This article is protected by copyright. All rights reserved.

Marcus Yonit, Eldor Roy, Yaron Mariana, Shaklai Sigal, Ish-Shalom Maya, Shefer Gabi, Stern Naftali, Golan Nehor, Dvir Amit Zeev, Pele Ofir, Gonen Mira

2020-May-23

Type-1 diabetes, blood glucose predictability, supervised machine learning

Surgery Surgery

Image-Based Cell Profiling Enables Quantitative Tissue Microscopy in Gastroenterology.

In Cytometry. Part A : the journal of the International Society for Analytical Cytology

Immunofluorescence microscopy is an essential tool for tissue-based research, yet data reporting is almost always qualitative. Quantification of images, at the per-cell level, enables "flow cytometry-type" analyses with intact locational data but achieving this is complex. Gastrointestinal tissue, for example, is highly diverse: from mixed-cell epithelial layers through to discrete lymphoid patches. Moreover, different species (e.g., rat, mouse, and humans) and tissue preparations (paraffin/frozen) are all commonly studied. Here, using field-relevant examples, we develop open, user-friendly methodology that can encompass these variables to provide quantitative tissue microscopy for the field. Antibody-independent cell labeling approaches, compatible across preparation types and species, were optimized. Per-cell data were extracted from routine confocal micrographs, with semantic machine learning employed to tackle densely packed lymphoid tissues. Data analysis was achieved by flow cytometry-type analyses alongside visualization and statistical definition of cell locations, interactions and established microenvironments. First, quantification of Escherichia coli passage into human small bowel tissue, following Ussing chamber incubations exemplified objective quantification of rare events in the context of lumen-tissue crosstalk. Second, in rat jejenum, precise histological context revealed distinct populations of intraepithelial lymphocytes between and directly below enterocytes enabling quantification in context of total epithelial cell numbers. Finally, mouse mononuclear phagocyte-T cell interactions, cell expression and significant spatial cell congregations were mapped to shed light on cell-cell communication in lymphoid Peyer's patch. Accessible, quantitative tissue microscopy provides a new window-of-insight to diverse questions in gastroenterology. It can also help combat some of the data reproducibility crisis associated with antibody technologies and over-reliance on qualitative microscopy. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Wills John W, Robertson Jack, Summers Huw D, Miniter Michelle, Barnes Claire, Hewitt Rachel E, Keita Åsa V, Söderholm Johan D, Rees Paul, Powell Jonathan J

2020-May-23

**cell segmentation, confocal microscopy, immunofluorescence, intestinal tissue, machine learning, processing tilescans in CellProfiler Getis-Ord spatial statistics**

General General

Artificial intelligence models versus empirical equations for modeling monthly reference evapotranspiration.

In Environmental science and pollution research international

Accurate estimation of reference evapotranspiration (ETo) is profoundly crucial in crop modeling, sustainable management, hydrological water simulation, and irrigation scheduling, since it accounts for more than two-thirds of global precipitation losses. Therefore, ETo-based estimation is a major concern in the hydrological cycle. The estimation of ETo can be determined using various methods, including field measurement (the scale of the lysimeter), experimental methods, and mathematical equations. The Food and Agriculture Organization recommended the Penman-Monteith (FAO-56 PM) method which was identified as the standard method of ETo estimation. However, this equation requires a large number of measured climatic data (maximum and minimum air temperature, relative humidity, solar radiation, and wind speed) that are not always available on meteorological stations. Over the decade, the artificial intelligence (AI) models have received more attention for estimating ETo on multi-time scales. This research explores the potential of new hybrid AI model, i.e., support vector regression (SVR) integrated with grey wolf optimizer (SVR-GWO) for estimating monthly ETo at Algiers, Tlemcen, and Annaba stations located in the north of Algeria. Five climatic variables namely relative humidity (RH), maximum and minimum air temperatures (Tmax and Tmin), solar radiation (Rs), and wind speed (Us) were used for model construction and evaluation. The proposed hybrid SVR-GWO model was compared against hybrid SVR-genetic algorithm (SVR-GA), SVR-particle swarm optimizer (SVR-PSO), conventional artificial neural network (ANN), and empirical (Turc, Ritchie, Thornthwaite, and three versions of Valiantzas methods) models by using root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), Pearson correlation coefficient (PCC), and Willmott index (WI), and through graphical interpretation. Through the results obtained, the performance of the SVR-GWO provides very promising and occasionally competitive results compared to other data-driven and empirical methods at study stations. Thus, the proposed SVR-GWO model with five climatic input variables outperformed the other models (RMSE = 0.0776/0.0613/0.0374 mm, NSE = 0.9953/ 0.9990/0.9995, PCC = 0.9978/0.9995/0.9998 and WI = 0.9988/0.9997/0.9999) for estimating ETo at Algiers, Tlemcen, and Annaba stations, respectively. In conclusion, the results of this research indicate the suitability of the proposed hybrid artificial intelligence model (SVR-GWO) at the study stations. Besides, promising results encourage researchers to transfer and test these models in other locations in the world in future works.

Tikhamarine Yazid, Malik Anurag, Souag-Gamane Doudja, Kisi Ozgur

2020-May-23

Algeria, Empirical methods, Hybrid AI models, Metaheuristic algorithms, Reference evapotranspiration

Surgery Surgery

The effects of different levels of realism on the training of CNNs with only synthetic images for the semantic segmentation of robotic instruments in a head phantom.

In International journal of computer assisted radiology and surgery

PURPOSE : The manual generation of training data for the semantic segmentation of medical images using deep neural networks is a time-consuming and error-prone task. In this paper, we investigate the effect of different levels of realism on the training of deep neural networks for semantic segmentation of robotic instruments. An interactive virtual-reality environment was developed to generate synthetic images for robot-aided endoscopic surgery. In contrast with earlier works, we use physically based rendering for increased realism.

METHODS : Using a virtual reality simulator that replicates our robotic setup, three synthetic image databases with an increasing level of realism were generated: flat, basic, and realistic (using the physically-based rendering). Each of those databases was used to train 20 instances of a UNet-based semantic-segmentation deep-learning model. The networks trained with only synthetic images were evaluated on the segmentation of 160 endoscopic images of a phantom. The networks were compared using the Dwass-Steel-Critchlow-Fligner nonparametric test.

RESULTS : Our results show that the levels of realism increased the mean intersection-over-union (mIoU) of the networks on endoscopic images of a phantom ([Formula: see text]). The median mIoU values were 0.235 for the flat dataset, 0.458 for the basic, and 0.729 for the realistic. All the networks trained with synthetic images outperformed naive classifiers. Moreover, in an ablation study, we show that the mIoU of physically based rendering is superior to texture mapping ([Formula: see text]) of the instrument (0.606), the background (0.685), and the background and instruments combined (0.672).

CONCLUSIONS : Using physical-based rendering to generate synthetic images is an effective approach to improve the training of neural networks for the semantic segmentation of surgical instruments in endoscopic images. Our results show that this strategy can be an essential step in the broad applicability of deep neural networks in semantic segmentation tasks and help bridge the domain gap in machine learning.

Heredia Perez Saul Alexis, Marques Marinho Murilo, Harada Kanako, Mitsuishi Mamoru

2020-May-22

Deep learning, Photorealistic rendering, Semantic segmentation

Surgery Surgery

Spatio-temporal deep learning methods for motion estimation using 4D OCT image data.

In International journal of computer assisted radiology and surgery

PURPOSE : Localizing structures and estimating the motion of a specific target region are common problems for navigation during surgical interventions. Optical coherence tomography (OCT) is an imaging modality with a high spatial and temporal resolution that has been used for intraoperative imaging and also for motion estimation, for example, in the context of ophthalmic surgery or cochleostomy. Recently, motion estimation between a template and a moving OCT image has been studied with deep learning methods to overcome the shortcomings of conventional, feature-based methods.

METHODS : We investigate whether using a temporal stream of OCT image volumes can improve deep learning-based motion estimation performance. For this purpose, we design and evaluate several 3D and 4D deep learning methods and we propose a new deep learning approach. Also, we propose a temporal regularization strategy at the model output.

RESULTS : Using a tissue dataset without additional markers, our deep learning methods using 4D data outperform previous approaches. The best performing 4D architecture achieves an correlation coefficient (aCC) of 98.58% compared to 85.0% of a previous 3D deep learning method. Also, our temporal regularization strategy at the output further improves 4D model performance to an aCC of 99.06%. In particular, our 4D method works well for larger motion and is robust toward image rotations and motion distortions.

CONCLUSIONS : We propose 4D spatio-temporal deep learning for OCT-based motion estimation. On a tissue dataset, we find that using 4D information for the model input improves performance while maintaining reasonable inference times. Our regularization strategy demonstrates that additional temporal information is also beneficial at the model output.

Bengs Marcel, Gessert Nils, Schlüter Matthias, Schlaefer Alexander

2020-May-22

4D deep learning, Motion estimation, Optical coherence tomography, Regularization

Radiology Radiology

Intensive Care Risk Estimation in COVID-19 Pneumonia Based on Clinical and Imaging Parameters: Experiences from the Munich Cohort.

In Journal of clinical medicine

The evolving dynamics of coronavirus disease 2019 (COVID-19) and the increasing infection numbers require diagnostic tools to identify patients at high risk for a severe disease course. Here we evaluate clinical and imaging parameters for estimating the need of intensive care unit (ICU) treatment. We collected clinical, laboratory and imaging data from 65 patients with confirmed COVID-19 infection based on polymerase chain reaction (PCR) testing. Two radiologists evaluated the severity of findings in computed tomography (CT) images on a scale from 1 (no characteristic signs of COVID-19) to 5 (confluent ground glass opacities in over 50% of the lung parenchyma). The volume of affected lung was quantified using commercially available software. Machine learning modelling was performed to estimate the risk for ICU treatment. Patients with a severe course of COVID-19 had significantly increased interleukin (IL)-6, C-reactive protein (CRP), and leukocyte counts and significantly decreased lymphocyte counts. The radiological severity grading was significantly increased in ICU patients. Multivariate random forest modelling showed a mean ± standard deviation sensitivity, specificity and accuracy of 0.72 ± 0.1, 0.86 ± 0.16 and 0.80 ± 0.1 and a receiver operating characteristic-area under curve (ROC-AUC) of 0.79 ± 0.1. The need for ICU treatment is independently associated with affected lung volume, radiological severity score, CRP, and IL-6.

Burian Egon, Jungmann Friederike, Kaissis Georgios A, Lohöfer Fabian K, Spinner Christoph D, Lahmer Tobias, Treiber Matthias, Dommasch Michael, Schneider Gerhard, Geisler Fabian, Huber Wolfgang, Protzer Ulrike, Schmid Roland M, Schwaiger Markus, Makowski Marcus R, Braren Rickmer F

2020-May-18

COVID-19, clinical parameters, computed tomography, intensive care unit, radiological parameters, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)

Radiology Radiology

A Fully Automatic Deep Learning System for COVID-19 Diagnostic and Prognostic Analysis.

In The European respiratory journal

Coronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19, and finding high-risk patients with worse prognosis for early prevention and medical resources optimisation is important. Here, we proposed a fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis by routinely used computed tomography.We retrospectively collected 5372 patients with computed tomography images from 7 cities or provinces. Firstly, 4106 patients with computed tomography images were used to pre-train the DL system, making it learn lung features. Afterwards, 1266 patients (924 with COVID-19, and 471 had follow-up for 5+ days; 342 with other pneumonia) from 6 cities or provinces were enrolled to train and externally validate the performance of the deep learning system.In the 4 external validation sets, the deep learning system achieved good performance in identifying COVID-19 from other pneumonia (AUC=0.87 and 0.88) and viral pneumonia (AUC=0.86). Moreover, the deep learning system succeeded to stratify patients into high-risk and low-risk groups whose hospital-stay time have significant difference (p=0.013 and 0.014). Without human-assistance, the deep learning system automatically focused on abnormal areas that showed consistent characteristics with reported radiological findings.Deep learning provides a convenient tool for fast screening COVID-19 and finding potential high-risk patients, which may be helpful for medical resource optimisation and early prevention before patients show severe symptoms.

Wang Shuo, Zha Yunfei, Li Weimin, Wu Qingxia, Li Xiaohu, Niu Meng, Wang Meiyun, Qiu Xiaoming, Li Hongjun, Yu He, Gong Wei, Bai Yan, Li Li, Zhu Yongbei, Wang Liusu, Tian Jie

2020-May-22

General General

WGAN domain adaptation for the joint optic disc-and-cup segmentation in fundus images.

In International journal of computer assisted radiology and surgery

PURPOSE : The cup-to-disc ratio (CDR), a clinical metric of the relative size of the optic cup to the optic disc, is a key indicator of glaucoma, a chronic eye disease leading to loss of vision. CDR can be measured from fundus images through the segmentation of optic disc and optic cup . Deep convolutional networks have been proposed to achieve biomedical image segmentation with less time and more accuracy, but requires large amounts of annotated training data on a target domain, which is often unavailable. Unsupervised domain adaptation framework alleviates this problem through leveraging off-the-shelf labeled data from its relevant source domains, which is realized by learning domain invariant features and improving the generalization capabilities of the segmentation model.

METHODS : In this paper, we propose a WGAN domain adaptation framework for detecting optic disc-and-cup boundary in fundus images. Specifically, we build a novel adversarial domain adaptation framework that is guided by Wasserstein distance, therefore with better stability and convergence than typical adversarial methods. We finally evaluate our approach on publicly available datasets.

RESULTS : Our experiments show that the proposed approach improves Intersection-over-Union score for optic disc-and-cup segmentation, Dice score and reduces the root-mean-square error of cup-to-disc ratio, when we compare it with direct transfer learning and other state-of-the-art adversarial domain adaptation methods.

CONCLUSION : With this work, we demonstrate that WGAN guided domain adaptation obtains a state-of-the-art performance for the joint optic disc-and-cup segmentation in fundus images.

Kadambi Shreya, Wang Zeya, Xing Eric

2020-May-22

Deep learning, Domain adaptation, Optic disc-and-cup boundary

General General

MaskMitosis: a deep learning framework for fully supervised, weakly supervised, and unsupervised mitosis detection in histopathology images.

In Medical & biological engineering & computing ; h5-index 32.0

Counting the mitotic cells in histopathological cancerous tissue areas is the most relevant indicator of tumor grade in aggressive breast cancer diagnosis. In this paper, we propose a robust and accurate technique for the automatic detection of mitoses from histological breast cancer slides using the multi-task deep learning framework for object detection and instance segmentation Mask RCNN. Our mitosis detection and instance segmentation framework is deployed for two main tasks: it is used as a detection network to perform mitosis localization and classification in the fully annotated mitosis datasets (i.e., the pixel-level annotated datasets), and it is used as a segmentation network to estimate the mitosis mask labels for the weakly annotated mitosis datasets (i.e., the datasets with centroid-pixel labels only). We evaluate our approach on the fully annotated 2012 ICPR grand challenge dataset and the weakly annotated 2014 ICPR MITOS-ATYPIA challenge dataset. Our evaluation experiments show that we can obtain the highest F-score of 0.863 on the 2012 ICPR dataset by applying the mitosis detection and instance segmentation model trained on the pixel-level labels provided by this dataset. For the weakly annotated 2014 ICPR dataset, we first employ the mitosis detection and instance segmentation model trained on the fully annotated 2012 ICPR dataset to segment the centroid-pixel annotated mitosis ground truths, and produce the mitosis mask and bounding box labels. These estimated labels are then used to train another mitosis detection and instance segmentation model for mitosis detection on the 2014 ICPR dataset. By adopting this two-stage framework, our method outperforms all state-of-the-art mitosis detection approaches on the 2014 ICPR dataset by achieving an F-score of 0.475. Moreover, we show that the proposed framework can also perform unsupervised mitosis detection through the estimation of pseudo labels for an unlabeled dataset and it can achieve promising detection results. Code has been made available at: https://github.com/MeriemSebai/MaskMitosis. Graphical Abstract Overview of MaskMitosis framework.

Sebai Meriem, Wang Xinggang, Wang Tianjiang

2020-May-22

Automatic mitosis detection, Breast cancer histopathological images, Mask RCNN, Mitosis instance segmentation, Multi-task learning

General General

Feasibility of use of medical dual energy scanner for forensic detection and characterization of explosives, a phantom study.

In International journal of legal medicine

OBJECTIVE : Detection of explosives is a challenge due to the use of improvised and concealed bombs. Post-bomb strike bodies are handled by emergency and forensic teams. We aimed to determine whether medical dual-energy computed tomography (DECT) algorithm and prediction model can readily detect and distinguish a range of explosives on the human body during disaster victim identification (DVI) processes of bombings.

MATERIALS AND METHODS : A medical DECT of 8 explosives (Semtex, Pastex, Hexamethylene triperoxide diamine, Acetone peroxide, Nitrocellulose, Pentrite, Ammonium Nitrate, and classified explosive) was conducted ex-vivo and on an anthropomorphic phantom. Hounsfield unit (HU), electron density (ED), effective atomic number (Zeff), and dual energy index (DEI),were compared by Wilcoxon signed rank test. Intra-class (ICC) and Pearson correlation coefficients (r) were computed. Explosives classification was performed through a prediction model with test-retest samples.

RESULTS : Except for DEI (p = 0.036), means of HU, ED, and Zeff were not statistically different (p > 0.05) between explosives ex-vivo and on the phantom (r > 0.80). Intra- and inter-reader ICC were good to excellent: 0.806 to 0.997 and 0.890, respectively. Except for the phantom DEI, all measurements from each individual explosive differed significantly. HU, ED, Zeff, and DEI differed depending on the type of explosive. Our decision tree provided Zeff and ED for explosives classification with high accuracy (83.7%) and excellent reliability (100%).

CONCLUSION : Our medical DECT algorithm and prediction model can readily detect and distinguish our range of explosives on the human body. This would avoid possible endangering of DVI staff.

Ognard Julien, Bourhis David, Cadieu Romain, Grenier Michel, Saccardy Claire, Alavi Zarrin, Ben Salem Douraied

2020-May-23

Artificial intelligence, Computer-assisted image processing, Explosives, Forensic medicine, Machine learning

General General

parSMURF, a high-performance computing tool for the genome-wide detection of pathogenic variants.

In GigaScience

BACKGROUND : Several prediction problems in computational biology and genomic medicine are characterized by both big data as well as a high imbalance between examples to be learned, whereby positive examples can represent a tiny minority with respect to negative examples. For instance, deleterious or pathogenic variants are overwhelmed by the sea of neutral variants in the non-coding regions of the genome: thus, the prediction of deleterious variants is a challenging, highly imbalanced classification problem, and classical prediction tools fail to detect the rare pathogenic examples among the huge amount of neutral variants or undergo severe restrictions in managing big genomic data.

RESULTS : To overcome these limitations we propose parSMURF, a method that adopts a hyper-ensemble approach and oversampling and undersampling techniques to deal with imbalanced data, and parallel computational techniques to both manage big genomic data and substantially speed up the computation. The synergy between Bayesian optimization techniques and the parallel nature of parSMURF enables efficient and user-friendly automatic tuning of the hyper-parameters of the algorithm, and allows specific learning problems in genomic medicine to be easily fit. Moreover, by using MPI parallel and machine learning ensemble techniques, parSMURF can manage big data by partitioning them across the nodes of a high-performance computing cluster. Results with synthetic data and with single-nucleotide variants associated with Mendelian diseases and with genome-wide association study hits in the non-coding regions of the human genome, involhing millions of examples, show that parSMURF achieves state-of-the-art results and an 80-fold speed-up with respect to the sequential version.

CONCLUSIONS : parSMURF is a parallel machine learning tool that can be trained to learn different genomic problems, and its multiple levels of parallelization and high scalability allow us to efficiently fit problems characterized by big and imbalanced genomic data. The C++ OpenMP multi-core version tailored to a single workstation and the C++ MPI/OpenMP hybrid multi-core and multi-node parSMURF version tailored to a High Performance Computing cluster are both available at https://github.com/AnacletoLAB/parSMURF.

Petrini Alessandro, Mesiti Marco, Schubach Max, Frasca Marco, Danis Daniel, Re Matteo, Grossi Giuliano, Cappelletti Luca, Castrignanò Tiziana, Robinson Peter N, Valentini Giorgio

2020-May-01

GWAS, Mendelian diseases, ensemble methods, high-performance computing, high-performance computing tool for genomic medicine, machine learning for genomic medicine, machine learning for imbalanced genomic data, parallel machine learning tool for big data, parallel machine learning tool for imbalanced data, prediction of deleterious or pathogenic variants

General General

Attentional multi-level representation encoding based on convolutional and variance autoencoders for lncRNA-disease association prediction.

In Briefings in bioinformatics

As the abnormalities of long non-coding RNAs (lncRNAs) are closely related to various human diseases, identifying disease-related lncRNAs is important for understanding the pathogenesis of complex diseases. Most of current data-driven methods for disease-related lncRNA candidate prediction are based on diseases and lncRNAs. Those methods, however, fail to consider the deeply embedded node attributes of lncRNA-disease pairs, which contain multiple relations and representations across lncRNAs, diseases and miRNAs. Moreover, the low-dimensional feature distribution at the pairwise level has not been taken into account. We propose a prediction model, VADLP, to extract, encode and adaptively integrate multi-level representations. Firstly, a triple-layer heterogeneous graph is constructed with weighted inter-layer and intra-layer edges to integrate the similarities and correlations among lncRNAs, diseases and miRNAs. We then define three representations including node attributes, pairwise topology and feature distribution. Node attributes are derived from the graph by an embedding strategy to represent the lncRNA-disease associations, which are inferred via their common lncRNAs, diseases and miRNAs. Pairwise topology is formulated by random walk algorithm and encoded by a convolutional autoencoder to represent the hidden topological structural relations between a pair of lncRNA and disease. The new feature distribution is modeled by a variance autoencoder to reveal the underlying lncRNA-disease relationship. Finally, an attentional representation-level integration module is constructed to adaptively fuse the three representations for lncRNA-disease association prediction. The proposed model is tested over a public dataset with a comprehensive list of evaluations. Our model outperforms six state-of-the-art lncRNA-disease prediction models with statistical significance. The ablation study showed the important contributions of three representations. In particular, the improved recall rates under different top $k$ values demonstrate that our model is powerful in discovering true disease-related lncRNAs in the top-ranked candidates. Case studies of three cancers further proved the capacity of our model to discover potential disease-related lncRNAs.

Sheng Nan, Cui Hui, Zhang Tiangang, Xuan Ping

2020-May-23

convolutional and variance autoencoders, deep learning, lncRNA–disease association prediction, representation-level attention

General General

Assessing the Big Five personality traits using real-life static facial images.

In Scientific reports ; h5-index 158.0

There is ample evidence that morphological and social cues in a human face provide signals of human personality and behaviour. Previous studies have discovered associations between the features of artificial composite facial images and attributions of personality traits by human experts. We present new findings demonstrating the statistically significant prediction of a wider set of personality features (all the Big Five personality traits) for both men and women using real-life static facial images. Volunteer participants (N = 12,447) provided their face photographs (31,367 images) and completed a self-report measure of the Big Five traits. We trained a cascade of artificial neural networks (ANNs) on a large labelled dataset to predict self-reported Big Five scores. The highest correlations between observed and predicted personality scores were found for conscientiousness (0.360 for men and 0.335 for women) and the mean effect size was 0.243, exceeding the results obtained in prior studies using 'selfies'. The findings strongly support the possibility of predicting multidimensional personality profiles from static facial images using ANNs trained on large labelled datasets. Future research could investigate the relative contribution of morphological features of the face and other characteristics of facial images to predicting personality.

Kachur Alexander, Osin Evgeny, Davydov Denis, Shutilov Konstantin, Novokshonov Alexey

2020-May-22

General General

Linking perturbations to temporal changes in diversity, stability, and compositions of neonatal calf gut microbiota: prediction of diarrhea.

In The ISME journal

Perturbations in early life gut microbiota can have long-term impacts on host health. In this study, we investigated antimicrobial-induced temporal changes in diversity, stability, and compositions of gut microbiota in neonatal veal calves, with the objective of identifying microbial markers that predict diarrhea. A total of 220 samples from 63 calves in first 8 weeks of life were used in this study. The results suggest that increase in diversity and stability of gut microbiota over time was a feature of "healthy" (non-diarrheic) calves during early life. Therapeutic antimicrobials delayed the temporal development of diversity and taxa-function robustness (a measure of microbial stability). In addition, predicted genes associated with beta lactam and cationic antimicrobial peptide resistance were more abundant in gut microbiota of calves treated with therapeutic antimicrobials. Random forest machine learning algorithm revealed that Trueperella, Streptococcus, Dorea, uncultured Lachnospiraceae, Ruminococcus 2, and Erysipelatoclostridium may be key microbial markers that can differentiate "healthy" and "unhealthy" (diarrheic) gut microbiota, as they predicted early life diarrhea with an accuracy of 84.3%. Our findings suggest that diarrhea in veal calves may be predicted by the shift in early life gut microbiota, which may provide an opportunity for early intervention (e.g., prebiotics or probiotics) to improve calf health with reduced usage of antimicrobials.

Ma Tao, Villot Clothilde, Renaud David, Skidmore Andrew, Chevaux Eric, Steele Michael, Guan Le Luo

2020-May-22

General General

BBMRI-ERIC's contributions to research and knowledge exchange on COVID-19.

In European journal of human genetics : EJHG

During the COVID-19 pandemic, the European biobanking infrastructure is in a unique position to preserve valuable biological material complemented with detailed data for future research purposes. Biobanks can be either integrated into healthcare, where preservation of the biological material is a fork in clinical routine diagnostics and medical treatment processes or they can also host prospective cohorts or material related to clinical trials. The paper discussed objectives of BBMRI-ERIC, the European research infrastructure established to facilitate access to quality-defined biological materials and data for research purposes, with respect to the COVID-19 crisis: (a) to collect information on available European as well as non-European COVID-19-relevant biobanking resources in BBMRI-ERIC Directory and to facilitate access to these via BBMRI-ERIC Negotiator platform; (b) to help harmonizing guidelines on how data and biological material is to be collected to maximize utility for future research, including large-scale data processing in artificial intelligence, by participating in activities such as COVID-19 Host Genetics Initiative; (c) to minimize risks for all involved parties dealing with (potentially) infectious material by developing recommendations and guidelines; (d) to provide a European-wide platform of exchange in relation to ethical, legal, and societal issues (ELSI) specific to the collection of biological material and data during the COVID-19 pandemic.

Holub Petr, Kozera Lukasz, Florindi Francesco, van Enckevort Esther, Swertz Morris, Reihs Robert, Wutte Andrea, Valík Dalibor, Mayrhofer Michaela Th

2020-May-22

Pathology Pathology

Combining gene expression profiling and machine learning to diagnose B-cell non-Hodgkin lymphoma.

In Blood cancer journal ; h5-index 41.0

Non-Hodgkin B-cell lymphomas (B-NHLs) are a highly heterogeneous group of mature B-cell malignancies. Their classification thus requires skillful evaluation by expert hematopathologists, but the risk of error remains higher in these tumors than in many other areas of pathology. To facilitate diagnosis, we have thus developed a gene expression assay able to discriminate the seven most frequent B-cell NHL categories. This assay relies on the combination of ligation-dependent RT-PCR and next-generation sequencing, and addresses the expression of more than 130 genetic markers. It was designed to retrieve the main gene expression signatures of B-NHL cells and their microenvironment. The classification is handled by a random forest algorithm which we trained and validated on a large cohort of more than 400 annotated cases of different histology. Its clinical relevance was verified through its capacity to prevent important misclassification in low grade lymphomas and to retrieve clinically important characteristics in high grade lymphomas including the cell-of-origin signatures and the MYC and BCL2 expression levels. This accurate pan-B-NHL predictor, which allows a systematic evaluation of numerous diagnostic and prognostic markers, could thus be proposed as a complement to conventional histology to guide the management of patients and facilitate their stratification into clinical trials.

Bobée Victor, Drieux Fanny, Marchand Vinciane, Sater Vincent, Veresezan Liana, Picquenot Jean-Michel, Viailly Pierre-Julien, Lanic Marie-Delphine, Viennot Mathieu, Bohers Elodie, Oberic Lucie, Copie-Bergman Christiane, Molina Thierry Jo, Gaulard Philippe, Haioun Corinne, Salles Gilles, Tilly Hervé, Jardin Fabrice, Ruminy Philippe

2020-May-22

General General

Using publicly available satellite imagery and deep learning to understand economic well-being in Africa.

In Nature communications ; h5-index 260.0

Accurate and comprehensive measurements of economic well-being are fundamental inputs into both research and policy, but such measures are unavailable at a local level in many parts of the world. Here we train deep learning models to predict survey-based estimates of asset wealth across ~ 20,000 African villages from publicly-available multispectral satellite imagery. Models can explain 70% of the variation in ground-measured village wealth in countries where the model was not trained, outperforming previous benchmarks from high-resolution imagery, and comparison with independent wealth measurements from censuses suggests that errors in satellite estimates are comparable to errors in existing ground data. Satellite-based estimates can also explain up to 50% of the variation in district-aggregated changes in wealth over time, with daytime imagery particularly useful in this task. We demonstrate the utility of satellite-based estimates for research and policy, and demonstrate their scalability by creating a wealth map for Africa's most populous country.

Yeh Christopher, Perez Anthony, Driscoll Anne, Azzari George, Tang Zhongyi, Lobell David, Ermon Stefano, Burke Marshall

2020-May-22

General General

Supervised machine learning for source allocation of per- and polyfluoroalkyl substances (PFAS) in environmental samples.

In Chemosphere

Environmental contamination by per- and polyfluoroalkyl substances (PFAS) is widespread, because of both their decades of use, and their persistence in the environment. These factors can make identification of the source of contamination in samples a challenge, because in many cases contamination may originate from decades ago, or from a number of candidate sources. Forensic source allocation is important for delineating plumes, and may also be able to provide insights into environmental behaviors of specific PFAS components. This paper describes work conducted to explore the use of supervised machine learning classifiers for allocating the source of PFAS contamination based on patterns identified in component concentrations. A dataset containing PFAS component concentrations in 1197 environmental water samples was assembled based on data from sites from around the world. The dataset was split evenly into training and test datasets, and the 598-sample training dataset was used to train four machine learning classifiers, including three conventional machine learning classifiers (Extra Trees, Support-Vector Machines, K-Neighbors), and one multilayer perceptron feedforward deep neural network. Of the methods tested, the deep neural network and Extra Trees exhibited particularly high performance at classification of samples from a range of sources. The fact that the methods function on completely different principles and yet provide similar predictions supports the hypothesis that patterns exist in PFAS water sample data that can allow forensic source allocation. The results of the work support the idea that supervised machine learning may have substantial promise as a tool for forensic source allocation.

Kibbey Tohren C G, Jabrzemski Rafal, O’Carroll Denis M

2020-Aug

Machine learning, Neural networks, PFAS, Pattern recognition, Source allocation

Radiology Radiology

Intensive Care Risk Estimation in COVID-19 Pneumonia Based on Clinical and Imaging Parameters: Experiences from the Munich Cohort.

In Journal of clinical medicine

The evolving dynamics of coronavirus disease 2019 (COVID-19) and the increasing infection numbers require diagnostic tools to identify patients at high risk for a severe disease course. Here we evaluate clinical and imaging parameters for estimating the need of intensive care unit (ICU) treatment. We collected clinical, laboratory and imaging data from 65 patients with confirmed COVID-19 infection based on polymerase chain reaction (PCR) testing. Two radiologists evaluated the severity of findings in computed tomography (CT) images on a scale from 1 (no characteristic signs of COVID-19) to 5 (confluent ground glass opacities in over 50% of the lung parenchyma). The volume of affected lung was quantified using commercially available software. Machine learning modelling was performed to estimate the risk for ICU treatment. Patients with a severe course of COVID-19 had significantly increased interleukin (IL)-6, C-reactive protein (CRP), and leukocyte counts and significantly decreased lymphocyte counts. The radiological severity grading was significantly increased in ICU patients. Multivariate random forest modelling showed a mean ± standard deviation sensitivity, specificity and accuracy of 0.72 ± 0.1, 0.86 ± 0.16 and 0.80 ± 0.1 and a receiver operating characteristic-area under curve (ROC-AUC) of 0.79 ± 0.1. The need for ICU treatment is independently associated with affected lung volume, radiological severity score, CRP, and IL-6.

Burian Egon, Jungmann Friederike, Kaissis Georgios A, Lohöfer Fabian K, Spinner Christoph D, Lahmer Tobias, Treiber Matthias, Dommasch Michael, Schneider Gerhard, Geisler Fabian, Huber Wolfgang, Protzer Ulrike, Schmid Roland M, Schwaiger Markus, Makowski Marcus R, Braren Rickmer F

2020-May-18

COVID-19, clinical parameters, computed tomography, intensive care unit, radiological parameters, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)

General General

Using Artificial Intelligence and Novel Polynomials to Predict Subjective Refraction.

In Scientific reports ; h5-index 158.0

This work aimed to use artificial intelligence to predict subjective refraction from wavefront aberrometry data processed with a novel polynomial decomposition basis. Subjective refraction was converted to power vectors (M, J0, J45). Three gradient boosted trees (XGBoost) algorithms were trained to predict each power vector using data from 3729 eyes. The model was validated by predicting subjective refraction power vectors of 350 other eyes, unknown to the model. The machine learning models were significantly better than the paraxial matching method for producing a spectacle correction, resulting in a mean absolute error of 0.301 ± 0.252 Diopters (D) for the M vector, 0.120 ± 0.094 D for the J0 vector and 0.094 ± 0.084 D for the J45 vector. Our results suggest that subjective refraction can be accurately and precisely predicted from novel polynomial wavefront data using machine learning algorithms. We anticipate that the combination of machine learning and aberrometry based on this novel wavefront decomposition basis will aid the development of refined algorithms which could become a new gold standard to predict refraction objectively.

Rampat Radhika, Debellemanière Guillaume, Malet Jacques, Gatinel Damien

2020-May-22

General General

Continuous monitoring of suspended sediment concentrations using image analytics and deriving inherent correlations by machine learning.

In Scientific reports ; h5-index 158.0

The barriers for the development of continuous monitoring of Suspended Sediment Concentration (SSC) in channels/rivers include costs and technological gaps but this paper shows that a solution is feasible by: (i) using readily available high-resolution images; (ii) transforming the images into image analytics to form a modelling dataset; and (iii) constructing predictive models by learning inherent correlation between observed SSC values and their image analytics. High-resolution images were taken of water containing a series of SSC values using an exploratory flume. Machine learning is processed by dividing the dataset into training and testing sets and the paper uses the following models: Generalized Linear Machine (GLM) and Distributed Random Forest (DRF). Results show that each model is capable of reliable predictions but the errors at higher SSC are not fully explained by modelling alone. Here we offer sufficient evidence for the feasibility of a continuous SSC monitoring capability in channels before the next phase of the study with the goal of producing practice guidelines.

Ghorbani Mohammad Ali, Khatibi Rahman, Singh Vijay P, Kahya Ercan, Ruskeepää Heikki, Saggi Mandeep Kaur, Sivakumar Bellie, Kim Sungwon, Salmasi Farzin, Hasanpour Kashani Mahsa, Samadianfard Saeed, Shahabi Mahmood, Jani Rasoul

2020-May-22

General General

A biochemically-interpretable machine learning classifier for microbial GWAS.

In Nature communications ; h5-index 260.0

Current machine learning classifiers have successfully been applied to whole-genome sequencing data to identify genetic determinants of antimicrobial resistance (AMR), but they lack causal interpretation. Here we present a metabolic model-based machine learning classifier, named Metabolic Allele Classifier (MAC), that uses flux balance analysis to estimate the biochemical effects of alleles. We apply the MAC to a dataset of 1595 drug-tested Mycobacterium tuberculosis strains and show that MACs predict AMR phenotypes with accuracy on par with mechanism-agnostic machine learning models (isoniazid AUC = 0.93) while enabling a biochemical interpretation of the genotype-phenotype map. Interpretation of MACs for three antibiotics (pyrazinamide, para-aminosalicylic acid, and isoniazid) recapitulates known AMR mechanisms and suggest a biochemical basis for how the identified alleles cause AMR. Extending flux balance analysis to identify accurate sequence classifiers thus contributes mechanistic insights to GWAS, a field thus far dominated by mechanism-agnostic results.

Kavvas Erol S, Yang Laurence, Monk Jonathan M, Heckmann David, Palsson Bernhard O

2020-May-22

General General

DCU-Net: Multi-scale U-Net for brain tumor segmentation.

In Journal of X-ray science and technology

BACKGROUND : Brain tumor segmentation plays an important role in assisting diagnosis of disease, treatment plan planning, and surgical navigation.

OBJECTIVE : This study aims to improve the accuracy of tumor boundary segmentation using the multi-scale U-Net network.

METHODS : In this study, a novel U-Net with dilated convolution (DCU-Net) structure is proposed for brain tumor segmentation based on the classic U-Net structure. First, the MR brain tumor images are pre-processed to alleviate the class imbalance problem by reducing the input of the background pixels. Then, the multi-scale spatial pyramid pooling is used to replace the max pooling at the end of the down-sampling path. It can expand the feature receptive field while maintaining image resolution. Finally, a dilated convolution residual block is combined to improve the skip connections in the training networks to improve the network's ability to recognize the tumor details.

RESULTS : The proposed model has been evaluated using the Brain Tumor Segmentation (BRATS) 2018 Challenge training dataset and achieved the dice similarity coefficients (DSC) score of 0.91, 0.78 and 0.83 for whole tumor, core tumor and enhancing tumor segmentation, respectively.

CONCLUSIONS : The experiment results indicate that the proposed model yields a promising performance in automated brain tumor segmentation.

Yang Tiejun, Zhou Yudan, Li Lei, Zhu Chunhua

2020-May-16

Brain tumor segmentation, DCU-Net, U-Net, dilated convolution, multi-scale spatial pyramid pooling

General General

Detecting breast cancer using artificial intelligence: Convolutional neural network.

In Technology and health care : official journal of the European Society for Engineering and Medicine

BACKGROUND : One of the most broadly founded approaches to envisage cancer treatment relies upon a pathologist's efficiency to visually inspect the appearances of bio-markers on the invasive tumor tissue section. Lately, deep learning techniques have radically enriched the ability of computers to identify objects in images fostering the prospect for fully automated computer-aided diagnosis. Given the noticeable role of nuclear structure in cancer detection, AI's pattern recognizing ability can expedite the diagnostic process.

OBJECTIVE : In this study, we propose and implement an image classification technique to identify breast cancer.

METHODS : We implement the convolutional neural network (CNN) on breast cancer image data set to identify invasive ductal carcinoma (IDC).

RESULT : The proposed CNN model after data augmentation yielded 78.4% classification accuracy. 16% of IDC (-) were predicted incorrectly (false negative) whereas 25% of IDC (+) were predicted incorrectly (false positive).

CONCLUSION : The results achieved by the proposed approach have shown that it is feasible to employ a convolutional neural network particularly for breast cancer classification tasks. However, a common problem in any artificial intelligence algorithm is its dependence on the data set. Therefore, the performance of the proposed model might not be generalized.

Choudhury Avishek, Perumalla Sunanda

2020-May-09

Convolutional neural network, artificial intelligence, breast cancer, deep learning, ductal carcinoma, machine learning

Pathology Pathology

Deep Phenotyping of Parkinson's Disease.

In Journal of Parkinson's disease

Phenotype is the set of observable traits of an organism or condition. While advances in genetics, imaging, and molecular biology have improved our understanding of the underlying biology of Parkinson's disease (PD), clinical phenotyping of PD still relies primarily on history and physical examination. These subjective, episodic, categorical assessments are valuable for diagnosis and care but have created gaps in our understanding of the PD phenotype. Sensors can provide objective, continuous, real-world data about the PD clinical phenotype, increase our knowledge of its pathology, enhance evaluation of therapies, and ultimately, improve patient care. In this paper, we explore the concept of deep phenotyping-the comprehensive assessment of a condition using multiple clinical, biological, genetic, imaging, and sensor-based tools-for PD. We discuss the rationale for, outline current approaches to, identify benefits and limitations of, and consider future directions for deep clinical phenotyping.

Dorsey E Ray, Omberg Larsson, Waddell Emma, Adams Jamie L, Adams Roy, Ali Mohammad Rafayet, Amodeo Katherine, Arky Abigail, Augustine Erika F, Dinesh Karthik, Hoque Mohammed Ehsan, Glidden Alistair M, Jensen-Roberts Stella, Kabelac Zachary, Katabi Dina, Kieburtz Karl, Kinel Daniel R, Little Max A, Lizarraga Karlo J, Myers Taylor, Riggare Sara, Rosero Spencer Z, Saria Suchi, Schifitto Giovanni, Schneider Ruth B, Sharma Gaurav, Shoulson Ira, Stevenson E Anna, Tarolli Christopher G, Luo Jiebo, McDermott Michael P

2020-May-22

Autonomic nervous system, Parkinson’s disease, gait, natural history, observational study, phenotype, real-world data, sleep, smartphone, social behavior

Radiology Radiology

A Fully Automatic Deep Learning System for COVID-19 Diagnostic and Prognostic Analysis.

In The European respiratory journal

Coronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19, and finding high-risk patients with worse prognosis for early prevention and medical resources optimisation is important. Here, we proposed a fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis by routinely used computed tomography.We retrospectively collected 5372 patients with computed tomography images from 7 cities or provinces. Firstly, 4106 patients with computed tomography images were used to pre-train the DL system, making it learn lung features. Afterwards, 1266 patients (924 with COVID-19, and 471 had follow-up for 5+ days; 342 with other pneumonia) from 6 cities or provinces were enrolled to train and externally validate the performance of the deep learning system.In the 4 external validation sets, the deep learning system achieved good performance in identifying COVID-19 from other pneumonia (AUC=0.87 and 0.88) and viral pneumonia (AUC=0.86). Moreover, the deep learning system succeeded to stratify patients into high-risk and low-risk groups whose hospital-stay time have significant difference (p=0.013 and 0.014). Without human-assistance, the deep learning system automatically focused on abnormal areas that showed consistent characteristics with reported radiological findings.Deep learning provides a convenient tool for fast screening COVID-19 and finding potential high-risk patients, which may be helpful for medical resource optimisation and early prevention before patients show severe symptoms.

Wang Shuo, Zha Yunfei, Li Weimin, Wu Qingxia, Li Xiaohu, Niu Meng, Wang Meiyun, Qiu Xiaoming, Li Hongjun, Yu He, Gong Wei, Bai Yan, Li Li, Zhu Yongbei, Wang Liusu, Tian Jie

2020-May-22

Radiology Radiology

Comparison of 3 deep learning neural networks for classifying the relationship between the mandibular third molar and the mandibular canal on panoramic radiographs.

In Oral surgery, oral medicine, oral pathology and oral radiology ; h5-index 33.0

OBJECTIVE : The aim of this study was to compare time and storage space requirements, diagnostic performance, and consistency among 3 image recognition convolutional neural networks (CNNs) in the evaluation of the relationships between the mandibular third molar and the mandibular canal on panoramic radiographs.

STUDY DESIGN : Of 600 panoramic radiographs, 300 each were assigned to noncontact and contact groups based on the relationship between the mandibular third molar and the mandibular canal. The CNNs were trained twice by using cropped image patches with sizes of 70 × 70 pixels and 140 × 140 pixels. Time and storage space were measured for each system. Accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) were determined. Intra-CNN and inter-CNN consistency values were calculated.

RESULTS : Time and storage space requirements depended on the depth of CNN layers and number of learned parameters, respectively. The highest AUC values ranged from 0.88 to 0.93 in the CNNs created by 70 × 70 pixel patches, but there were no significant differences in diagnostic performance among any of the models with smaller patches. Intra-CNN and inter-CNN consistency values were good or very good for all CNNs.

CONCLUSIONS : The size of the image patches should be carefully determined to ensure acquisition of high diagnostic performance and consistency.

Fukuda Motoki, Ariji Yoshiko, Kise Yoshitaka, Nozawa Michihito, Kuwada Chiaki, Funakoshi Takuma, Muramatsu Chisako, Fujita Hiroshi, Katsumata Akitoshi, Ariji Eiichiro

2020-May-19

General General

Enhancer Predictions and Genome-Wide Regulatory Circuits.

In Annual review of genomics and human genetics

Spatiotemporal control of gene expression during development requires orchestrated activities of numerous enhancers, which are cis-regulatory DNA sequences that, when bound by transcription factors, support selective activation or repression of associated genes. Proper activation of enhancers is critical during embryonic development, adult tissue homeostasis, and regeneration, and inappropriate enhancer activity is often associated with pathological conditions such as cancer. Multiple consortia [e.g., the Encyclopedia of DNA Elements (ENCODE) Consortium and National Institutes of Health Roadmap Epigenomics Mapping Consortium] and independent investigators have mapped putative regulatory regions in a large number of cell types and tissues, but the sequence determinants of cell-specific enhancers are not yet fully understood. Machine learning approaches trained on large sets of these regulatory regions can identify core transcription factor binding sites and generate quantitative predictions of enhancer activity and the impact of sequence variants on activity. Here, we review these computational methods in the context of enhancer prediction and gene regulatory network models specifying cell fate. Expected final online publication date for the Annual Review of Genomics and Human Genetics, Volume 21 is August 31, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Beer Michael A, Shigaki Dustin, Huangfu Danwei

2020-May-22

Cardiology Cardiology

Automatic Detection of Atrial Fibrillation Based on CNN-LSTM and Shortcut Connection.

In Healthcare (Basel, Switzerland)

Atrial fibrillation (AF) is one of the most common persistent arrhythmias, which has a close connection to a large number of cardiovascular diseases. However, if spotted early, the diagnosis of AF can improve the effectiveness of clinical treatment and effectively prevent serious complications. In this paper, a combination of an 8-layer convolutional neural network (CNN) with a shortcut connection and 1-layer long short-term memory (LSTM), named 8CSL, was proposed for the Electrocardiogram (ECG) classification task. Compared with recurrent neural networks (RNN) and multi-scale convolution neural networks (MCNN), not only can 8CSL extract features skillfully, but also deal with long-term dependency between data. In particular, 8CSL includes eight shortcut connections that can improve the speed of the data transmission and processing as a result of the shortcut connections. The model was evaluated on the base of the test set of the Computing in Cardiology Challenge 2017 dataset with the F1 score. The ECG recordings were cropped or padded to the same length. After 10-fold cross-validation, the average test F1 score was 84.89%, 89.55%, and 85.64% when the segment length was 5, 10, 20 seconds, respectively. The experiment results demonstrate excellent performance with potential practical applications.

Ping Yongjie, Chen Chao, Wu Lu, Wang Yinglong, Shu Minglei

2020-May-20

CNN with shortcut connection, atrial fibrillation (AF), long short-term memory (LSTM)

Surgery Surgery

Converging intracortical signatures of two separated processing timescales in human early auditory cortex.

In NeuroImage ; h5-index 117.0

Neural oscillations in auditory cortex are argued to support parsing and representing speech constituents at their corresponding temporal scales. Yet, how incoming sensory information interacts with ongoing spontaneous brain activity, what features of the neuronal microcircuitry underlie spontaneous and stimulus-evoked spectral fingerprints, and what these fingerprints entail for stimulus encoding, remain largely open questions. We used a combination of human invasive electrophysiology, computational modeling and decoding techniques to assess the information encoding properties of brain activity and to relate them to a plausible underlying neuronal microarchitecture. We analyzed intracortical auditory EEG activity from 10 patients while they were listening to short sentences. Pre-stimulus neural activity in early auditory cortical regions often exhibited power spectra with a shoulder in the delta range and a small bump in the beta range. Speech decreased power in the beta range, and increased power in the delta-theta and gamma ranges. Using multivariate machine learning techniques, we assessed the spectral profile of information content for two aspects of speech processing: detection and discrimination. We obtained better phase than power information decoding, and a bimodal spectral profile of information content with better decoding at low (delta-theta) and high (gamma) frequencies than at intermediate (beta) frequencies. These experimental data were reproduced by a simple rate model made of two subnetworks with different timescales, each composed of coupled excitatory and inhibitory units, and connected via a negative feedback loop. Modeling and experimental results were similar in terms of pre-stimulus spectral profile (except for the iEEG beta bump), spectral modulations with speech, and spectral profile of information content. Altogether, we provide converging evidence from both univariate spectral analysis and decoding approaches for a dual timescale processing infrastructure in human auditory cortex, and show that it is consistent with the dynamics of a simple rate model.

Baroni Fabiano, Morillon Benjamin, Trébuchon Agnès, Liégeois-Chauvel Catherine, Olasagasti Itsaso, Giraud Anne-Lise

2020-May-18

auditory cortex, brain decoding, computational modeling, iEEG, spectral analysis, speech perception

General General

Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states.

In NeuroImage ; h5-index 117.0

Predicting biomedical outcomes from Magnetoencephalography and Electroencephalography (M/EEG) is central to applications like decoding, brain-computer-interfaces (BCI) or biomarker development and is facilitated by supervised machine learning. Yet most of the literature is concerned with classification of outcomes defined at the event-level. Here, we focus on predicting continuous outcomes from M/EEG signal defined at the subject-level, and analyze about 600 MEG recordings from Cam-CAN dataset and about 1000 EEG recordings from TUH dataset. Considering different generative mechanisms for M/EEG signals and the biomedical outcome, we propose statistically-consistent predictive models that avoid source-reconstruction based on the covariance as representation. Our mathematical analysis and ground truth simulations demonstrated that consistent function approximation can be obtained with supervised spatial filtering or by embedding with Riemannian geometry. Additional simulations revealed that Riemannian methods were more robust to model violations, in particular geometric distortions induced by individual anatomy. To estimate the relative contribution of brain dynamics and anatomy to prediction performance, we propose a novel model inspection procedure based on biophysical forward modeling. Applied to prediction of outcomes at the subject-level, the analysis revealed that the Riemannian model better exploited anatomical information while sensitivity to brain dynamics was similar across methods. We then probed the robustness of the models across different data cleaning options. Environmental denoising was globally important but Riemannian models were strikingly robust and continued performing well even without preprocessing. Our results suggest each method has its niche: supervised spatial filtering is practical for event-level prediction while the Riemannian model may enable simple end-to-end learning.

Sabbagh David, Ablin Pierre, Varoquaux Gaël, Gramfort Alexandre, Engemann Denis A

2020-May-18

Covariance, MEG/EEG, Machine Learning, Neuronal oscillations, Riemannian Geometry, Spatial Filters

General General

Summarization of biomedical articles using domain-specific word embeddings and graph ranking.

In Journal of biomedical informatics ; h5-index 55.0

Text summarization tools can help biomedical researchers and clinicians reduce the time and effort needed for acquiring important information from numerous documents. It has been shown that the input text can be modeled as a graph, and important sentences can be selected by identifying central nodes within the graph. However, the effective representation of documents, quantifying the relatedness of sentences, and selecting the most informative sentences are main challenges that need to be addressed in graph-based summarization. In this paper, we address these challenges in the context of biomedical text summarization. We evaluate the efficacy of a graph-based summarizer using different types of context-free and contextualized embeddings. The word representations are produced by pre-training neural language models on large corpora of biomedical texts. The summarizer models the input text as a graph in which the strength of relations between sentences is measured using the domain specific vector representations. We also assess the usefulness of different graph ranking techniques in the sentence selection step of our summarization method. Using the common Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics, we evaluate the performance of our summarizer against various comparison methods. The results show that when the summarizer utilizes proper combinations of context-free and contextualized embeddings, along with an effective ranking method, it can outperform the other methods. We demonstrate that the best settings of our graph-based summarizer can efficiently improve the informative content of summaries and decrease the redundancy.

Moradi Milad, Dashti Maedeh, Samwald Matthias

2020-May-18

Deep learning, Graph ranking, Medical text mining, Natural language processing, Text summarization, Word embedding

General General

How Artificial Intelligence Will Impact Colonoscopy and Colorectal Screening.

In Gastrointestinal endoscopy clinics of North America

Artificial intelligence may improve value in colonoscopy-based colorectal screening and surveillance by improving quality and decreasing unnecessary costs. The quality of screening and surveillance as measured by adenoma detection rates can be improved through real-time computer-assisted detection of polyps. Unnecessary costs can be decreased with optical biopsies to identify low-risk polyps using computer-assisted diagnosis that can undergo the resect-and-discard or diagnose-and-leave strategy. Key challenges include the clinical integration of artificial intelligence-based technology into the endoscopists' workflow, the effect of this technology on endoscopy center efficiency, and the interpretability of the underlying deep learning algorithms. The future for image-based artificial intelligence in gastroenterology will include applications to improve the diagnosis and treatment of cancers throughout the gastrointestinal tract.

Shung Dennis L, Byrne Michael F

2020-Jul

Artificial intelligence, Colonoscopy, Value-based care

General General

The Case for High-Quality Colonoscopy Remaining a Premier Colorectal Cancer Screening Strategy in the United States.

In Gastrointestinal endoscopy clinics of North America

Most colorectal cancer screening in the United States occurs in the opportunistic setting, where screening is initiated by a patient-provider interaction. Colonoscopy provides the longest-interval protection, and high-quality colonoscopy is ideally suited to the opportunistic setting. Both detection and colonoscopic resection have improved as a result of intense scientific investigation. Further improvements in detection are expected with the introduction of artificial intelligence programs into colonoscopy platforms. We may expect recommended intervals or colonoscopy after negative examinations performed by high-quality detectors to expand beyond 10 years. Thus, high-quality colonoscopy remains an excellent approach to colorectal cancer screening in the opportunistic setting.

Rex Douglas K

2020-Jul

Colonoscopy, Colorectal adenomas, Colorectal cancer, Colorectal cancer screening, Colorectal polyps

Public Health Public Health

Risk Stratification Strategies for Colorectal Cancer Screening: From Logistic Regression to Artificial Intelligence.

In Gastrointestinal endoscopy clinics of North America

Risk stratification is a system by which clinically meaningful separation of risk is achieved in a group of otherwise similar persons. Although parametric logistic regression dominates risk prediction, use of nonparametric and semiparametric methods, including artificial neural networks, is increasing. These statistical-learning and machine-learning methods, along with simple rules, are collectively referred to as "artificial intelligence" (AI). AI requires knowledge of study validity, understanding of model metrics, and determination of whether and to what extent the model can and should be applied to the patient or population under consideration. Further investigation is needed, especially in model validation and impact assessment.

Imperiale Thomas F, Monahan Patrick O

2020-Jul

Cancer prevention, Colorectal cancer screening, Machine learning methods, Multivariate methods, Risk prediction models, Risk stratification

General General

Immunological Profiling of Paediatric Inflammatory Bowel Disease Using Unsupervised Machine Learning.

In Journal of pediatric gastroenterology and nutrition ; h5-index 50.0

OBJECTIVES : The current classification of inflammatory bowel disease (IBD) is based on clinical phenotypes, which is blind to the molecular basis of the disease. The aim of this study was to stratify a treatment-naïve paediatric IBD cohort through specific innate immunity pathway profiling and application of unsupervised machine learning (UML).

METHODS : In order to test the molecular integrity of biological pathways implicated in IBD, innate immune responses were assessed at diagnosis in 22 paediatric patients and 10 age-matched controls. Peripheral blood mononuclear cells (PBMCs) were selectively stimulated for assessing the functionality of upstream activation receptors including NOD2, toll-like receptor (TLR) 1-2 and TLR4, and the downstream cytokine responses (IL-10, IL-1β, IL-6, and TNF-α) using multiplex assays. Cytokine data generated were subjected to hierarchical clustering to assess for patient stratification.

RESULTS : Combined immune responses in patients across 12 effector responses were significantly reduced compared with controls (P = 0.003) and driven primarily by "hypofunctional" TLR responses (P values 0.045, 0.010, and 0.018 for TLR4-mediated IL-10, IL-1β, and TNF-α, respectively; 0.018 and 0.015 for TLR1-2 -mediated IL-10 and IL-1β). Hierarchical clustering generated 3 distinct clusters of patients and a fourth group of "unclustered" individuals. No relationship was observed between the observed immune clusters and the clinical disease phenotype.

CONCLUSIONS : Although a clinically useful outcome was not observed through hierarchical clustering, our study provides a rationale for using an UML approach to stratify patients. The study also highlights the predominance of hypo-inflammatory innate immune responses as a key mechanism in the pathogenesis of IBD.

Coelho Tracy, Mossotto Enrico, Gao Yifang, Haggarty Rachel, Ashton James J, Batra Akshay, Stafford Imogen S, Beattie Robert M, Williams Anthony P, Ennis Sarah

2020-Jun

General General

Design of high-performance plasmonic nanosensors by particle swarm optimization algorithm combined with machine learning.

In Nanotechnology

Metallic plasmonic nanosensors that are ultra-sensitive, label-free, and operate in real time hold great promise in the field of chemical and biological research. Conventionally, the design of these nanostructures has strongly relied on time-consuming electromagnetic simulations that iteratively solve Maxwell's equations to scan multi-dimensional parameter space until the desired sensing performance is attained. Here, we propose an algorithm based on particle swarm optimization (PSO), which in combination with a machine learning (ML) model, is used to design plasmonic sensors. The ML model is trained with the geometric structure and sensing performance of the plasmonic sensor to accurately capture the geometry-sensing performance relationships, and the well-trained ML model is then applied to the PSO algorithm to obtain the plasmonic structure with the desired sensing performance. Using the trained ML model to predict the sensing performance instead of using complex electromagnetic calculation methods allows the PSO algorithm to optimize the solutions fours orders of magnitude faster. Implementation of this composite algorithm enabled us to quickly and accurately realize a nanoridge plasmonic sensor with sensitivity as high as 142,500 nm/RIU. We expect this efficient and accurate approach to pave the way for the design of nanophotonic devices in future.

Yan RuoQin, Wang Tao, Jiang Xiaoyun, Zhong Qingfang, Huang Xing, Wang Lu, Yue XinZhao

2020-May-22

machine learning, nanosensor, particle swarm optimization, plasmonic

Ophthalmology Ophthalmology

Towards multi-center glaucoma OCT image screening with semi-supervised joint structure and function multi-task learning.

In Medical image analysis

Glaucoma is the leading cause of irreversible blindness in the world. Structure and function assessments play an important role in diagnosing glaucoma. Nowadays, Optical Coherence Tomography (OCT) imaging gains increasing popularity in measuring the structural change of eyes. However, few automated methods have been developed based on OCT images to screen glaucoma. In this paper, we are the first to unify the structure analysis and function regression to distinguish glaucoma patients from normal controls effectively. Specifically, our method works in two steps: a semi-supervised learning strategy with smoothness assumption is first applied for the surrogate assignment of missing function regression labels. Subsequently, the proposed multi-task learning network is capable of exploring the structure and function relationship between the OCT image and visual field measurement simultaneously, which contributes to classification performance improvement. It is also worth noting that the proposed method is assessed by two large-scale multi-center datasets. In other words, we first build the largest glaucoma OCT image dataset (i.e., HK dataset) involving 975,400 B-scans from 4,877 volumes to develop and evaluate the proposed method, then the model without further fine-tuning is directly applied on another independent dataset (i.e., Stanford dataset) containing 246,200 B-scans from 1,231 volumes. Extensive experiments are conducted to assess the contribution of each component within our framework. The proposed method outperforms the baseline methods and two glaucoma experts by a large margin, achieving volume-level Area Under ROC Curve (AUC) of 0.977 on HK dataset and 0.933 on Stanford dataset, respectively. The experimental results indicate the great potential of the proposed approach for the automated diagnosis system.

Wang Xi, Chen Hao, Ran An-Ran, Luo Luyang, Chan Poemen P, Tham Clement C, Chang Robert T, Mannil Suria S, Cheung Carol Y, Heng Pheng-Ann

2020-May-19

Deep learning, Glaucoma screening, Optical coherence tomography, Semi-supervised multi-task learning

General General

Self-weighted adaptive structure learning for ASD diagnosis via multi-template multi-center representation.

In Medical image analysis

As a kind of neurodevelopmental disease, autism spectrum disorder (ASD) can cause severe social, communication, interaction, and behavioral challenges. To date, many imaging-based machine learning techniques have been proposed to address ASD diagnosis issues. However, most of these techniques are restricted to a single template or dataset from one imaging center. In this paper, we propose a novel multi-template multi-center ensemble classification scheme for automatic ASD diagnosis. Specifically, based on different pre-defined templates, we construct multiple functional connectivity (FC) brain networks for each subject based on our proposed Pearson's correlation-based sparse low-rank representation. After extracting features from these FC networks, informative features to learn optimal similarity matrix are then selected by our self-weighted adaptive structure learning (SASL) model. For each template, the SASL method automatically assigns an optimal weight learned from the structural information without additional weights and parameters. Finally, an ensemble strategy based on the multi- template multi-center representations is applied to derive the final diagnosis results. Extensive experiments are conducted on the publicly available Autism Brain Imaging Data Exchange (ABIDE) database to demonstrate the efficacy of our proposed method. Experimental results verify that our proposed method boosts ASD diagnosis performance and outperforms state-of-the-art methods.

Huang Fanglin, Tan Ee-Leng, Yang Peng, Huang Shan, Ou-Yang Le, Cao Jiuwen, Wang Tianfu, Lei Baiying

2020-Feb-01

Autism spectrum disorder, Multi-template multi-center, “Pearsons correlation (PC) -based sparse low-rank representation”, Self-weighted adaptive structure learning

General General

Artificial neural networks applied to quality-by-design: From formulation development to clinical outcome.

In European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V

Quality-by-Design (QbD) is a methodology used to build quality into products and is characterized by a well-defined roadmap. In this study, the application of Artificial Neural Networks (ANNs) in the QbD-based development of a test drug product is presented, where material specifications are defined and correlated with its performance in vivo. Along with other process parameters, drug particle size distribution (PSD) was identified as a critical material attribute and a three-tier specification was needed. An ANN was built with only five hidden nodes in one hidden layer, using hyperbolic tangent functions, and was validated using a random holdback of 33% of the dataset. The model led to significant and valid prediction formulas for the three responses, with R2 values higher than 0.94 for all responses, both for the training and the validation datasets. The prediction formulas were applied to contour plots and tight limits were set based on the design space and feasible working area for the drug PSD, as well as for process parameters. The manufacturing process was validated through the production of three exhibit batches of 180,000 tablets in the industrial GMP facility, and the ANN model was applied to successfully predict the in vitro dissolution, with a bias of approximately 5%. The product was then tested on two clinical studies (under fasting and fed conditions) and the criteria to demonstrate bioequivalence to the Reference Listed Drug were met. In this study, ANNs were successfully applied to support the establishment of drug specifications and limits for process parameters, bridging the formulation development with in vitro performance and the positive clinical results obtained in the bioequivalence studies.

Simões Marta F, Silva Gabriel, Pinto Ana C, Fonseca Marlene, Silva Nuno E, Pinto Rui M A, Simões Sérgio

2020-May-19

Artificial intelligence, Artificial neural networks, Bioequivalence, Design space, Poorly soluble drugs, Predictive modeling, Quality by design, Wet granulation

Pathology Pathology

Deep-Hipo: Multi-scale Receptive Field Deep Learning for Histopathological Image Analysis.

In Methods (San Diego, Calif.)

Digitizing whole-slide imaging in digital pathology has led to the advancement of computer-aided tissue examination using machine learning techniques, especially convolutional neural networks. A number of convolutional neural network-based methodologies have been proposed to accurately analyze histopathological images for cancer detection, risk prediction, and cancer subtype classification. Most existing methods have conducted patch-based examinations, due to the extremely large size of histopathological images. However, patches of a small window often do not contain sufficient information or patterns for the tasks of interest. It corresponds that pathologists also examine tissues at various magnification levels, while checking complex morphological patterns in a microscope. We propose a novel multi-task based deep learning model for HIstoPatholOgy (named Deep-Hipo) that takes multi-scale patches simultaneously for accurate histopathological image analysis. Deep-Hipo extracts two patches of the same size in both high and low magnification levels, and captures complex morphological patterns in both large and small receptive fields of a whole-slide image. Deep-Hipo has outperformed the current state-of-the-art deep learning methods. We assessed the proposed method in various types of whole-slide images of the stomach: well-differentiated, moderately-differentiated, and poorly-differentiated adenocarcinoma; poorly cohesive carcinoma, including signet-ring cell features; and normal gastric mucosa. The optimally trained model was also applied to histopathological images of The Cancer Genome Atlas (TCGA), Stomach Adenocarcinoma (TCGA-STAD) and TCGA Colon Adenocarcinoma (TCGA-COAD), which show similar pathological patterns with gastric carcinoma, and the experimental results were clinically verified by a pathologist. The source code of Deep-Hipo is publicly available at http://dataxlab.org/deep-hipo.

Kosaraju Sai Chandra, Hao Jie, Koh Hyun Min, Kang Mingon

2020-May-19

General General

Detecting Modeling Inconsistencies in SNOMED CT using a Machine Learning Technique.

In Methods (San Diego, Calif.)

SNOMED CT is a comprehensive and evolving clinical reference terminology that has been widely adopted as a common vocabulary to promote interoperability between Electronic Health Records. Owing to its importance in healthcare, quality assurance becomes an integral part of the lifecycle of SNOMED CT. While, manual auditing of every concept in SNOMED CT is difficult and labor intensive, identifying inconsistencies in the modeling of concepts without any context can be challenging. Algorithmic techniques are needed to identify modeling inconsistencies, if any, in SNOMED CT. This study proposes a context-based, machine learning quality assurance technique to identify concepts in SNOMED CT that may be in need of auditing. The Clinical Finding and the Procedure hierarchies are used as a testbed to check the efficacy of the method. Results of auditing show that the method identified inconsistencies in 72% of the concept pairs that were deemed inconsistent by the algorithm. The method is shown to be effective in both maximizing the yield of correction, as well as providing a context to identify the inconsistencies. Such methods, along with SNOMED International's own efforts, can greatly help reduce inconsistencies in SNOMED CT.

Agrawal Ankur, Qazi Kashifuddin

2020-May-19

Contextual Auditing, Lexical Analysis, Machine Learning, Quality Assurance, SNOMED CT

Surgery Surgery

Three-dimensional acquisition technologies for facial soft tissues - Applications and prospects in orthognathic surgery.

In Journal of stomatology, oral and maxillofacial surgery

The management of patients with dento-maxillofacial deformities is based on assessments of the dental occlusion - facial skeleton - soft tissues triad. As societal demands and surgical practices have evolved, facial soft tissues have moved to the forefront of considerations in orthognathic surgery. Techniques are therefore required to analyze facial soft tissues objectively and reproducibly, for diagnosis, preoperative planning, and follow-up. Several technologies are currently capable of providing three-dimensional (3D) models of the face, either by 3D reconstruction of traditional computed tomography or cone beam computed tomography data, or directly by stereophotogrammetry, laser scanning or structured light scanning. Multimodal image registration techniques allow bone base, dental occlusion and facial soft tissue information to be combined in a 3D virtual patient. Three-dimensional cephalometric analysis of the facial skeleton and skin is now perfectly integrated in virtual planning and is gradually gaining in automation and accuracy. Photorealistic 3D simulations allow optimal soft tissue planning and facilitate physician-patient communication. Finally, these facial modeling techniques facilitate post-operative studies of soft tissues, which generally involve comparisons of volumetric data. There are many research avenues to pursue and technical improvements are to be expected, particularly through the development of big data and artificial intelligence approaches.

Rasteau S, Sigaux N, Louvrier A, Bouletreau P

2020-May-19

Artificial intelligence, Cephalometric, Orthognathic surgery, Soft tissues, Surgical simulation, Three-dimensional imaging

General General

B·RIGHT: usability and satisfaction with a mobile app for self-managing emotional crises in patients with borderline personality disorder.

In Australasian psychiatry : bulletin of Royal Australian and New Zealand College of Psychiatrists

OBJECTIVE : Borderline personality disorder (BPD) is a severe mental disorder characterized by emotional crises. To date, crisis interventions for BPD have been conducted via telephone calls and emergency units, which are associated with an extra amount of resources. The aim of this research was to test the usability and satisfaction with a psychotherapeutic mobile app for self-managing crises in BPD.

METHOD : The B·RIGHT app was designed based on Artificial Intelligence psychotherapeutic algorithms. Usability and satisfaction with the app were assessed in 25 outpatients diagnosed with BPD (84% female, mean age = 35.80 years) using the System Usability Scale (SUS) and other questionnaires. Clinical features were assessed using the Borderline Symptom List, the Difficulties in Emotion Regulation Scale and Beck's Depression Inventory.

RESULTS : Patients with BPD considered the app user-friendly (mean total score = 4.03) and highly satisfactory (mean total score = 4.02), resulting in a positive user experience (mean total score = 4.09). Total usability was negatively associated with age (r = -.44), positively associated with educational level (rho = .47) and with overall emotion dysregulation (r = .51), and negatively associated with depression severity (r = -.47).

CONCLUSIONS : The usability and satisfaction testing of the B·RIGHT app showed promising findings, which warrant further research in order to validate its effectiveness.

Frías Álvaro, Palma Carol, Salvador Ana, Aluco Elena, Navarro Sara, Farriols Núria, Aliaga Ferrán, Solves Laia, Antón Meritxell

2020-May-22

borderline personality disorder, emotional crises, mobile app, satisfaction, usability

General General

Deep learning from label proportions with labeled samples.

In Neural networks : the official journal of the International Neural Network Society

Learning from label proportions (LLP), where the training data is in form of bags, and only the proportions of classes in each bag are available, has attracted wide interest in machine learning community. In general, most LLP algorithms adopt random sampling to obtain the proportional information of different categories, which correspondingly obtains some labeled samples in each bag. However, LLP training process always fails to leverage these labeled samples, which may contain essential data distribution information. To address this issue, in this paper, we propose end-to-end LLP solver based on convolutional neural networks (ConvNets), called LLP with labeled samples (LLP-LS). First, we reshape the cross entropy loss in ConvNets, so that it can combine the proportional information and labeled samples in each bag. Second, in order to comply with the training data in a bag manner, ADAM based on batch is employed to train LLP-LS. Hence, the batch size in training process is in accordance with the bag size. Compared with up-to-date methods on multi-class problem, our algorithm can obtain the state-of-the-art on several image datasets.

Shi Yong, Liu Jiabin, Wang Bo, Qi Zhiquan, Tian YingJie

2020-May-07

Convolutional neural networks (convNets), Learning from label proportions (LLP), Multi-class problem, Random sampling

General General

Robust image classification against adversarial attacks using elastic similarity measures between edge count sequences.

In Neural networks : the official journal of the International Neural Network Society

Due to their unprecedented capacity to learn patterns from raw data, deep neural networks have become the de facto modeling choice to address complex machine learning tasks. However, recent works have emphasized the vulnerability of deep neural networks when being fed with intelligently manipulated adversarial data instances tailored to confuse the model. In order to overcome this issue, a major effort has been made to find methods capable of making deep learning models robust against adversarial inputs. This work presents a new perspective for improving the robustness of deep neural networks in image classification. In computer vision scenarios, adversarial images are crafted by manipulating legitimate inputs so that the target classifier is eventually fooled, but the manipulation is not visually distinguishable by an external observer. The reason for the imperceptibility of the attack is that the human visual system fails to detect minor variations in color space, but excels at detecting anomalies in geometric shapes. We capitalize on this fact by extracting color gradient features from input images at multiple sensitivity levels to detect possible manipulations. We resort to a deep neural classifier to predict the category of unseen images, whereas a discrimination model analyzes the extracted color gradient features with time series techniques to determine the legitimacy of input images. The performance of our method is assessed over experiments comprising state-of-the-art techniques for crafting adversarial attacks. Results corroborate the increased robustness of the classifier when using our discrimination module, yielding drastically reduced success rates of adversarial attacks that operate on the whole image rather than on localized regions or around the existing shapes of the image. Future research is outlined towards improving the detection accuracy of the proposed method for more general attack strategies.

Oregi Izaskun, Del Ser Javier, Pérez Aritz, Lozano José A

2020-Apr-30

Adversarial machine learning, Computer vision, Deep neural networks, Time series analysis

oncology Oncology

SYNERGxDB: an integrative pharmacogenomic portal to identify synergistic drug combinations for precision oncology.

In Nucleic acids research ; h5-index 217.0

Drug-combination data portals have recently been introduced to mine huge amounts of pharmacological data with the aim of improving current chemotherapy strategies. However, these portals have only been investigated for isolated datasets, and molecular profiles of cancer cell lines are lacking. Here we developed a cloud-based pharmacogenomics portal called SYNERGxDB (http://SYNERGxDB.ca/) that integrates multiple high-throughput drug-combination studies with molecular and pharmacological profiles of a large panel of cancer cell lines. This portal enables the identification of synergistic drug combinations through harmonization and unified computational analysis. We integrated nine of the largest drug combination datasets from both academic groups and pharmaceutical companies, resulting in 22 507 unique drug combinations (1977 unique compounds) screened against 151 cancer cell lines. This data compendium includes metabolomics, gene expression, copy number and mutation profiles of the cancer cell lines. In addition, SYNERGxDB provides analytical tools to discover effective therapeutic combinations and predictive biomarkers across cancer, including specific types. Combining molecular and pharmacological profiles, we systematically explored the large space of univariate predictors of drug synergism. SYNERGxDB constitutes a comprehensive resource that opens new avenues of research for exploring the mechanism of action for drug synergy with the potential of identifying new treatment strategies for cancer patients.

Seo Heewon, Tkachuk Denis, Ho Chantal, Mammoliti Anthony, Rezaie Aria, Madani Tonekaboni Seyed Ali, Haibe-Kains Benjamin

2020-May-22

General General

Measuring relative opinion from location-based social media: A case study of the 2016 U.S. presidential election.

In PloS one ; h5-index 176.0

Social media has become an emerging alternative to opinion polls for public opinion collection, while it is still posing many challenges as a passive data source, such as structurelessness, quantifiability, and representativeness. Social media data with geotags provide new opportunities to unveil the geographic locations of users expressing their opinions. This paper aims to answer two questions: 1) whether quantifiable measurement of public opinion can be obtained from social media and 2) whether it can produce better or complementary measures compared to opinion polls. This research proposes a novel approach to measure the relative opinion of Twitter users towards public issues in order to accommodate more complex opinion structures and take advantage of the geography pertaining to the public issues. To ensure that this new measure is technically feasible, a modeling framework is developed including building a training dataset by adopting a state-of-the-art approach and devising a new deep learning method called Opinion-Oriented Word Embedding. With a case study of tweets on the 2016 U.S. presidential election, we demonstrate the predictive superiority of our relative opinion approach and we show how it can aid visual analytics and support opinion predictions. Although the relative opinion measure is proved to be more robust than polling, our study also suggests that the former can advantageously complement the latter in opinion prediction.

Gong Zhaoya, Cai Tengteng, Thill Jean-Claude, Hale Scott, Graham Mark

2020

Radiology Radiology

Computed tomography-derived radiomic signature of head and neck squamous cell carcinoma (peri)tumoral tissue for the prediction of locoregional recurrence and distant metastasis after concurrent chemo-radiotherapy.

In PloS one ; h5-index 176.0

INTRODUCTION : In this study, we investigate the role of radiomics for prediction of overall survival (OS), locoregional recurrence (LRR) and distant metastases (DM) in stage III and IV HNSCC patients treated by chemoradiotherapy. We hypothesize that radiomic analysis of (peri-)tumoral tissue may detect invasion of surrounding tissues indicating a higher chance of locoregional recurrence and distant metastasis.

METHODS : Two comprehensive data sources were used: the Dutch Cancer Society Database (Alp 7072, DESIGN) and "Big Data To Decide" (BD2Decide). The gross tumor volumes (GTV) were delineated on contrast-enhanced CT. Radiomic features were extracted using the RadiomiX Discovery Toolbox (OncoRadiomics, Liege, Belgium). Clinical patient features such as age, gender, performance status etc. were collected. Two machine learning methods were chosen for their ability to handle censored data: Cox proportional hazards regression and random survival forest (RSF). Multivariable clinical and radiomic Cox/ RSF models were generated based on significance in univariable cox regression/ RSF analyses on the held out data in the training dataset. Features were selected according to a decreasing hazard ratio for Cox and relative importance for RSF.

RESULTS : A total of 444 patients with radiotherapy planning CT-scans were included in this study: 301 head and neck squamous cell carcinoma (HNSCC) patients in the training cohort (DESIGN) and 143 patients in the validation cohort (BD2DECIDE). We found that the highest performing model was a clinical model that was able to predict distant metastasis in oropharyngeal cancer cases with an external validation C-index of 0.74 and 0.65 with the RSF and Cox models respectively. Peritumoral radiomics based prediction models performed poorly in the external validation, with C-index values ranging from 0.32 to 0.61 utilizing both feature selection and model generation methods.

CONCLUSION : Our results suggest that radiomic features from the peritumoral regions are not useful for the prediction of time to OS, LR and DM.

Keek Simon, Sanduleanu Sebastian, Wesseling Frederik, de Roest Reinout, van den Brekel Michiel, van der Heijden Martijn, Vens Conchita, Giuseppina Calareso, Licitra Lisa, Scheckenbach Kathrin, Vergeer Marije, Leemans C René, Brakenhoff Ruud H, Nauta Irene, Cavalieri Stefano, Woodruff Henry C, Poli Tito, Leijenaar Ralph, Hoebers Frank, Lambin Philippe

2020

General General

Technical Metrics Used to Evaluate Health Care Chatbots: A Scoping Review.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Dialog agents (chatbots) have a long history of application in health care, where they have been used for tasks such as supporting patient self-management and providing counseling. Their use is expected to grow with increasing demands on health systems and improving artificial intelligence (AI) capability. Approaches to the evaluation of health care chatbots, however, appear to be diverse and haphazard, resulting in a potential barrier to the advancement of the field.

OBJECTIVE : This study aims to identify the technical (nonclinical) metrics used by previous studies to evaluate health care chatbots.

METHODS : Studies were identified by searching 7 bibliographic databases (eg, MEDLINE and PsycINFO) in addition to conducting backward and forward reference list checking of the included studies and relevant reviews. The studies were independently selected by two reviewers who then extracted data from the included studies. Extracted data were synthesized narratively by grouping the identified metrics into categories based on the aspect of chatbots that the metrics evaluated.

RESULTS : Of the 1498 citations retrieved, 65 studies were included in this review. Chatbots were evaluated using 27 technical metrics, which were related to chatbots as a whole (eg, usability, classifier performance, speed), response generation (eg, comprehensibility, realism, repetitiveness), response understanding (eg, chatbot understanding as assessed by users, word error rate, concept error rate), and esthetics (eg, appearance of the virtual agent, background color, and content).

CONCLUSIONS : The technical metrics of health chatbot studies were diverse, with survey designs and global usability metrics dominating. The lack of standardization and paucity of objective measures make it difficult to compare the performance of health chatbots and could inhibit advancement of the field. We suggest that researchers more frequently include metrics computed from conversation logs. In addition, we recommend the development of a framework of technical metrics with recommendations for specific circumstances for their inclusion in chatbot studies.

Abd-Alrazaq Alaa, Safi Zeineb, Alajlani Mohannad, Warren Jim, Househ Mowafa, Denecke Kerstin

2020-Apr-15

General General

Testing Suicide Risk Prediction Algorithms Using Phone Measurements with Patients in Acute Mental Health Settings: Feasibility Study.

In JMIR mHealth and uHealth

BACKGROUND : Digital phenotyping and machine learning are currently being used to augment or even replace traditional analytic procedures in many domains, including health care. Given the heavy reliance on smartphones and mobile devices around the world, this readily available source of data is an important and highly underutilized source that has the potential to improve mental health risk prediction and prevention and advance mental health globally.

OBJECTIVE : This study aimed to apply machine learning in an acute mental health setting for suicide risk prediction. This study uses a nascent approach, adding to existing knowledge by using data collected through a smartphone in place of clinical data, which have typically been collected from health care records.

METHODS : We created a smartphone app called Strength Within Me, which was linked to Fitbit, Apple Health kit, and Facebook, to collect salient clinical information such as sleep behavior and mood, step frequency and count, and engagement patterns with the phone from a cohort of inpatients with acute mental health (n=66). In addition, clinical research interviews were used to assess mood, sleep, and suicide risk. Multiple machine learning algorithms were tested to determine the best fit.

RESULTS : K-nearest neighbors (KNN; k=2) with uniform weighting and the Euclidean distance metric emerged as the most promising algorithm, with 68% mean accuracy (averaged over 10,000 simulations of splitting the training and testing data via 10-fold cross-validation) and an average area under the curve of 0.65. We applied a combined 5×2 F test to test the model performance of KNN against the baseline classifier that guesses training majority, random forest, support vector machine and logistic regression, and achieved F statistics of 10.7 (P=.009) and 17.6 (P=.003) for training majority and random forest, respectively, rejecting the null of performance being the same. Therefore, we have taken the first steps in prototyping a system that could continuously and accurately assess the risk of suicide via mobile devices.

CONCLUSIONS : Predicting for suicidality is an underaddressed area of research to which this paper makes a useful contribution. This is part of the first generation of studies to suggest that it is feasible to utilize smartphone-generated user input and passive sensor data to generate a risk algorithm among inpatients at suicide risk. The model reveals fair concordance between phone-derived and research-generated clinical data, and with iterative development, it has the potential for accurate discriminant risk prediction. However, although full automation and independence of clinical judgment or input would be a worthy development for those individuals who are less likely to access specialist mental health services, and for providing a timely response in a crisis situation, the ethical and legal implications of such advances in the field of psychiatry need to be acknowledged.

Haines-Delmont Alina, Chahal Gurdit, Bruen Ashley Jane, Wall Abbie, Khan Christina Tara, Sadashiv Ramesh, Fearnley David

2020-Feb-29

General General

Predictive Modeling for Frailty Conditions in Elderly People: Machine Learning Approaches.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : Frailty is one of the most critical age-related conditions in older adults. It is often recognized as a syndrome of physiological decline in late life, characterized by a marked vulnerability to adverse health outcomes. A clear operational definition of frailty, however, has not been agreed so far. There is a wide range of studies on the detection of frailty and their association with mortality. Several of these studies have focused on the possible risk factors associated with frailty in the elderly population while predicting who will be at increased risk of frailty is still overlooked in clinical settings.

OBJECTIVE : The objective of our study was to develop predictive models for frailty conditions in older people using different machine learning methods based on a database of clinical characteristics and socioeconomic factors.

METHODS : An administrative health database containing 1,095,612 elderly people aged 65 or older with 58 input variables and 6 output variables was used. We first identify and define six problems/outputs as surrogates of frailty. We then resolve the imbalanced nature of the data through resampling process and a comparative study between the different machine learning (ML) algorithms - Artificial neural network(ANN), Genetic programming (GP), Support vector machines (SVM), Random Forest (RF), Logistic regression (LR) and Decision tree (DT) - was carried out. The performance of each model was evaluated using a separate unseen dataset.

RESULTS : Predicting mortality outcome has shown higher performance with ANN (TPR 0.81, TNR 0.76, accuracy 0.78, F1-score 0.79) and SVM (TPR 0.77, TNR 0.80, accuracy 0.79, F1-score 0.78) than predicting the other outcomes. On average, over the six problems, the DT classifier has shown the lowest accuracy, while other models (GP, LR, RF, ANN, and SVM) performed better. All models have shown lower accuracy in predicting an event of an emergency admission with red code than predicting fracture and disability. In predicting urgent hospitalization, only SVM achieved better performance (TPR 0.75, TNR 0.77, accuracy 0.73, F1-score 0.76) with the 10-fold cross validation compared with other models in all evaluation metrics.

CONCLUSIONS : We developed machine learning models for predicting frailty conditions (mortality, urgent hospitalization, disability, fracture, and emergency admission). The results show that the prediction performance of machine learning models significantly varies from problem to problem in terms of different evaluation metrics. Through further improvement, the model that performs better can be used as a base for developing decision-support tools to improve early identification and prediction of frail older adults.

Tarekegn Adane, Ricceri Fulvio, Costa Giuseppe, Ferracin Elisa, Giacobini Mario

2020-Feb-16

General General

Patient Perception of Plain-Language Medical Notes Generated Using Artificial Intelligence Software: Pilot Mixed-Methods Study.

In JMIR formative research

BACKGROUND : Clinicians' time with patients has become increasingly limited due to regulatory burden, documentation and billing, administrative responsibilities, and market forces. These factors limit clinicians' time to deliver thorough explanations to patients. OpenNotes began as a research initiative exploring the ability of sharing medical notes with patients to help patients understand their health care. Providing patients access to their medical notes has been shown to have many benefits, including improved patient satisfaction and clinical outcomes. OpenNotes has since evolved into a national movement that helps clinicians share notes with patients. However, a significant barrier to the widespread adoption of OpenNotes has been clinicians' concerns that OpenNotes may cost additional time to correct patient confusion over medical language. Recent advances in artificial intelligence (AI) technology may help resolve this concern by converting medical notes to plain language with minimal time required of clinicians.

OBJECTIVE : This pilot study assesses patient comprehension and perceived benefits, concerns, and insights regarding an AI-simplified note through comprehension questions and guided interview.

METHODS : Synthea, a synthetic patient generator, was used to generate a standardized medical language which was then simplified using AI software. A multiple-choice comprehension assessment questionnaire was drafted with physician input. Study participants were recruited from inpatients at the University of Colorado Hospital. Participants were randomly assigned to be tested for their comprehension of the standardized medical language version or AI-generated plain-language version of the patient note. Following this, participants reviewed the opposite version of the note and participated in a guided interview. A Student t test was performed to assess for differences in comprehension assessment scores between plain-language and medical-language note groups. Multivariate modeling was performed to assess the impact of demographic variables on comprehension. Interview responses were thematically analyzed.

RESULTS : Twenty patients agreed to participate. The mean number of comprehension assessment questions answered correctly was found to be higher in the plain-language group compared with the medical-language group; however, the Student t test was found to be underpowered to determine if this was significant. Age, ethnicity, and health literacy were found to have a significant impact on comprehension scores by multivariate modeling. Thematic analysis of guided interviews highlighted patients' perceived benefits, concerns, and suggestions regarding such notes. Major themes of benefits were that simplified plain-language notes may (1) be more useable than unsimplified medical-language notes, (2) improve the patient-clinician relationship, and (3) empower patients through an enhanced understanding of their health care.

CONCLUSIONS : AI software may translate medical notes into plain-language notes that are perceived as beneficial by patients. Limitations included sample size, inpatient-only setting, and possible confounding factors. Larger studies are needed to assess comprehension. Insight from patient responses to guided interviews can guide the future study and development of this technology.

Bala Sandeep, Keniston Angela, Burden Marisha

2020-Mar-29

General General

Effect of Speech Recognition for Consumer Digital Health Tasks Related to Problem Solving and Recall: Controlled Laboratory Experiment.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Recent advances in natural language processing and artificial intelligence have led to widespread adoption of speech recognition technologies. In consumer health applications, speech recognition is usually applied to support interactions with conversational agents for data collection, decision support, and patient monitoring. However, little is known about the use of speech recognition in consumer health applications and few studies have evaluated the efficacy of conversational agents in the hands of consumers. In other consumer-facing tools, cognitive load has been observed to be an important factor affecting the use of speech recognition technologies in tasks involving problem solving and recall. Users find it more difficult to think and speak at the same time when compared to typing, pointing, and clicking. However, the effects of speech recognition on cognitive load when performing health tasks has not yet been explored.

OBJECTIVE : The aim of this study was to evaluate the use of speech recognition for documentation in consumer digital health tasks involving problem solving and recall.

METHODS : Fifty university staff and students were recruited to undertake four documentation tasks with a simulated conversational agent in a computer laboratory. The tasks varied in complexity determined by the amount of problem solving and recall required (simple and complex) and the input modality (speech recognition vs keyboard and mouse). Cognitive load, task completion time, error rate, and usability were measured.

RESULTS : Compared to using a keyboard and mouse, speech recognition significantly increased the cognitive load for complex tasks (Z=-4.08, P<.001) and simple tasks (Z=-2.24, P=.03). Complex tasks took significantly longer to complete (Z=-2.52, P=.01) and speech recognition was found to be overall less usable than a keyboard and mouse (Z=-3.30, P=.001). However, there was no effect on errors.

CONCLUSIONS : Use of a keyboard and mouse was preferable to speech recognition for complex tasks involving problem solving and recall. Further studies using a broader variety of consumer digital health tasks of varying complexity are needed to investigate the contexts in which use of speech recognition is most appropriate. The effects of cognitive load on task performance and its significance also need to be investigated.

Chen Jessica, Lyell David, Laranjo Liliana, Magrabi Farah

2020-Mar-29

General General

Robust Sampling of Defective Pathways in Alzheimer's Disease. Implications in Drug Repositioning.

In International journal of molecular sciences ; h5-index 102.0

We present the analysis of the defective genetic pathways of the Late-Onset Alzheimer's Disease (LOAD) compared to the Mild Cognitive Impairment (MCI) and Healthy Controls (HC) using different sampling methodologies. These algorithms sample the uncertainty space that is intrinsic to any kind of highly underdetermined phenotype prediction problem, by looking for the minimum-scale signatures (header genes) corresponding to different random holdouts. The biological pathways can be identified performing posterior analysis of these signatures established via cross-validation holdouts and plugging the set of most frequently sampled genes into different ontological platforms. That way, the effect of helper genes, whose presence might be due to the high degree of under determinacy of these experiments and data noise, is reduced. Our results suggest that common pathways for Alzheimer's disease and MCI are mainly related to viral mRNA translation, influenza viral RNA transcription and replication, gene expression, mitochondrial translation, and metabolism, with these results being highly consistent regardless of the comparative methods. The cross-validated predictive accuracies achieved for the LOAD and MCI discriminations were 84% and 81.5%, respectively. The difference between LOAD and MCI could not be clearly established (74% accuracy). The most discriminatory genes of the LOAD-MCI discrimination are associated with proteasome mediated degradation and G-protein signaling. Based on these findings we have also performed drug repositioning using Dr. Insight package, proposing the following different typologies of drugs: isoquinoline alkaloids, antitumor antibiotics, phosphoinositide 3-kinase PI3K, autophagy inhibitors, antagonists of the&nbsp;muscarinic acetylcholine receptor and histone deacetylase inhibitors. We believe that the potential clinical relevance of these findings should be further investigated and confirmed with other independent studies.

Fernández-Martínez Juan Luis, Álvarez-Machancoses Óscar, de Andrés-Galiana Enrique J, Bea Guillermina, Kloczkowski Andrzej

2020-May-19

Alzheimer’s Disease, Deep Pathways Sampling, Drug repositioning, Mild Cognitive Impairment, Pathway analysis

General General

Deep learning segmentation of gadolinium-enhancing lesions in multiple sclerosis.

In Multiple sclerosis (Houndmills, Basingstoke, England)

OBJECTIVE : The aim of this study is to assess the performance of deep learning convolutional neural networks (CNNs) in segmenting gadolinium-enhancing lesions using a large cohort of multiple sclerosis (MS) patients.

METHODS : A three-dimensional (3D) CNN model was trained for segmentation of gadolinium-enhancing lesions using multispectral magnetic resonance imaging data (MRI) from 1006 relapsing-remitting MS patients. The network performance was evaluated for three combinations of multispectral MRI used as input: (U5) fluid-attenuated inversion recovery (FLAIR), T2-weighted, proton density-weighted, and pre- and post-contrast T1-weighted images; (U2) pre- and post-contrast T1-weighted images; and (U1) only post-contrast T1-weighted images. Segmentation performance was evaluated using the Dice similarity coefficient (DSC) and lesion-wise true-positive (TPR) and false-positive (FPR) rates. Performance was also evaluated as a function of enhancing lesion volume.

RESULTS : The DSC/TPR/FPR values averaged over all the enhancing lesion sizes were 0.77/0.90/0.23 using the U5 model. These values for the largest enhancement volumes (>500 mm3) were 0.81/0.97/0.04. For U2, the average DSC/TPR/FPR values were 0.72/0.86/0.31. Comparable performance was observed with U1. For all types of input, the network performance degraded with decreased enhancement size.

CONCLUSION : Excellent segmentation of enhancing lesions was observed for enhancement volume ⩾70 mm3. The best performance was achieved when the input included all five multispectral image sets.

Coronado Ivan, Gabr Refaat E, Narayana Ponnada A

2020-May-22

Convolutional neural networks, MRI, active lesions, artificial intelligence, false positive, white matter lesions

Cardiology Cardiology

Electrocardiography (ECG) analysis and a new feature extraction method using wavelet transform with scalogram analysis.

In Biomedizinische Technik. Biomedical engineering

Electrocardiography (ECG) signals and the information obtained through the analysis of these signals constitute the main source of diagnosis for many cardiovascular system diseases. Therefore, accurate analyses of ECG signals are very important for correct diagnosis. In this study, an ECG analysis toolbox together with a user-friendly graphical user interface, which contains the all ECG analysis steps between the recording unit and the statistical investigation, is developed. Furthermore, a new feature calculation methodology is proposed for ECG analysis, which carries distinct information than amplitudes and durations of ECG main waves and can be used in artificial intelligence studies. Developed toolbox is tested using both Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia ECG Database and an experimentally collected dataset for performance evaluation. The results show that ECG analysis toolbox presented in this study increases the accuracy and reliability of the ECG main wave detection analysis, highly fasten the process duration compared to manual ones and the new feature set can be used as a new parameter for decision support systems about ECG based on artificial intelligence.

Yanık Hüseyin, Değirmenci Evren, Büyükakıllı Belgin, Karpuz Derya, Kılınç Olgu Hallıoğlu, Gürgül Serkan

2020-May-22

denoising, electrocardiography, feature extraction, pulmonary arterial hypertension, scalogram

General General

Distinguishing Obstructive Versus Central Apneas in Infrared Video of Sleep Using Deep Learning: Validation Study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Sleep apnea is a respiratory disorder characterized by an intermittent reduction (hypopnea) or cessation (apnea) of breathing during sleep. Depending on the presence of a breathing effort, sleep apnea is divided into obstructive sleep apnea (OSA) and central sleep apnea (CSA) based on the different pathologies involved. If the majority of apneas in a person are obstructive, they will be diagnosed as OSA or otherwise as CSA. In addition, as it is challenging and highly controversial to divide hypopneas into central or obstructive, the decision about sleep apnea type (OSA vs CSA) is made based on apneas only. Choosing the appropriate treatment relies on distinguishing between obstructive apnea (OA) and central apnea (CA).

OBJECTIVE : The objective of this study was to develop a noncontact method to distinguish between OAs and CAs.

METHODS : Five different computer vision-based algorithms were used to process infrared (IR) video data to track and analyze body movements to differentiate different types of apnea (OA vs CA). In the first two methods, supervised classifiers were trained to process optical flow information. In the remaining three methods, a convolutional neural network (CNN) was designed to extract distinctive features from optical flow and to distinguish OA from CA.

RESULTS : Overnight sleeping data of 42 participants (mean age 53, SD 15 years; mean BMI 30, SD 7 kg/m2; 27 men and 15 women; mean number of OA 16, SD 30; mean number of CA 3, SD 7; mean apnea-hypopnea index 27, SD 31 events/hour; mean sleep duration 5 hours, SD 1 hour) were collected for this study. The test and train data were recorded in two separate laboratory rooms. The best-performing model (3D-CNN) obtained 95% accuracy and an F1 score of 89% in differentiating OA vs CA.

CONCLUSIONS : In this study, the first vision-based method was developed that differentiates apnea types (OA vs CA). The developed algorithm tracks and analyses chest and abdominal movements captured via an IR video camera. Unlike previously developed approaches, this method does not require any attachment to a user that could potentially alter the sleeping condition.

Akbarian Sina, Montazeri Ghahjaverestan Nasim, Yadollahi Azadeh, Taati Babak

2020-May-22

central apnea, computer vision, deep learning, machine learning, motion analysis, noncontact monitoring, obstructive apnea, sleep apnea

General General

Integrating data mining and transmission theory in the ecology of infectious diseases.

In Ecology letters

Our understanding of ecological processes is built on patterns inferred from data. Applying modern analytical tools such as machine learning to increasingly high dimensional data offers the potential to expand our perspectives on these processes, shedding new light on complex ecological phenomena such as pathogen transmission in wild populations. Here, we propose a novel approach that combines data mining with theoretical models of disease dynamics. Using rodents as an example, we incorporate statistical differences in the life history features of zoonotic reservoir hosts into pathogen transmission models, enabling us to bound the range of dynamical phenomena associated with hosts, based on their traits. We then test for associations between equilibrium prevalence, a key epidemiological metric and data on human outbreaks of rodent-borne zoonoses, identifying matches between empirical evidence and theoretical predictions of transmission dynamics. We show how this framework can be generalized to other systems through a rubric of disease models and parameters that can be derived from empirical data. By linking life history components directly to their effects on disease dynamics, our mining-modelling approach integrates machine learning and theoretical models to explore mechanisms in the macroecology of pathogen transmission and their consequences for spillover infection to humans.

Han Barbara A, O’Regan Suzanne M, Paul Schmidt John, Drake John M

2020-May-22

Boosted regression, disease dynamics, disease macroecology, pathogen transmission, random forest, statistical learning, zoonosis, zoonotic spillover

General General

How to Be Helpful to Multiple People at Once.

In Cognitive science

When someone hosts a party, when governments choose an aid program, or when assistive robots decide what meal to serve to a family, decision-makers must determine how to help even when their recipients have very different preferences. Which combination of people's desires should a decision-maker serve? To provide a potential answer, we turned to psychology: What do people think is best when multiple people have different utilities over options? We developed a quantitative model of what people consider desirable behavior, characterizing participants' preferences by inferring which combination of "metrics" (maximax, maxsum, maximin, or inequality aversion [IA]) best explained participants' decisions in a drink-choosing task. We found that participants' behavior was best described by the maximin metric, describing the desire to maximize the happiness of the worst-off person, though participant behavior was also consistent with maximizing group utility (the maxsum metric) and the IA metric to a lesser extent. Participant behavior was consistent across variation in the agents involved and  tended to become more maxsum-oriented when participants were told they were players in the task (Experiment 1). In later experiments, participants maintained maximin behavior across multi-step tasks rather than shortsightedly focusing on the individual steps therein (Experiment 2, Experiment 3). By repeatedly asking participants what choices they would hope for in an optimal, just decision-maker, and carefully disambiguating which quantitative metrics describe these nuanced choices, we help constrain the space of what behavior we desire in leaders, artificial intelligence systems helping decision-makers, and the assistive robots and decision-makers of the future.

Gates Vael, Griffiths Thomas L, Dragan Anca D

2020-Jun

Assistive artificial intelligence, Fairness, Maximin, Modeling, Preferences

General General

High-Content Phenotypic Profiling in Esophageal Adenocarcinoma Identifies Selectively Active Pharmacological Classes of Drugs for Repurposing and Chemical Starting Points for Novel Drug Discovery.

In SLAS discovery : advancing life sciences R & D

Esophageal adenocarcinoma (EAC) is a highly heterogeneous disease, dominated by large-scale genomic rearrangements and copy number alterations. Such characteristics have hampered conventional target-directed drug discovery and personalized medicine strategies, contributing to poor outcomes for patients. We describe the application of a high-content Cell Painting assay to profile the phenotypic response of 19,555 compounds across a panel of six EAC cell lines and two tissue-matched control lines. We built an automated high-content image analysis pipeline to identify compounds that selectively modified the phenotype of EAC cell lines. We further trained a machine-learning model to predict the mechanism of action of EAC selective compounds using phenotypic fingerprints from a library of reference compounds. We identified a number of phenotypic clusters enriched with similar pharmacological classes, including methotrexate and three other antimetabolites that are highly selective for EAC cell lines. We further identify a small number of hits from our diverse chemical library that show potent and selective activity for EAC cell lines and that do not cluster with the reference library of compounds, indicating they may be selectively targeting novel esophageal cancer biology. Overall, our results demonstrate that our EAC phenotypic screening platform can identify existing pharmacologic classes and novel compounds with selective activity for EAC cell phenotypes.

Hughes Rebecca E, Elliott Richard J R, Munro Alison F, Makda Ashraff, O’Neill J Robert, Hupp Ted, Carragher Neil O

2020-May-22

esophageal adenocarcinoma, high content, machine learning, mechanism of action, phenotypic

General General

Evaluation of the diagnostic value of joint PET myocardial perfusion and metabolic imaging for vascular stenosis in patients with obstructive coronary artery disease.

In Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology

BACKGROUND : To investigate the diagnostic value of joint PET myocardial perfusion and metabolic imaging for vascular stenosis in patients with suspected obstructive coronary artery disease (CAD).

METHODS : Eighty-eight patients (53 and 35 applied for training and validation, respectively) with suspected obstructive CAD were referred to 13N-NH3 PET/CT myocardial perfusion imaging (MPI) and 18F-FDG PET/CT myocardial metabolic imaging (MMI) with available coronary angiography for analysis. One semi-quantitative indicator summed rest score (SRS) and five quantitative indicators, namely, perfusion defect extent (EXT), total perfusion deficit (TPD), myocardial blood flow (MBF), scar degree (SCR), and metabolism-perfusion mismatch (MIS), were extracted from the PET rest MPI and MMI scans. Different combinations of indicators and seven machine learning methods were used to construct diagnostic models. Diagnostic performance was evaluated using the sum of four metrics (noted as sumScore), namely, area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.

RESULTS : In univariate analysis, MIS outperformed other individual indicators in terms of sumScore (2.816-3.042 vs 2.138-2.908). In multivariate analysis, support vector machine (SVM) consisting of three indicators (MBF, SCR, and MIS) achieved the best performance (AUC 0.856, accuracy 0.810, sensitivity 0.838, specificity 0.757, and sumScore 3.261). This model consistently achieved significantly higher AUC compared with the SRS method for four specific subgroups (0.897, 0.839, 0.875, and 0.949 vs 0.775, 0.606, 0.713, and 0.744; P = 0.041, 0.005, 0.034 0.003, respectively).

CONCLUSIONS : The joint evaluation of PET rest MPI and MMI could improve the diagnostic performance for obstructive CAD. The multivariate model (MBF, SCR, and MIS) combined with SVM outperformed other methods.

Wang Fanghu, Xu Weiping, Lv Wenbing, Du Dongyang, Feng Hui, Zhang Xiaochun, Wang Shuxia, Chen Wufan, Lu Lijun

2020-May-21

Myocardial perfusion imaging, coronary artery disease, machine learning, myocardial metabolic imaging

Surgery Surgery

i3PosNet: instrument pose estimation from X-ray in temporal bone surgery.

In International journal of computer assisted radiology and surgery

PURPOSE : Accurate estimation of the position and orientation (pose) of surgical instruments is crucial for delicate minimally invasive temporal bone surgery. Current techniques lack in accuracy and/or line-of-sight constraints (conventional tracking systems) or expose the patient to prohibitive ionizing radiation (intra-operative CT). A possible solution is to capture the instrument with a c-arm at irregular intervals and recover the pose from the image.

METHODS : i3PosNet infers the position and orientation of instruments from images using a pose estimation network. Said framework considers localized patches and outputs pseudo-landmarks. The pose is reconstructed from pseudo-landmarks by geometric considerations.

RESULTS : We show i3PosNet reaches errors [Formula: see text] mm. It outperforms conventional image registration-based approaches reducing average and maximum errors by at least two thirds. i3PosNet trained on synthetic images generalizes to real X-rays without any further adaptation.

CONCLUSION : The translation of deep learning-based methods to surgical applications is difficult, because large representative datasets for training and testing are not available. This work empirically shows sub-millimeter pose estimation trained solely based on synthetic training data.

Kügler David, Sehring Jannik, Stefanov Andrei, Stenin Igor, Kristin Julia, Klenzner Thomas, Schipper Jörg, Mukhopadhyay Anirban

2020-May-21

Cochlear implant, Fluoroscopic tracking, Minimally invasive bone surgery, Modular deep learning, Vestibular schwannoma removal, instrument pose estimation

Radiology Radiology

Deep Multi-Scale 3D Convolutional Neural Network (CNN) for MRI Gliomas Brain Tumor Classification.

In Journal of digital imaging

Accurate and fully automatic brain tumor grading from volumetric 3D magnetic resonance imaging (MRI) is an essential procedure in the field of medical imaging analysis for full assistance of neuroradiology during clinical diagnosis. We propose, in this paper, an efficient and fully automatic deep multi-scale three-dimensional convolutional neural network (3D CNN) architecture for glioma brain tumor classification into low-grade gliomas (LGG) and high-grade gliomas (HGG) using the whole volumetric T1-Gado MRI sequence. Based on a 3D convolutional layer and a deep network, via small kernels, the proposed architecture has the potential to merge both the local and global contextual information with reduced weights. To overcome the data heterogeneity, we proposed a preprocessing technique based on intensity normalization and adaptive contrast enhancement of MRI data. Furthermore, for an effective training of such a deep 3D network, we used a data augmentation technique. The paper studied the impact of the proposed preprocessing and data augmentation on classification accuracy.Quantitative evaluations, over the well-known benchmark (Brats-2018), attest that the proposed architecture generates the most discriminative feature map to distinguish between LG and HG gliomas compared with 2D CNN variant. The proposed approach offers promising results outperforming the recently supervised and unsupervised state-of-the-art approaches by achieving an overall accuracy of 96.49% using the validation dataset. The obtained experimental results confirm that adequate MRI's preprocessing and data augmentation could lead to an accurate classification when exploiting CNN-based approaches.

Mzoughi Hiba, Njeh Ines, Wali Ali, Slima Mohamed Ben, BenHamida Ahmed, Mhiri Chokri, Mahfoudhe Kharedine Ben

2020-May-21

3D convolutional neural network (CNN), Classification, Deep learning, Gliomas, Magnetic resonance imaging (MRI)

General General

Machine Learning to Analyze Single-Case Data: A Proof of Concept.

In Perspectives on behavior science

Visual analysis is the most commonly used method for interpreting data from single-case designs, but levels of interrater agreement remain a concern. Although structured aids to visual analysis such as the dual-criteria (DC) method may increase interrater agreement, the accuracy of the analyses may still benefit from improvements. Thus, the purpose of our study was to (a) examine correspondence between visual analysis and models derived from different machine learning algorithms, and (b) compare the accuracy, Type I error rate and power of each of our models with those produced by the DC method. We trained our models on a previously published dataset and then conducted analyses on both nonsimulated and simulated graphs. All our models derived from machine learning algorithms matched the interpretation of the visual analysts more frequently than the DC method. Furthermore, the machine learning algorithms outperformed the DC method on accuracy, Type I error rate, and power. Our results support the somewhat unorthodox proposition that behavior analysts may use machine learning algorithms to supplement their visual analysis of single-case data, but more research is needed to examine the potential benefits and drawbacks of such an approach.

Lanovaz Marc J, Giannakakos Antonia R, Destras Océane

2020-Mar

AB design, Artificial intelligence, Error rate, Machine learning, Single-case design

Public Health Public Health

Drug Resistance Prediction Using Deep Learning Techniques on HIV-1 Sequence Data.

In Viruses ; h5-index 58.0

The fast replication rate and lack of repair mechanisms of human immunodeficiency virus (HIV) contribute to its high mutation frequency, with some mutations resulting in the evolution of resistance to antiretroviral therapies (ART). As such, studying HIV drug resistance allows for real-time evaluation of evolutionary mechanisms. Characterizing the biological process of drug resistance is also critically important for sustained effectiveness of ART. Investigating the link between "black box" deep learning methods applied to this problem and evolutionary principles governing drug resistance has been overlooked to date. Here, we utilized publicly available HIV-1 sequence data and drug resistance assay results for 18 ART drugs to evaluate the performance of three architectures (multilayer perceptron, bidirectional recurrent neural network, and convolutional neural network) for drug resistance prediction, jointly with biological analysis. We identified convolutional neural networks as the best performing architecture and displayed a correspondence between the importance of biologically relevant features in the classifier and overall performance. Our results suggest that the high classification performance of deep learning models is indeed dependent on drug resistance mutations (DRMs). These models heavily weighted several features that are not known DRM locations, indicating the utility of model interpretability to address causal relationships in viral genotype-phenotype data.

Steiner Margaret C, Gibson Keylie M, Crandall Keith A

2020-May-19

HIV, HIV drug resistance, antiretroviral therapy, deep learning, machine learning, neural networks

Public Health Public Health

Rapid antibiotic susceptibility testing based on bacterial motion patterns with long short-term memory neural networks.

In IEEE sensors journal

Antibiotic resistance is an increasing public health threat. To combat it, a fast method to determine the antibiotic susceptibility of infecting pathogens is required. Here we present an optical imaging-based method to track the motion of single bacterial cells and generate a model to classify active and inactive cells based on the motion patterns of the individual cells. The model includes an image-processing algorithm to segment individual bacterial cells and track the motion of the cells over time, and a deep learning algorithm (Long Short-Term Memory network) to learn and determine if a bacterial cell is active or inactive. By applying the model to human urine specimens spiked with an Escherichia coli lab strain, we show that the method can accurately perform antibiotic susceptibility testing as fast as 30 minutes for five commonly used antibiotics.

Iriya Rafael, Jing Wenwen, Syal Karan, Mo Manni, Chen Chao, Yu Hui, Haydel Shelley E, Wang Shaopeng, Tao Nongjian

2020-May-01

AST, Antibiotic resistance, E. coli, LSTM, antibiotic susceptibility testing, deep learning, long short-term memory, neural networks, single cell tracking

General General

Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker.

In Nature biotechnology ; h5-index 151.0

Small molecules are usually compared by their chemical structure, but there is no unified analytic framework for representing and comparing their biological activity. We present the Chemical Checker (CC), which provides processed, harmonized and integrated bioactivity data on ~800,000 small molecules. The CC divides data into five levels of increasing complexity, from the chemical properties of compounds to their clinical outcomes. In between, it includes targets, off-targets, networks and cell-level information, such as omics data, growth inhibition and morphology. Bioactivity data are expressed in a vector format, extending the concept of chemical similarity to similarity between bioactivity signatures. We show how CC signatures can aid drug discovery tasks, including target identification and library characterization. We also demonstrate the discovery of compounds that reverse and mimic biological signatures of disease models and genetic perturbations in cases that could not be addressed using chemical information alone. Overall, the CC signatures facilitate the conversion of bioactivity data to a format that is readily amenable to machine learning methods.

Duran-Frigola Miquel, Pauls Eduardo, Guitart-Pla Oriol, Bertoni Martino, Alcalde Víctor, Amat David, Juan-Blanco Teresa, Aloy Patrick

2020-May-18

General General

Predicting lake dissolved organic carbon at a global scale.

In Scientific reports ; h5-index 158.0

The pool of dissolved organic carbon (DOC), is one of the main regulators of the ecology and biogeochemistry of inland water ecosystems, and an important loss term in the carbon budgets of land ecosystems. We used a novel machine learning technique and global databases to test if and how different environmental factors contribute to the variability of in situ DOC concentrations in lakes. In order to estimate DOC in lakes globally we predicted DOC in each lake with a surface area larger than 0.1 km2. Catchment properties and meteorological and hydrological features explained most of the variability of the lake DOC concentration, whereas lake morphometry played only a marginal role. The predicted average of the global DOC concentration in lake water was 3.88 mg L-1. The global predicted pool of DOC in lake water was 729 Tg from which 421 Tg was the share of the Caspian Sea. The results provide global-scale evidence for ecological, climate and carbon cycle models of lake ecosystems and related future prognoses.

Toming Kaire, Kotta Jonne, Uuemaa Evelyn, Sobek Sebastian, Kutser Tiit, Tranvik Lars J

2020-May-21

Cardiology Cardiology

Performance of a convolutional neural network derived from an ECG database in recognizing myocardial infarction.

In Scientific reports ; h5-index 158.0

Artificial intelligence (AI) is developing rapidly in the medical technology field, particularly in image analysis. ECG-diagnosis is an image analysis in the sense that cardiologists assess the waveforms presented in a 2-dimensional image. We hypothesized that an AI using a convolutional neural network (CNN) may also recognize ECG images and patterns accurately. We used the PTB ECG database consisting of 289 ECGs including 148 myocardial infarction (MI) cases to develop a CNN to recognize MI in ECG. Our CNN model, equipped with 6-layer architecture, was trained with training-set ECGs. After that, our CNN and 10 physicians are tested with test-set ECGs and compared their MI recognition capability in metrics F1 (harmonic mean of precision and recall) and accuracy. The F1 and accuracy by our CNN were significantly higher (83 ± 4%, 81 ± 4%) as compared to physicians (70 ± 7%, 67 ± 7%, P < 0.0001, respectively). Furthermore, elimination of Goldberger-leads or ECG image compression up to quarter resolution did not significantly decrease the recognition capability. Deep learning with a simple CNN for image analysis may achieve a comparable capability to physicians in recognizing MI on ECG. Further investigation is warranted for the use of AI in ECG image assessment.

Makimoto Hisaki, Höckmann Moritz, Lin Tina, Glöckner David, Gerguri Shqipe, Clasen Lukas, Schmidt Jan, Assadi-Schmidt Athena, Bejinariu Alexandru, Müller Patrick, Angendohr Stephan, Babady Mehran, Brinkmeyer Christoph, Makimoto Asuka, Kelm Malte

2020-May-21

General General

Inferring multimodal latent topics from electronic health records.

In Nature communications ; h5-index 260.0

Electronic health records (EHR) are rich heterogeneous collections of patient health information, whose broad adoption provides clinicians and researchers unprecedented opportunities for health informatics, disease-risk prediction, actionable clinical recommendations, and precision medicine. However, EHRs present several modeling challenges, including highly sparse data matrices, noisy irregular clinical notes, arbitrary biases in billing code assignment, diagnosis-driven lab tests, and heterogeneous data types. To address these challenges, we present MixEHR, a multi-view Bayesian topic model. We demonstrate MixEHR on MIMIC-III, Mayo Clinic Bipolar Disorder, and Quebec Congenital Heart Disease EHR datasets. Qualitatively, MixEHR disease topics reveal meaningful combinations of clinical features across heterogeneous data types. Quantitatively, we observe superior prediction accuracy of diagnostic codes and lab test imputations compared to the state-of-art methods. We leverage the inferred patient topic mixtures to classify target diseases and predict mortality of patients in critical conditions. In all comparison, MixEHR confers competitive performance and reveals meaningful disease-related topics.

Li Yue, Nair Pratheeksha, Lu Xing Han, Wen Zhi, Wang Yuening, Dehaghi Amir Ardalan Kalantari, Miao Yan, Liu Weiqi, Ordog Tamas, Biernacka Joanna M, Ryu Euijung, Olson Janet E, Frye Mark A, Liu Aihua, Guo Liming, Marelli Ariane, Ahuja Yuri, Davila-Velderrain Jose, Kellis Manolis

2020-May-21

Ophthalmology Ophthalmology

Epiretinal Membrane Detection at the Ophthalmologist Level using Deep Learning of Optical Coherence Tomography.

In Scientific reports ; h5-index 158.0

PURPOSE : Previous deep learning studies on optical coherence tomography (OCT) mainly focused on diabetic retinopathy and age-related macular degeneration. We proposed a deep learning model that can identify epiretinal membrane (ERM) in OCT with ophthalmologist-level performance.

DESIGN : Cross-sectional study.

PARTICIPANTS : A total of 3,618 central fovea cross section OCT images from 1,475 eyes of 964 patients.

METHODS : We retrospectively collected 7,652 OCT images from 1,197 patients. From these images, 2,171 were normal and 1,447 were ERM OCT. A total of 3,141 OCT images was used as training dataset and 477 images as testing dataset. DL algorithm was used to train the interpretation model. Diagnostic results by four board-certified non-retinal specialized ophthalmologists on the testing dataset were compared with those generated by the DL model.

MAIN OUTCOME MEASURES : We calculated for the derived DL model the following characteristics: sensitivity, specificity, F1 score and area under curve (AUC) of the receiver operating characteristic (ROC) curve. These were calculated according to the gold standard results which were parallel diagnoses of the retinal specialist. Performance of the DL model was finally compared with that of non-retinal specialized ophthalmologists.

RESULTS : Regarding the diagnosis of ERM in OCT images, the trained DL model had the following characteristics in performance: sensitivity: 98.7%, specificity: 98.0%, and F1 score: 0.945. The accuracy on the training dataset was 99.7% (95% CI: 99.4 - 99.9%), and for the testing dataset, diagnostic accuracy was 98.1% (95% CI: 96.5 - 99.1%). AUC of the ROC curve was 0.999. The DL model slightly outperformed the average non-retinal specialized ophthalmologists.

CONCLUSIONS : An ophthalmologist-level DL model was built here to accurately identify ERM in OCT images. The performance of the model was slightly better than the average non-retinal specialized ophthalmologists. The derived model may play a role to assist clinicians to promote the efficiency and safety of healthcare in the future.

Lo Ying-Chih, Lin Keng-Hung, Bair Henry, Sheu Wayne Huey-Herng, Chang Chi-Sen, Shen Ying-Cheng, Hung Che-Lun

2020-May-21

General General

Global threat of arsenic in groundwater.

In Science (New York, N.Y.)

Naturally occurring arsenic in groundwater affects millions of people worldwide. We created a global prediction map of groundwater arsenic exceeding 10 micrograms per liter using a random forest machine-learning model based on 11 geospatial environmental parameters and more than 50,000 aggregated data points of measured groundwater arsenic concentration. Our global prediction map includes known arsenic-affected areas and previously undocumented areas of concern. By combining the global arsenic prediction model with household groundwater-usage statistics, we estimate that 94 million to 220 million people are potentially exposed to high arsenic concentrations in groundwater, the vast majority (94%) being in Asia. Because groundwater is increasingly used to support growing populations and buffer against water scarcity due to changing climate, this work is important to raise awareness, identify areas for safe wells, and help prioritize testing.

Podgorski Joel, Berg Michael

2020-May-22

oncology Oncology

Prognostic significance of immune cell populations identified by machine learning in colorectal cancer using routine hematoxylin & eosin stained sections.

In Clinical cancer research : an official journal of the American Association for Cancer Research

PURPOSE : While high T-cell density is a well-established favorable prognostic factor in colorectal cancer, the prognostic significance of tumor-associated plasma cells, neutrophils, and eosinophils is less well-defined.

EXPERIMENTAL DESIGN : We computationally processed digital images of hematoxylin and eosin (H&E)-stained sections to identify lymphocytes, plasma cells, neutrophils, and eosinophils in tumor intraepithelial and stromal areas of 934 colorectal cancers in two prospective cohort studies. Multivariable Cox proportional hazards regression was used to compute mortality HR according to cell density quartiles. The spatial patterns of immune cell infiltration were studied using the GTumor:Immune cell function, which estimates the likelihood of any tumor cell in a sample having at least one neighboring immune cell of the specified type within a certain radius. Validation studies were performed on an independent cohort of 570 colorectal cancers.

RESULTS : Immune cell densities measured by the automated classifier demonstrated high correlation with densities both from manual counts and those obtained from an independently trained automated classifier (Spearman's rho 0.71-0.96). High densities of stromal lymphocytes and eosinophils were associated with better cancer-specific survival [Ptrend<0.001; multivariable HR (4th vs. 1st quartile of eosinophils), 0.49; 95% CI, 0.34-0.71]. High GTumor:Lymphocyte area under the curve (AUC0,20µm) (Ptrend=0.002) and high GTumor:Eosinophil AUC0,20µm (Ptrend<0.001) also showed associations with better cancer-specific survival. High stromal eosinophil density was also associated with better cancer-specific survival in the validation cohort (Ptrend<0.001).

CONCLUSIONS : These findings highlight the potential for machine learning assessment of H&E-stained sections to provide robust, quantitative tumor-immune biomarkers for precision medicine.

Väyrynen Juha P, Lau Mai Chan, Haruki Koichiro, Väyrynen Sara A, Dias Costa Andressa, Borowsky Jennifer, Zhao Melissa, Fujiyoshi Kenji, Arima Kota, Twombly Tyler S, Kishikawa Junko, Gu Simeng, Aminmozaffari Saina, Shi Shanshan, Baba Yoshifumi, Akimoto Naohiko, Ugai Tomotaka, da Silva Annacarolina, Song Mingyang, Wu Kana, Chan Andrew T, Nishihara Reiko, Fuchs Charles S, Meyerhardt Jeffrey A, Giannakis Marios, Ogino Shuji, Nowak Jonathan A

2020-May-21

Radiology Radiology

Resting-State Brain Activity for Early Prediction Outcome in Postanoxic Patients in a Coma with Indeterminate Clinical Prognosis.

In AJNR. American journal of neuroradiology

BACKGROUND AND PURPOSE : Early outcome prediction of postanoxic patients in a coma after cardiac arrest proves challenging. Current prognostication relies on multimodal testing, using clinical examination, electrophysiologic testing, biomarkers, and structural MR imaging. While this multimodal prognostication is accurate for predicting poor outcome (ie, death), it is not sensitive enough to identify good outcome (ie, consciousness recovery), thus leaving many patients with indeterminate prognosis. We specifically assessed whether resting-state fMRI provides prognostic information, notably in postanoxic patients in a coma with indeterminate prognosis early after cardiac arrest, specifically for good outcome.

MATERIALS AND METHODS : We used resting-state fMRI in a prospective study to compare whole-brain functional connectivity between patients with good and poor outcomes, implementing support vector machine learning. Then, we automatically predicted coma outcome using resting-state fMRI and also compared the prediction based on resting-state fMRI with the outcome prediction based on DWI.

RESULTS : Of 17 eligible patients who completed the study procedure (among 351 patients screened), 9 regained consciousness and 8 remained comatose. We found higher functional connectivity in patients recovering consciousness, with greater changes occurring within and between the occipitoparietal and temporofrontal regions. Coma outcome prognostication based on resting-state fMRI machine learning was very accurate, notably for identifying patients with good outcome (accuracy, 94.4%; area under the receiver operating curve, 0.94). Outcome predictors using resting-state fMRI performed significantly better (P < .05) than DWI (accuracy, 60.0%; area under the receiver operating curve, 0.63).

CONCLUSIONS : Indeterminate prognosis might lead to major clinical uncertainty and significant variations in life-sustaining treatments. Resting-state fMRI might bridge the gap left in early prognostication of postanoxic patients in a coma by identifying those with both good and poor outcomes.

Pugin D, Hofmeister J, Gasche Y, Vulliemoz S, Lövblad K-O, Van De Ville D, Haller S

2020-May-21

Ophthalmology Ophthalmology

Fully Automated Segmentation of Globes for Volume Quantification in CT Images of Orbits using Deep Learning.

In AJNR. American journal of neuroradiology

BACKGROUND AND PURPOSE : Fast and accurate quantification of globe volumes in the event of an ocular trauma can provide clinicians with valuable diagnostic information. In this work, an automated workflow using a deep learning-based convolutional neural network is proposed for prediction of globe contours and their subsequent volume quantification in CT images of the orbits.

MATERIALS AND METHODS : An automated workflow using a deep learning -based convolutional neural network is proposed for prediction of globe contours in CT images of the orbits. The network, 2D Modified Residual UNET (MRes-UNET2D), was trained on axial CT images from 80 subjects with no imaging or clinical findings of globe injuries. The predicted globe contours and volume estimates were compared with manual annotations by experienced observers on 2 different test cohorts.

RESULTS : On the first test cohort (n = 18), the average Dice, precision, and recall scores were 0.95, 96%, and 95%, respectively. The average 95% Hausdorff distance was only 1.5 mm, with a 5.3% error in globe volume estimates. No statistically significant differences (P = .72) were observed in the median globe volume estimates from our model and the ground truth. On the second test cohort (n = 9) in which a neuroradiologist and 2 residents independently marked the globe contours, MRes-UNET2D (Dice = 0.95) approached human interobserver variability (Dice = 0.94). We also demonstrated the utility of inter-globe volume difference as a quantitative marker for trauma in 3 subjects with known globe injuries.

CONCLUSIONS : We showed that with fast prediction times, we can reliably detect and quantify globe volumes in CT images of the orbits across a variety of acquisition parameters.

Umapathy L, Winegar B, MacKinnon L, Hill M, Altbach M I, Miller J M, Bilgin A

2020-May-21

Surgery Surgery

Spatial immune profiling of the colorectal tumor microenvironment predicts good outcome in stage II patients.

In NPJ digital medicine

Cellular subpopulations within the colorectal tumor microenvironment (TME) include CD3+ and CD8+ lymphocytes, CD68+ and CD163+ macrophages, and tumor buds (TBs), all of which have known prognostic significance in stage II colorectal cancer. However, the prognostic relevance of their spatial interactions remains unknown. Here, by applying automated image analysis and machine learning approaches, we evaluate the prognostic significance of these cellular subpopulations and their spatial interactions. Resultant data, from a training cohort retrospectively collated from Edinburgh, UK hospitals (n = 113), were used to create a combinatorial prognostic model, which identified a subpopulation of patients who exhibit 100% survival over a 5-year follow-up period. The combinatorial model integrated lymphocytic infiltration, the number of lymphocytes within 50-μm proximity to TBs, and the CD68+/CD163+ macrophage ratio. This finding was confirmed on an independent validation cohort, which included patients treated in Japan and Scotland (n = 117). This work shows that by analyzing multiple cellular subpopulations from the complex TME, it is possible to identify patients for whom surgical resection alone may be curative.

Nearchou Ines P, Gwyther Bethany M, Georgiakakis Elena C T, Gavriel Christos G, Lillard Kate, Kajiwara Yoshiki, Ueno Hideki, Harrison David J, Caie Peter D

2020

Cancer microenvironment, Computational biology and bioinformatics

Dermatology Dermatology

Effects of Label Noise on Deep Learning-Based Skin Cancer Classification.

In Frontiers in medicine

Recent studies have shown that deep learning is capable of classifying dermatoscopic images at least as well as dermatologists. However, many studies in skin cancer classification utilize non-biopsy-verified training images. This imperfect ground truth introduces a systematic error, but the effects on classifier performance are currently unknown. Here, we systematically examine the effects of label noise by training and evaluating convolutional neural networks (CNN) with 804 images of melanoma and nevi labeled either by dermatologists or by biopsy. The CNNs are evaluated on a test set of 384 images by means of 4-fold cross validation comparing the outputs with either the corresponding dermatological or the biopsy-verified diagnosis. With identical ground truths of training and test labels, high accuracies with 75.03% (95% CI: 74.39-75.66%) for dermatological and 73.80% (95% CI: 73.10-74.51%) for biopsy-verified labels can be achieved. However, if the CNN is trained and tested with different ground truths, accuracy drops significantly to 64.53% (95% CI: 63.12-65.94%, p < 0.01) on a non-biopsy-verified and to 64.24% (95% CI: 62.66-65.83%, p < 0.01) on a biopsy-verified test set. In conclusion, deep learning methods for skin cancer classification are highly sensitive to label noise and future work should use biopsy-verified training images to mitigate this problem.

Hekler Achim, Kather Jakob N, Krieghoff-Henning Eva, Utikal Jochen S, Meier Friedegund, Gellrich Frank F, Upmeier Zu Belzen Julius, French Lars, Schlager Justin G, Ghoreschi Kamran, Wilhelm Tabea, Kutzner Heinz, Berking Carola, Heppt Markus V, Haferkamp Sebastian, Sondermann Wiebke, Schadendorf Dirk, Schilling Bastian, Izar Benjamin, Maron Roman, Schmitt Max, Fröhling Stefan, Lipka Daniel B, Brinker Titus J

2020

artificial intelligence, dermatology, label noise, melanoma, nevi, skin cancer

General General

Using the force: STEM knowledge and experience construct shared neural representations of engineering concepts.

In NPJ science of learning

How does STEM knowledge learned in school change students' brains? Using fMRI, we presented photographs of real-world structures to engineering students with classroom-based knowledge and hands-on lab experience, examining how their brain activity differentiated them from their "novice" peers not pursuing engineering degrees. A data-driven MVPA and machine-learning approach revealed that neural response patterns of engineering students were convergent with each other and distinct from novices' when considering physical forces acting on the structures. Furthermore, informational network analysis demonstrated that the distinct neural response patterns of engineering students reflected relevant concept knowledge: learned categories of mechanical structures. Information about mechanical categories was predominantly represented in bilateral anterior ventral occipitotemporal regions. Importantly, mechanical categories were not explicitly referenced in the experiment, nor does visual similarity between stimuli account for mechanical category distinctions. The results demonstrate how learning abstract STEM concepts in the classroom influences neural representations of objects in the world.

Cetron Joshua S, Connolly Andrew C, Diamond Solomon G, May Vicki V, Haxby James V, Kraemer David J M

2020

Human behaviour, Learning and memory

General General

Non-destructive estimation of field maize biomass using terrestrial lidar: an evaluation from plot level to individual leaf level.

In Plant methods

Background : Precision agriculture is an emerging research field that relies on monitoring and managing field variability in phenotypic traits. An important phenotypic trait is biomass, a comprehensive indicator that can reflect crop yields. However, non-destructive biomass estimation at fine levels is unknown and challenging due to the lack of accurate and high-throughput phenotypic data and algorithms.

Results : In this study, we evaluated the capability of terrestrial light detection and ranging (lidar) data in estimating field maize biomass at the plot, individual plant, leaf group, and individual organ (i.e., individual leaf or stem) levels. The terrestrial lidar data of 59 maize plots with more than 1000 maize plants were collected and used to calculate phenotypes through a deep learning-based pipeline, which were then used to predict maize biomass through simple regression (SR), stepwise multiple regression (SMR), artificial neural network (ANN), and random forest (RF). The results showed that terrestrial lidar data were useful for estimating maize biomass at all levels (at each level, R2 was greater than 0.80), and biomass estimation at leaf group level was the most precise (R2 = 0.97, RMSE = 2.22 g) among all four levels. All four regression techniques performed similarly at all levels. However, considering the transferability and interpretability of the model itself, SR is the suggested method for estimating maize biomass from terrestrial lidar-derived phenotypes. Moreover, height-related variables showed to be the most important and robust variables for predicting maize biomass from terrestrial lidar at all levels, and some two-dimensional variables (e.g., leaf area) and three-dimensional variables (e.g., volume) showed great potential as well.

Conclusion : We believe that this study is a unique effort on evaluating the capability of terrestrial lidar on estimating maize biomass at difference levels, and can provide a useful resource for the selection of the phenotypes and models required to estimate maize biomass in precision agriculture practices.

Jin Shichao, Su Yanjun, Song Shilin, Xu Kexin, Hu Tianyu, Yang Qiuli, Wu Fangfang, Xu Guangcai, Ma Qin, Guan Hongcan, Pang Shuxin, Li Yumei, Guo Qinghua

2020

Biomass, Machine learning, Phenotype, Precision agriculture, Terrestrial lidar

General General

Using Administrative Data to Predict Suicide After Psychiatric Hospitalization in the Veterans Health Administration System.

In Frontiers in psychiatry

There is a very high suicide rate in the year after psychiatric hospital discharge. Intensive postdischarge case management programs can address this problem but are not cost-effective for all patients. This issue can be addressed by developing a risk model to predict which inpatients might need such a program. We developed such a model for the 391,018 short-term psychiatric hospital admissions of US veterans in Veterans Health Administration (VHA) hospitals 2010-2013. Records were linked with the National Death Index to determine suicide within 12 months of hospital discharge (n=771). The Super Learner ensemble machine learning method was used to predict these suicides for time horizon between 1 week and 12 months after discharge in a 70% training sample. Accuracy was validated in the remaining 30% holdout sample. Predictors included VHA administrative variables and small area geocode data linked to patient home addresses. The models had AUC=.79-.82 for time horizons between 1 week and 6 months and AUC=.74 for 12 months. An analysis of operating characteristics showed that 22.4%-32.2% of patients who died by suicide would have been reached if intensive case management was provided to the 5% of patients with highest predicted suicide risk. Positive predictive value (PPV) at this higher threshold ranged from 1.2% over 12 months to 3.8% per case manager year over 1 week. Focusing on the low end of the risk spectrum, the 40% of patients classified as having lowest risk account for 0%-9.7% of suicides across time horizons. Variable importance analysis shows that 51.1% of model performance is due to psychopathological risk factors accounted, 26.2% to social determinants of health, 14.8% to prior history of suicidal behaviors, and 6.6% to physical disorders. The paper closes with a discussion of next steps in refining the model and prospects for developing a parallel precision treatment model.

Kessler Ronald C, Bauer Mark S, Bishop Todd M, Demler Olga V, Dobscha Steven K, Gildea Sarah M, Goulet Joseph L, Karras Elizabeth, Kreyenbuhl Julie, Landes Sara J, Liu Howard, Luedtke Alex R, Mair Patrick, McAuliffe William H B, Nock Matthew, Petukhova Maria, Pigeon Wilfred R, Sampson Nancy A, Smoller Jordan W, Weinstock Lauren M, Bossarte Robert M

2020

intensive case management, machine learning, predictive analytics, suicide, super learner

Pathology Pathology

Fast differentiable DNA and protein sequence optimization for molecular design

ArXiv Preprint

Designing DNA and protein sequences with improved or novel function has the potential to greatly accelerate synthetic biology. Machine learning models that accurately predict biological fitness from sequence are becoming a powerful tool for molecular design. Activation maximization offers a simple design strategy for differentiable models: one-hot coded sequences are first approximated by a continuous representation which is then iteratively optimized with respect to the predictor oracle by gradient ascent. While elegant, this method is limited by technical challenges, as it suffers from vanishing gradients and may cause predictor pathologies leading to poor convergence. Here, we build on a previously proposed straight-through approximation method to optimize through discrete sequence samples. By normalizing nucleotide logits across positions and introducing an adaptive entropy variable, we remove bottlenecks arising from overly large or skewed sampling parameters. This results in a markedly improved algorithm with up to 100-fold faster convergence. Moreover, our method finds improved fitness optima compared to existing methods, including the original algorithm without normalization and global optimization heuristics such as Simulated Annealing. We demonstrate our improved method by designing DNA and enzyme sequences for six deep learning predictors, including a protein structure predictor (trRosetta).

Johannes Linder, Georg Seelig

2020-05-22

Surgery Surgery

Deep Learning Provides Exceptional Accuracy to ECoG-Based Functional Language Mapping for Epilepsy Surgery.

In Frontiers in neuroscience ; h5-index 72.0

The success of surgical resection in epilepsy patients depends on preserving functionally critical brain regions, while removing pathological tissues. Being the gold standard, electro-cortical stimulation mapping (ESM) helps surgeons in localizing the function of eloquent cortex through electrical stimulation of electrodes placed directly on the cortical brain surface. Due to the potential hazards of ESM, including increased risk of provoked seizures, electrocorticography based functional mapping (ECoG-FM) was introduced as a safer alternative approach. However, ECoG-FM has a low success rate when compared to the ESM. In this study, we address this critical limitation by developing a new algorithm based on deep learning for ECoG-FM and thereby we achieve an accuracy comparable to ESM in identifying eloquent language cortex. In our experiments, with 11 epilepsy patients who underwent presurgical evaluation (through deep learning-based signal analysis on 637 electrodes), our proposed algorithm obtained an accuracy of 83.05% in identifying language regions, an exceptional 23% improvement with respect to the conventional ECoG-FM analysis (∼60%). Our findings have demonstrated, for the first time, that deep learning powered ECoG-FM can serve as a stand-alone modality and avoid likely hazards of the ESM in epilepsy surgery. Hence, reducing the potential for developing post-surgical morbidity in the language function.

RaviPrakash Harish, Korostenskaja Milena, Castillo Eduardo M, Lee Ki H, Salinas Christine M, Baumgartner James, Anwar Syed M, Spampinato Concetto, Bagci Ulas

2020

deep learning, electro-cortical stimulation mapping, electrocorticography, eloquent cortex localization, real-time functional mapping

Pathology Pathology

Classification of Epithelial Ovarian Carcinoma Whole-Slide Pathology Images Using Deep Transfer Learning

ArXiv Preprint

Ovarian cancer is the most lethal cancer of the female reproductive organs. There are $5$ major histological subtypes of epithelial ovarian cancer, each with distinct morphological, genetic, and clinical features. Currently, these histotypes are determined by a pathologist's microscopic examination of tumor whole-slide images (WSI). This process has been hampered by poor inter-observer agreement (Cohen's kappa $0.54$-$0.67$). We utilized a \textit{two}-stage deep transfer learning algorithm based on convolutional neural networks (CNN) and progressive resizing for automatic classification of epithelial ovarian carcinoma WSIs. The proposed algorithm achieved a mean accuracy of $87.54\%$ and Cohen's kappa of $0.8106$ in the slide-level classification of $305$ WSIs; performing better than a standard CNN and pathologists without gynecology-specific training.

Yiping Wang, David Farnell, Hossein Farahani, Mitchell Nursey, Basile Tessier-Cloutier, Steven J. M. Jones, David G. Huntsman, C. Blake Gilks, Ali Bashashati

2020-05-22

General General

Developing a supervised learning-based social media business sentiment index.

In The Journal of supercomputing

The fast-growing digital data generation leads to the emergence of the era of big data, which become particularly more valuable because approximately 70% of the collected data in the world comes from social media. Thus, the investigation of online social network services is of paramount importance. In this paper, we use the sentiment analysis, which detects attitudes and emotions toward issues of society posted in social media, to understand the actual economic situation. To this end, two steps are suggested. In the first step, after training the sentiment classifiers with several big data sources of social media datasets, we consider three types of feature sets: feature vector, sequence vector and a combination of dictionary-based feature and sequence vectors. Then, the performance of six classifiers is assessed: MaxEnt-L1, C4.5 decision tree, SVM-kernel, Ada-boost, Naïve Bayes and MaxEnt. In the second step, we collect datasets that are relevant to several economic words that the public use to explicitly express their opinions. Finally, we use a vector auto-regression analysis to confirm our hypothesis. The results show the statistically significant relationship between public sentiment and economic performance. That is, "depression" and "unemployment" lead to KOSPI. Also, it shows that the extracted keywords from the sentiment analysis, such as "price," "year-end-tax" and "budget deficit," cause the exchange rates.

Lee Hyeonseo, Lee Nakyeong, Seo Harim, Song Min

2020

Machine learning, Sentiment analysis, Social media, Supervised learning

General General

Epidemiologically and Socio-economically Optimal Policies via Bayesian Optimization

ArXiv Preprint

Mass public quarantining, colloquially known as a lock-down, is a non-pharmaceutical intervention to check spread of disease and pandemics such as the ongoing COVID-19 pandemic. We present ESOP, a novel application of active machine learning techniques using Bayesian optimization, that interacts with an epidemiological model to arrive at lock-down schedules that optimally balance public health benefits and socio-economic downsides of reduced economic activity during lock-down periods. The utility of ESOP is demonstrated using case studies with VIPER, a stochastic agent-based simulator that we also propose. However, ESOP can flexibly interact with arbitrary epidemiological simulators and produce schedules that involve multiple phases of lock-downs.

Amit Chandak, Debojyoti Dey, Bhaskar Mukhoty, Purushottam Kar

2020-05-22

General General

First-trimester screening for trisomy 21 via an individualized nomogram.

In Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology

OBJECTIVES : To develop and validate a nomogram based on fetal nuchal translucency (NT) and ultrasonographic facial markers for the screening of trisomy 21 in the first-trimester of pregnancy.

METHODS : This was a retrospective case-control study using stored 2D midsagittal fetal profile images captured at 11+0 to 13+6 weeks' gestation. Our database was used to identify 302 cases of trisomy 21 pregnancies and 322 euploid pregnancies. For each case, the maternal age and ultrasonographic facial markers were investigated. Least absolute shrinkage and selection operator (LASSO) method and multivariable analysis were used to select the discriminative markers automatically. Logistic regression was used to develop a model (LASSO-model) based on the selected markers to screen for trisomy 21 in the first-trimester of pregnancy. Furthermore, 60 cases were randomly selected as a retest set to evaluate the model's robustness. The predictive performance of the model of fetal NT and maternal age, the model of all markers of this study and the LASSO-model for the screening of trisomy 21 was assessed using area under receiver operating characteristic (ROC) curve (AUC). A nomogram was developed as an individualized tool to predict patient-specific probability for trisomy 21, which is a more intuitive presentation of the LASSO-model. The performance of the nomogram was assessed using C-index and calibration curve.

RESULTS : Eight markers were incorporated into the LASSO-model, including fetal NT, prenasal thickness-to-nasal bone length ratio, facial profile line, frontomaxillary facial angle, frontonasal facial angle, mandibulomaxillary facial angle, maxilla-nasion-mandible angle, and d2 (distance between the anterior edge of the prefrontal skin and the mandibulomaxillary line) (all P-values <0.05). The AUCs of the LASSO-model for the screening of trisomy 21 were 0.983 (95% CI: 0.971-0.994) and 0.979 (95% CI: 0.966-0.993) in the training and the validation sets, respectively, which were higher than the AUCs of all eight individual ultrasonographic markers included in the LASSO-model. The AUC of the LASSO-model in the retest set was 0.997 (95% CI: 0.990-1.000), which showed the good robustness of LASSO-model. The AUC of the LASSO-model was significantly higher than the AUC of the model based on fetal NT and maternal age in both the training and the validation sets. The nomogram of LASSO-model showed a good discrimination of trisomy 21 with C-indexes of 0.983 in the training set and 0.981 in the validation set.

CONCLUSION : This study has presented an individualized nomogram, which incorporates the fetal NT and a series of ultrasonographic facial profile markers selected by the LASSO-method and multivariable analysis. It can potentially be utilized as a convenient and effective tool for the screening of trisomy 21 in the first-trimester of pregnancy. This article is protected by copyright. All rights reserved.

Sun Y, Zhang L, Dong D, Li X, Wang J, Yin C, Poon L C, Tian J, Wu Q

2020-May-21

facial profile, first-trimester screening, machine learning, nomogram, trisomy 21, ultrasonographic markers

Radiology Radiology

Peripheral Nerves: Not Only Cross-sectional Area in the Era of Radiomics.

In Seminars in musculoskeletal radiology

The peripheral nervous system is increasingly being investigated using medical imaging as a complement or in association with electrodiagnostics tests. The application of imaging techniques, such as ultrasound (US) and magnetic resonance imaging (MRI), allows detailed visualization of the peripheral nervous system. According to the European Society of Musculoskeletal Radiology, the use of US for nerve evaluation is strongly encouraged. In addition, the role of US is further enhanced by the wide application of US-guided techniques to diagnose or to treat peripheral nerve disorders.Standard evaluation of peripheral nerves on US usually relies on cross-sectional area evaluation with different cutoff values in the osteofibrous tunnels and outside them. In several anatomical areas, side-to-side comparison is highly recommended because it helps distinguish subtle variations by using the unaffected limb as an internal control.US is widely used to perform US-guided interventional procedures on peripheral nerves. The recent development of radiomics and machine and deep learning applied to peripheral nerves may reveal new insights beyond the capabilities of the human eye. Radiomics may have a role in expanding the diagnostic capabilities of US and MRI in the study of peripheral nerve pathology, especially when the cross-sectional area is not markedly increased.

Tagliafico Alberto Stefano, González Raquel Prada, Rossi Federica, Bignotti Bianca, Martinoli Carlo

2020-Apr

Pathology Pathology

MRI radiomics-based machine-learning classification of bone chondrosarcoma.

In European journal of radiology ; h5-index 47.0

PURPOSE : To evaluate the diagnostic performance of machine learning for discrimination between low-grade and high-grade cartilaginous bone tumors based on radiomic parameters extracted from unenhanced magnetic resonance imaging (MRI).

METHODS : We retrospectively enrolled 58 patients with histologically-proven low-grade/atypical cartilaginous tumor of the appendicular skeleton (n = 26) or higher-grade chondrosarcoma (n = 32, including 16 appendicular and 16 axial lesions). They were randomly divided into training (n = 42) and test (n = 16) groups for model tuning and testing, respectively. All tumors were manually segmented on T1-weighted and T2-weighted images by drawing bidimensional regions of interest, which were used for first order and texture feature extraction. A Random Forest wrapper was employed for feature selection. The resulting dataset was used to train a locally weighted ensemble classifier (AdaboostM1). Its performance was assessed via 10-fold cross-validation on the training data and then on the previously unseen test set. Thereafter, an experienced musculoskeletal radiologist blinded to histological and radiomic data qualitatively evaluated the cartilaginous tumors in the test group.

RESULTS : After feature selection, the dataset was reduced to 4 features extracted from T1-weighted images. AdaboostM1 correctly classified 85.7 % and 75 % of the lesions in the training and test groups, respectively. The corresponding areas under the receiver operating characteristic curve were 0.85 and 0.78. The radiologist correctly graded 81.3 % of the lesions. There was no significant difference in performance between the radiologist and machine learning classifier (P = 0.453).

CONCLUSIONS : Our machine learning approach showed good diagnostic performance for classification of low-to-high grade cartilaginous bone tumors and could prove a valuable aid in preoperative tumor characterization.

Gitto Salvatore, Cuocolo Renato, Albano Domenico, Chianca Vito, Messina Carmelo, Gambino Angelo, Ugga Lorenzo, Cortese Maria Cristina, Lazzara Angelo, Ricci Domenico, Spairani Riccardo, Zanchetta Edoardo, Luzzati Alessandro, Brunetti Arturo, Parafioriti Antonina, Sconfienza Luca Maria

2020-May-07

Artificial intelligence, Cartilaginous tumor, Machine learning, Radiomics, Texture analysis

General General

Generalization of diffusion magnetic resonance imaging-based brain age prediction model through transfer learning.

In NeuroImage ; h5-index 117.0

Brain age prediction models using diffusion magnetic resonance imaging (dMRI) and machine learning techniques enable individual assessment of brain aging status in healthy people and patients with brain disorders. However, dMRI data are notorious for high intersite variability, prohibiting direct application of a model to the datasets obtained from other sites. In this study, we generalized the dMRI-based brain age model to different dMRI datasets acquired under different imaging conditions. Specifically, we adopted a transfer learning approach to achieve domain adaptation. To evaluate the performance of transferred models, a brain age prediction model was constructed using a large dMRI dataset as the source domain, and the model was transferred to three target domains with distinct acquisition scenarios. The experiments were performed to investigate (1) the tuning data size needed to achieve satisfactory performance for brain age prediction, (2) the feature types suitable for different dMRI acquisition scenarios, and (3) performance of the transfer learning approach compared with the statistical covariate approach. By tuning the models with relatively small data size and certain feature types, optimal transferred models were obtained with significantly improved prediction performance in all three target cohorts (p < 0.001). The mean absolute error of the predicted age was reduced from 13.89 to 4.78 years in Cohort 1, 8.34 to 5.35 years in Cohort 2, and 8.74 to 5.64 years in Cohort 3. The test-retest reliability of the transferred model was verified using dMRI data acquired at two timepoints (intraclass correlation coefficient = 0.950). Clinical sensitivity of the brain age prediction model was investigated by estimating the brain age in patients with schizophrenia. The prediction made by the transferred model was not significantly different from that made by the reference model. Both models predicted significant brain aging in patients with schizophrenia as compared with healthy controls (p < 0.001); the predicted age difference of the transferred model was 4.63 and 0.26 years for patients and controls, respectively, and that of the reference model was 4.39 and -0.09 years, respectively. In conclusion, transfer learning approach is an efficient way to generalize the dMRI-based brain age prediction model. Appropriate transfer learning approach and suitable tuning data size should be chosen according to different dMRI acquisition scenarios.

Chen Chang-Le, Hsu Yung-Chin, Yang Li-Ying, Tung Yu-Hung, Luo Wen-Bin, Liu Chih-Min, Hwang Tzung-Jeng, Hwu Hai-Gwo, Isaac Tseng Wen-Yih

2020-May-10

Brain age, Diffusion MRI, Diffusion harmonization, Neural network, Transfer learning, White matter

General General

Multi-feature fusion for deep learning to predict plant lncRNA-protein interaction.

In Genomics

Long non-coding RNAs (lncRNAs) play key roles in regulating cellular biological processes through diverse molecular mechanisms including binding to RNA binding proteins. The majority of plant lncRNAs are functionally uncharacterized, thus, accurate prediction of plant lncRNA-protein interaction is imperative for subsequent functional studies. We present an integrative model, namely DRPLPI. Its uniqueness is that it predicts by multi-feature fusion. Structural and four groups of sequence features are used, including tri-nucleotide composition, gapped k-mer, recursive complement and binary profile. We design a multi-head self-attention long short-term memory encoder-decoder network to extract generative high-level features. To obtain robust results, DRPLPI combines categorical boosting and extra trees into a single meta-learner. Experiments on Zea mays and Arabidopsis thaliana obtained 0.9820 and 0.9652 area under precision/recall curve (AUPRC) respectively. The proposed method shows significant enhancement in the prediction performance compared with existing state-of-the-art methods.

Wekesa Jael Sanyanda, Meng Jun, Luan Yushi

2020-May-08

Deep learning, Plants, Prediction, Secondary structure features, lncRNA–protein interaction

General General

The ideological divide in public perceptions of self-driving cars.

In Public understanding of science (Bristol, England)

Applications in artificial intelligence such as self-driving cars may profoundly transform our society, yet emerging technologies are frequently faced with suspicion or even hostility. Meanwhile, public opinions about scientific issues are increasingly polarized along the ideological line. By analyzing a nationally representative panel in the United States, we reveal an emerging ideological divide in public reactions to self-driving cars. Compared with liberals and Democrats, conservatives and Republicans express more concern about autonomous vehicles and more support for restrictively regulating autonomous vehicles. This ideological gap is largely driven by social conservatism. Moreover, both familiarity with driverless vehicles and scientific literacy reduce respondents' concerns over driverless vehicles and support for regulation policies. Still, the effects of familiarity and scientific literacy are weaker among social conservatives, indicating that people may assimilate new information in a biased manner that promotes their worldviews.

Peng Yilang

2020-May

economic and social conservatism, political ideology, risk perception, scientific literacy, self-driving cars

General General

GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Information

ArXiv Preprint

The past several years have witnessed a huge surge in the use of social media platforms during mass convergence events such as health emergencies, natural or human-induced disasters. These non-traditional data sources are becoming vital for disease forecasts and surveillance when preparing for epidemic and pandemic outbreaks. In this paper, we present GeoCoV19, a large-scale Twitter dataset containing more than 524 million multilingual tweets posted over a period of 90 days since February 1, 2020. Moreover, we employ a gazetteer-based approach to infer the geolocation of tweets. We postulate that this large-scale, multilingual, geolocated social media data can empower the research communities to evaluate how societies are collectively coping with this unprecedented global crisis as well as to develop computational methods to address challenges such as identifying fake news, understanding communities' knowledge gaps, building disease forecast and surveillance models, among others.

Umair Qazi, Muhammad Imran, Ferda Ofli

2020-05-22

General General

GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Information

ArXiv Preprint

The past several years have witnessed a huge surge in the use of social media platforms during mass convergence events such as health emergencies, natural or human-induced disasters. These non-traditional data sources are becoming vital for disease forecasts and surveillance when preparing for epidemic and pandemic outbreaks. In this paper, we present GeoCoV19, a large-scale Twitter dataset containing more than 524 million multilingual tweets posted over a period of 90 days since February 1, 2020. Moreover, we employ a gazetteer-based approach to infer the geolocation of tweets. We postulate that this large-scale, multilingual, geolocated social media data can empower the research communities to evaluate how societies are collectively coping with this unprecedented global crisis as well as to develop computational methods to address challenges such as identifying fake news, understanding communities' knowledge gaps, building disease forecast and surveillance models, among others.

Umair Qazi, Muhammad Imran, Ferda Ofli

2020-05-22

General General

Predicting treatment effects in unipolar depression: A meta-review.

In Pharmacology & therapeutics ; h5-index 80.0

There is increasing interest in clinical prediction models in psychiatry, which focus on developing multivariate algorithms to guide personalized diagnostic or management decisions. The main target of these models is the prediction of treatment response to different antidepressant therapies. This is because the ability to predict response based on patients' personal data may allow clinicians to make improved treatment decisions, and to provide more efficacious or more tolerable medications to the right patient. We searched the literature for systematic reviews about treatment prediction in the context of existing treatment modalities for adult unipolar depression, until July 2019. Treatment effect is defined broadly to include efficacy, safety, tolerability and acceptability outcomes. We first focused on the identification of individual predictor variables that might predict treatment response, and second, we considered multivariate clinical prediction models. Our meta-review included a total of 10 systematic reviews; seven (from 2014 to 2018) focusing on individual predictor variables and three focusing on clinical prediction models. These identified a number of sociodemographic, phenomenological, clinical, neuroimaging, remote monitoring, genetic and serum marker variables as possible predictor variables for treatment response, alongside statistical and machine-learning approaches to clinical prediction model development. Effect sizes for individual predictor variables were generally small and clinical prediction models had generally not been validated in external populations. There is a need for rigorous model validation in large external data-sets to prove the clinical utility of models. We also discuss potential future avenues in the field of personalized psychiatry, particularly the combination of multiple sources of data and the emerging field of artificial intelligence and digital mental health to identify new individual predictor variables.

Gillett George, Tomlinson Anneka, Efthimiou Orestis, Cipriani Andrea

2020-May-08

Antidepressant drugs, Clinical prediction model, Personalized medicine, Precision psychiatry, Prediction, Treatment response, Unipolar depression

General General

ARTIFICIAL INTELLIGENCE AND NEUROPSYCHOLOGICAL MEASURES: THE CASE OF ALZHEIMER'S DISEASE.

In Neuroscience and biobehavioral reviews

One of the current challenges in the field of Alzheimer's disease (AD) is to identify patients with mild cognitive impairment (MCI) that will convert to AD. Artificial intelligence, in particular machine learning (ML), has established as one of more powerful approach to extract reliable predictors and to automatically classify different AD phenotypes. It is time to accelerate the translation of this knowledge in clinical practice, mainly by using low-cost features originating from the neuropsychological assessment. We performed a meta-analysis to assess the contribution of ML and neuropsychological measures for the automated classification of MCI patients and the prediction of their conversion to AD. The pooled sensitivity and specificity of patients' classifications was obtained by means of a quantitative bivariate random-effect meta-analytic approach. Although a high heterogeneity was observed, the results of meta-analysis show that ML applied to neuropsychological measures can lead to a successful automatic classification, being more specific as screening rather than prognosis tool. Relevant categories of neuropsychological tests can be extracted by ML that maximize the classification accuracy.

Battista Petronilla, Salvatore Christian, Berlingeri Manuela, Cerasa Antonio, Castiglioni Isabella

2020-May-08

AD, MCI, Mild Cognitive impairment, automatic classification, biomarkers, cognitive measures, machine learning, neurodegenerative diseases: dementia, neuropsychological tests

General General

Lattice Light-Sheet Microscopy Multi-dimensional Analyses (LaMDA) of T-Cell Receptor Dynamics Predict T-Cell Signaling States.

In Cell systems

Lattice light-sheet microscopy provides large amounts of high-dimensional, high-spatiotemporal resolution imaging data of cell surface receptors across the 3D surface of live cells, but user-friendly analysis pipelines are lacking. Here, we introduce lattice light-sheet microscopy multi-dimensional analyses (LaMDA), an end-to-end pipeline comprised of publicly available software packages that combines machine learning, dimensionality reduction, and diffusion maps to analyze surface receptor dynamics and classify cellular signaling states without the need for complex biochemical measurements or other prior information. We use LaMDA to analyze images of T-cell receptor (TCR) microclusters on the surface of live primary T cells under resting and stimulated conditions. We observe global spatial and temporal changes of TCRs across the 3D cell surface, accurately differentiate stimulated cells from unstimulated cells, precisely predict attenuated T-cell signaling after CD4 and CD28 receptor blockades, and reliably discriminate between structurally similar TCR ligands. All instructions needed to implement LaMDA are included in this paper.

Rosenberg Jillian, Cao Guoshuai, Borja-Prieto Fernanda, Huang Jun

2020-May-20

T cell receptor, computational biology, lattice light-sheet microscopy, machine learning

General General

Prediction of Signed Protein Kinase Regulatory Circuits.

In Cell systems

Complex networks of regulatory relationships between protein kinases comprise a major component of intracellular signaling. Although many kinase-kinase regulatory relationships have been described in detail, these tend to be limited to well-studied kinases whereas the majority of possible relationships remains unexplored. Here, we implement a data-driven, supervised machine learning method to predict human kinase-kinase regulatory relationships and whether they have activating or inhibiting effects. We incorporate high-throughput data, kinase specificity profiles, and structural information to produce our predictions. The results successfully recapitulate previously annotated regulatory relationships and can reconstruct known signaling pathways from the ground up. The full network of predictions is relatively sparse, with the vast majority of relationships assigned low probabilities. However, it nevertheless suggests denser modes of inter-kinase regulation than normally considered in intracellular signaling research. A record of this paper's transparent peer review process is included in the Supplemental Information.

Invergo Brandon M, Petursson Borgthor, Akhtar Nosheen, Bradley David, Giudice Girolamo, Hijazi Maruan, Cutillas Pedro, Petsalaki Evangelia, Beltrao Pedro

2020-May-20

intracellular signaling, machine learning, phosphorylation, protein kinase, signaling networks

General General

Predictive and interpretable models via the stacked elastic net.

In Bioinformatics (Oxford, England)

MOTIVATION : Machine learning in the biomedical sciences should ideally provide predictive and interpretable models. When predicting outcomes from clinical or molecular features, applied researchers often want to know which features have effects, whether these effects are positive or negative, and how strong these effects are. Regression analysis includes this information in the coefficients but typically renders less predictive models than more advanced machine learning techniques.

RESULTS : Here we propose an interpretable meta-learning approach for high-dimensional regression. The elastic net provides a compromise between estimating weak effects for many features and strong effects for some features. It has a mixing parameter to weight between ridge and lasso regularisation. Instead of selecting one weighting by tuning, we combine multiple weightings by stacking. We do this in a way that increases predictivity without sacrificing interpretability.

AVAILABILITY AND IMPLEMENTATION : The R package starnet is available on GitHub: https://github.com/rauschenberger/starnet.

SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.

Rauschenberger Armin, Glaab Enrico, van de Wiel Mark

2020-May-21

General General

Optimizing an eDNA protocol for estuarine environments: Balancing sensitivity, cost and time.

In PloS one ; h5-index 176.0

Environmental DNA (eDNA) analysis has gained traction as a precise and cost-effective method for species and waterways management. To date, publications on eDNA protocol optimization have focused primarily on DNA yield. Therefore, it has not been possible to evaluate the cost and speed of specific components of the eDNA protocol, such as water filtration and DNA extraction method when designing or choosing an eDNA protocol. At the same time, these two parameters are essential for the experimental design of a project. Here we evaluate and rank 27 different eDNA protocols in the context of Chinook salmon (Oncorhynchus tshawytscha) eDNA detection in an estuarine environment. We present a comprehensive evaluation of multiple eDNA protocol parameters, balancing time, cost and DNA yield. We collected samples composed of 500 mL estuarine water from Deverton Slough (38°11'16.7"N 121°58'34.5"W) and 500 mL from tank water containing 1.3 juvenile Chinook Salmon per liter. Then, we compared extraction methods, filter types, use of inhibitor removal kit for DNA yield, processing time, and protocol cost. Lastly, we used an MCMC algorithm together with machine learning to understand the DNA yield of each step of the protocol as well as the interactions between those steps. Glass fiber filtration was to be the most resilient to high turbidites, filtering the samples in 2.32 ± 0.08 min instead of 14.16 ± 1.86 min and 6.72 ± 1.99 min for nitrocellulose and paper filter N1, respectively. The filtration DNA yield percentages for paper filter N1, glass fiber, and nitrocellulose were 0.00045 ± 0.00013, 0.00107 ± 0.00013, 0.00172 ± 0.00013. The DNA extraction yield percentage for QIagen, dipstick, NaOH, magnetic beads, and direct dipstick ranged from 0.047 ± 0.0388 to 0.475 ± 0.0357. For estuarine waters, which are challenging for eDNA studies due to high turbidity, variable salinity, and the presence of PCR inhibitors, we found that a protocol combining glass filters, magnetic beads, and an extra step for PCR inhibitor removal, is the method that best balances time, cost, and yield. In addition, we provide a generalized decision tree for determining the optimal eDNA protocol for other studies in aquatic systems. Our findings should be applicable to most aquatic environments and provide a clear guide for determining which eDNA protocol should be used under different study constraints.

Sanches Thiago M, Schreier Andrea D

2020

General General

Mangrove forest classification and aboveground biomass estimation using an atom search algorithm and adaptive neuro-fuzzy inference system.

In PloS one ; h5-index 176.0

BACKGROUND : Advances in earth observation and machine learning techniques have created new options for forest monitoring, primarily because of the various possibilities that they provide for classifying forest cover and estimating aboveground biomass (AGB).

METHODS : This study aimed to introduce a novel model that incorporates the atom search algorithm (ASO) and adaptive neuro-fuzzy inference system (ANFIS) into mangrove forest classification and AGB estimation. The Ca Mau coastal area was selected as a case study since it has been considered the most preserved mangrove forest area in Vietnam and is being investigated for the impacts of land-use change on forest quality. The model was trained and validated with a set of Sentinel-1A imagery with VH and VV polarizations, and multispectral information from the SPOT image. In addition, feature selection was also carried out to choose the optimal combination of predictor variables. The model performance was benchmarked against conventional methods, such as support vector regression, multilayer perceptron, random subspace, and random forest, by using statistical indicators, namely, root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2).

RESULTS : The results showed that all three indicators of the proposed model were statistically better than those from the benchmarked methods. Specifically, the hybrid model ended up at RMSE = 70.882, MAE = 55.458, R2 = 0.577 for AGB estimation.

CONCLUSION : From the experiments, such hybrid integration can be recommended for use as an alternative solution for biomass estimation. In a broader context, the fast growth of metaheuristic search algorithms has created new scientifically sound solutions for better analysis of forest cover.

Pham Minh Hai, Do Thi Hoai, Pham Van-Manh, Bui Quang-Thanh

2020

General General

Drought index prediction using advanced fuzzy logic model: Regional case study over Kumaon in India.

In PloS one ; h5-index 176.0

A new version of the fuzzy logic model, called the co-active neuro fuzzy inference system (CANFIS), is introduced for predicting standardized precipitation index (SPI). Multiple scales of drought information at six meteorological stations located in Uttarakhand State, India, are used. Different lead times of SPI were computed for prediction, including 1, 3, 6, 9, 12, and 24 months, with inputs abstracted by autocorrelation function (ACF) and partial-ACF (PACF) analysis at 5% significance level. The proposed CANFIS model was validated against two models: classical artificial intelligence model (e.g., multilayer perceptron neural network (MLPNN)) and regression model (e.g., multiple linear regression (MLR)). Several performance evaluation metrices (root mean square error, Nash-Sutcliffe efficiency, coefficient of correlation, and Willmott index), and graphical visualizations (scatter plot and Taylor diagram) were computed for the evaluation of model performance. Results indicated that the CANFIS model predicted the SPI better than the other models and prediction results were different for different meteorological stations. The proposed model can build a reliable expert intelligent system for predicting meteorological drought at multi-time scales and decision making for remedial schemes to cope with meteorological drought at the study stations and can help to maintain sustainable water resources management.

Malik Anurag, Kumar Anil, Salih Sinan Q, Kim Sungwon, Kim Nam Won, Yaseen Zaher Mundher, Singh Vijay P

2020

General General

Innovation in Chinese internet companies: A meta-frontier analysis.

In PloS one ; h5-index 176.0

The innovation of a particular company benefits the whole industry when innovation technology transfers to others. Similarly, the development and innovation in internet companies influence the development and innovation of the industry. This investigation has applied a unique approach of meta-frontier analysis to estimate and analyze the innovation in internet companies in China. A unique dataset of Chinese internet companies from 2000 to 2017 has been utilized to estimate and compare the innovation over the period of study. The change in technology gap ratio (TGR) and a shift in production function have translated into innovation which was overlooked by previous studies. It is found that the production function of internet companies is moving upward in the presence of external factors such as smartphones invention, mobile internet, mobile payments, and artificial intelligence, etc. Consequently, a sudden increase in TGR is captured due to the innovation of some companies. Hence, the average TE of the industry falls caused by the increased distance of other companies form industry production function. However, the innovation advantage defused when other companies start imitating and the average TE elevates. A steady increase in the TGR index revealed that the continuous innovation-based growth of some companies lifting the production frontier upward. This provides the opportunity for other companies to imitate and provides continuous growth in the industry. This study provides a novel methodological approach to measure innovation and also provide practical implication by empirical estimation of innovation in Chinese internet companies.

Hafeez Sadaf, Arshad Noreen Izza, Rahim Lukman Bin A B, Shabbir Muhammad Farooq, Iqbal Jawad

2020

General General

Machine learning provides evidence that stroke risk is not linear: The non-linear Framingham stroke risk score.

In PloS one ; h5-index 176.0

Current stroke risk assessment tools presume the impact of risk factors is linear and cumulative. However, both novel risk factors and their interplay influencing stroke incidence are difficult to reveal using traditional additive models. The goal of this study was to improve upon the established Revised Framingham Stroke Risk Score and design an interactive Non-Linear Stroke Risk Score. Leveraging machine learning algorithms, our work aimed at increasing the accuracy of event prediction and uncovering new relationships in an interpretable fashion. A two-phase approach was used to create our stroke risk prediction score. First, clinical examinations of the Framingham offspring cohort were utilized as the training dataset for the predictive model. Optimal Classification Trees were used to develop a tree-based model to predict 10-year risk of stroke. Unlike classical methods, this algorithm adaptively changes the splits on the independent variables, introducing non-linear interactions among them. Second, the model was validated with a multi-ethnicity cohort from the Boston Medical Center. Our stroke risk score suggests a key dichotomy between patients with history of cardiovascular disease and the rest of the population. While it agrees with known findings, it also identified 23 unique stroke risk profiles and highlighted new non-linear relationships; such as the role of T-wave abnormality on electrocardiography and hematocrit levels in a patient's risk profile. Our results suggested that the non-linear approach significantly improves upon the baseline in the c-statistic (training 87.43% (CI 0.85-0.90) vs. 73.74% (CI 0.70-0.76); validation 75.29% (CI 0.74-0.76) vs 65.93% (CI 0.64-0.67), even in multi-ethnicity populations. The clinical implications of the new risk score include prioritization of risk factor modification and personalized care at the patient level with improved targeting of interventions for stroke prevention.

Orfanoudaki Agni, Chesley Emma, Cadisch Christian, Stein Barry, Nouh Amre, Alberts Mark J, Bertsimas Dimitris

2020

General General

Oral microbiome-systemic link studies: perspectives on current limitations and future artificial intelligence-based approaches.

In Critical reviews in microbiology

In the past decade, there has been a tremendous increase in studies on the link between oral microbiome and systemic diseases. However, variations in study design and confounding variables across studies often lead to inconsistent observations. In this narrative review, we have discussed the potential influence of study design and confounding variables on the current sequencing-based oral microbiome-systemic disease link studies. The current limitations of oral microbiome-systemic link studies on type 2 diabetes mellitus, rheumatoid arthritis, pregnancy, atherosclerosis, and pancreatic cancer are discussed in this review, followed by our perspective on how artificial intelligence (AI), particularly machine learning and deep learning approaches, can be employed for predicting systemic disease and host metadata from the oral microbiome. The application of AI for predicting systemic disease as well as host metadata requires the establishment of a global database repository with microbiome sequences and annotated host metadata. However, this task requires collective efforts from researchers working in the field of oral microbiome to establish more comprehensive datasets with appropriate host metadata. Development of AI-based models by incorporating consistent host metadata will allow prediction of systemic diseases with higher accuracies, bringing considerable clinical benefits.

Seneviratne Chaminda Jayampath, Balan Preethi, Suriyanarayanan Tanujaa, Lakshmanan Meiyappan, Lee Dong-Yup, Rho Mina, Jakubovics Nicholas, Brandt Bernd, Crielaard Wim, Zaura Egija

2020-May-21

High-throughput DNA sequencing, deep learning, machine learning, metagenomics, oral health, saliva

Radiology Radiology

A review on the use of artificial intelligence for medical imaging of the lungs of patients with coronavirus disease 2019.

In Diagnostic and interventional radiology (Ankara, Turkey)

The results of research on the use of artificial intelligence (AI) for medical imaging of the lungs of patients with coronavirus disease 2019 (COVID-19) has been published in various forms. In this study, we reviewed the AI for diagnostic imaging of COVID-19 pneumonia. PubMed, arXiv, medRxiv, and Google scholar were used to search for AI studies. There were 15 studies of COVID-19 that used AI for medical imaging. Of these, 11 studies used AI for computed tomography (CT) and 4 used AI for chest radiography. Eight studies presented independent test data, 5 used disclosed data, and 4 disclosed the AI source codes. The number of datasets ranged from 106 to 5941, with sensitivities ranging from 0.67-1.00 and specificities ranging from 0.81-1.00 for prediction of COVID-19 pneumonia. Four studies with independent test datasets showed a breakdown of the data ratio and reported prediction of COVID-19 pneumonia with sensitivity, specificity, and area under the curve (AUC). These 4 studies showed very high sensitivity, specificity, and AUC, in the range of 0.9-0.98, 0.91-0.96, and 0.96-0.99, respectively.

Ito Rintaro, Iwano Shingo, Naganawa Shinji

2020-May-21

Radiology Radiology

SODA: Detecting Covid-19 in Chest X-rays with Semi-supervised Open Set Domain Adaptation

ArXiv Preprint

The global pandemic of COVID-19 has infected millions of people since its first outbreak in last December. A key challenge for preventing and controlling COVID-19 is how to quickly, widely, and effectively implement the test for the disease, because testing is the first step to break the chains of transmission. To assist the diagnosis of the disease, radiology imaging is used to complement the screening process and triage patients into different risk levels. Deep learning methods have taken a more active role in automatically detecting COVID-19 disease in chest x-ray images, as witnessed in many recent works. Most of these works first train a CNN on an existing large-scale chest x-ray image dataset and then fine-tune it with a COVID-19 dataset at a much smaller scale. However, direct transfer across datasets from different domains may lead to poor performance due to visual domain shift. Also, the small scale of the COVID-19 dataset on the target domain can make the training fall into the overfitting trap. To solve all these crucial problems and fully exploit the available large-scale chest x-ray image dataset, we formulate the problem of COVID-19 chest x-ray image classification in a semi-supervised open set domain adaptation setting, through which we are motivated to reduce the domain shift and avoid overfitting when training on a very small dataset of COVID-19. In addressing this formulated problem, we propose a novel Semi-supervised Open set Domain Adversarial network (SODA), which is able to align the data distributions across different domains in a general domain space and also in a common subspace of source and target data. In our experiments, SODA achieves a leading classification performance compared with recent state-of-the-art models, as well as effectively separating COVID-19 with common pneumonia.

Jieli Zhou, Baoyu Jing, Zeya Wang

2020-05-22

Radiology Radiology

SODA: Detecting Covid-19 in Chest X-rays with Semi-supervised Open Set Domain Adaptation

ArXiv Preprint

The global pandemic of COVID-19 has infected millions of people since its first outbreak in last December. A key challenge for preventing and controlling COVID-19 is how to quickly, widely, and effectively implement the test for the disease, because testing is the first step to break the chains of transmission. To assist the diagnosis of the disease, radiology imaging is used to complement the screening process and triage patients into different risk levels. Deep learning methods have taken a more active role in automatically detecting COVID-19 disease in chest x-ray images, as witnessed in many recent works. Most of these works first train a CNN on an existing large-scale chest x-ray image dataset and then fine-tune it with a COVID-19 dataset at a much smaller scale. However, direct transfer across datasets from different domains may lead to poor performance due to visual domain shift. Also, the small scale of the COVID-19 dataset on the target domain can make the training fall into the overfitting trap. To solve all these crucial problems and fully exploit the available large-scale chest x-ray image dataset, we formulate the problem of COVID-19 chest x-ray image classification in a semi-supervised open set domain adaptation setting, through which we are motivated to reduce the domain shift and avoid overfitting when training on a very small dataset of COVID-19. In addressing this formulated problem, we propose a novel Semi-supervised Open set Domain Adversarial network (SODA), which is able to align the data distributions across different domains in a general domain space and also in a common subspace of source and target data. In our experiments, SODA achieves a leading classification performance compared with recent state-of-the-art models, as well as effectively separating COVID-19 with common pneumonia.

Jieli Zhou, Baoyu Jing, Zeya Wang

2020-05-22

General General

Exhaustive Product Analysis of Three Benzene Discharges by Microwave Spectroscopy.

In The journal of physical chemistry. A

Using chirped and cavity microwave spectroscopies, automated double resonance, new high-speed fitting and deep learning algorithms, and large databases of computed structures, the discharge products of benzene alone, or in combination with molecular oxygen or nitrogen, have been exhaustively characterized between 6.5 and 26 GHz. In total, more than 3300 spectral features were observed; 88% of these, accounting for 97% of the total intensity, have now been assigned to 160 distinct chemical species and 60 of their variants (i.e. isotopic species and vibrationally excited states). Roughly 50 of the products are entirely new or poorly characterized at high resolution, including many heavier by mass than the precursor benzene. These findings provide direct evidence for a rich architecture of two- and three-dimensional carbon, and indicate that benzene growth, particularly formation of ring-chain molecules, occurs facilely under our experimental conditions. The present analysis also illustrates the utility of microwave spectroscopy as a precision tool for complex mixture analysis, irrespective of whether the rotational spectrum of a product species is known a priori or not. From this large quantity of data, for example, it is possible to determine with confidence the relative abundances of different product masses, but more importantly the relative abundances of different isomers with the same mass. The complementary nature of this type of analysis to traditional mass spectrometry is discussed.

McCarthy Michael C, Lee Kin Long Kelvin, Carroll Paul Brandon, Porterfield Jessica P, Changala Bryan, Thorpe James H, Stanton John F

2020-May-21

General General

Accurate multistage prediction of protein crystallization propensity using deep-cascade forest with sequence-based features.

In Briefings in bioinformatics

X-ray crystallography is the major approach for determining atomic-level protein structures. Because not all proteins can be easily crystallized, accurate prediction of protein crystallization propensity provides critical help in guiding experimental design and improving the success rate of X-ray crystallography experiments. This study has developed a new machine-learning-based pipeline that uses a newly developed deep-cascade forest (DCF) model with multiple types of sequence-based features to predict protein crystallization propensity. Based on the developed pipeline, two new protein crystallization propensity predictors, denoted as DCFCrystal and MDCFCrystal, have been implemented. DCFCrystal is a multistage predictor that can estimate the success propensities of the three individual steps (production of protein material, purification and production of crystals) in the protein crystallization process. MDCFCrystal is a single-stage predictor that aims to estimate the probability that a protein will pass through the entire crystallization process. Moreover, DCFCrystal is designed for general proteins, whereas MDCFCrystal is specially designed for membrane proteins, which are notoriously difficult to crystalize. DCFCrystal and MDCFCrystal were separately tested on two benchmark datasets consisting of 12 289 and 950 proteins, respectively, with known crystallization results from various experimental records. The experimental results demonstrated that DCFCrystal and MDCFCrystal increased the value of Matthew's correlation coefficient by 199.7% and 77.8%, respectively, compared to the best of other state-of-the-art protein crystallization propensity predictors. Detailed analyses show that the major advantages of DCFCrystal and MDCFCrystal lie in the efficiency of the DCF model and the sensitivity of the sequence-based features used, especially the newly designed pseudo-predicted hybrid solvent accessibility (PsePHSA) feature, which improves crystallization recognition by incorporating sequence-order information with solvent accessibility of residues. Meanwhile, the new crystal-dataset constructions help to train the models with more comprehensive crystallization knowledge.

Zhu Yi-Heng, Hu Jun, Ge Fang, Li Fuyi, Song Jiangning, Zhang Yang, Yu Dong-Jun

2020-May-20

bioinformatics, deep-cascade forest, predictor, protein crystallization propensity, sequence-based feature

Radiology Radiology

A review on the use of artificial intelligence for medical imaging of the lungs of patients with coronavirus disease 2019.

In Diagnostic and interventional radiology (Ankara, Turkey)

The results of research on the use of artificial intelligence (AI) for medical imaging of the lungs of patients with coronavirus disease 2019 (COVID-19) has been published in various forms. In this study, we reviewed the AI for diagnostic imaging of COVID-19 pneumonia. PubMed, arXiv, medRxiv, and Google scholar were used to search for AI studies. There were 15 studies of COVID-19 that used AI for medical imaging. Of these, 11 studies used AI for computed tomography (CT) and 4 used AI for chest radiography. Eight studies presented independent test data, 5 used disclosed data, and 4 disclosed the AI source codes. The number of datasets ranged from 106 to 5941, with sensitivities ranging from 0.67-1.00 and specificities ranging from 0.81-1.00 for prediction of COVID-19 pneumonia. Four studies with independent test datasets showed a breakdown of the data ratio and reported prediction of COVID-19 pneumonia with sensitivity, specificity, and area under the curve (AUC). These 4 studies showed very high sensitivity, specificity, and AUC, in the range of 0.9-0.98, 0.91-0.96, and 0.96-0.99, respectively.

Ito Rintaro, Iwano Shingo, Naganawa Shinji

2020-May-21

General General

Global burden of sleep-disordered breathing and its implications.

In Respirology (Carlton, Vic.)

One-seventh of the world's adult population, or approximately one billion people, are estimated to have OSA. Over the past four decades, obesity, the main risk factor for OSA, has risen in striking proportion worldwide. In the past 5 years, the WHO estimates global obesity to affect almost two billion adults. A second major risk factor for OSA is advanced age. As the prevalence of the ageing population and obesity increases, the vulnerability towards having OSA increases. In addition to these traditional OSA risk factors, studies of the global population reveal select contributing features and phenotypes, including extreme phenotypes and symptom clusters that deserve further examination. Untreated OSA is associated with significant comorbidities and mortality. These represent a tremendous threat to the individual and global health. Beyond the personal toll, the economic costs of OSA are far-reaching, affecting the individual, family and society directly and indirectly, in terms of productivity and public safety. A better understanding of the pathophysiology, individual and ethnic similarities and differences is needed to better facilitate management of this chronic disease. In some countries, measures of the OSA disease burden are sparse. As the global burden of OSA and its associated comorbidities are projected to further increase, the infrastructure to diagnose and manage OSA will need to adapt. The use of novel approaches (electronic health records and artificial intelligence) to stratify risk, diagnose and affect treatment are necessary. Together, a unified multi-disciplinary, multi-organizational, global approach will be needed to manage this disease.

Lyons M Melanie, Bhatt Nitin Y, Pack Allan I, Magalang Ulysses J

2020-May-21

economics, global burden, obesity, obstructive sleep apnoea, risk factors

Pathology Pathology

Deep learning-guided joint attenuation and scatter correction in multitracer neuroimaging studies.

In Human brain mapping

PET attenuation correction (AC) on systems lacking CT/transmission scanning, such as dedicated brain PET scanners and hybrid PET/MRI, is challenging. Direct AC in image-space, wherein PET images corrected for attenuation and scatter are synthesized from nonattenuation corrected PET (PET-nonAC) images in an end-to-end fashion using deep learning approaches (DLAC) is evaluated for various radiotracers used in molecular neuroimaging studies. One hundred eighty brain PET scans acquired using 18 F-FDG, 18 F-DOPA, 18 F-Flortaucipir (targeting tau pathology), and 18 F-Flutemetamol (targeting amyloid pathology) radiotracers (40 + 5, training/validation + external test, subjects for each radiotracer) were included. The PET data were reconstructed using CT-based AC (CTAC) to generate reference PET-CTAC and without AC to produce PET-nonAC images. A deep convolutional neural network was trained to generate PET attenuation corrected images (PET-DLAC) from PET-nonAC. The quantitative accuracy of this approach was investigated separately for each radiotracer considering the values obtained from PET-CTAC images as reference. A segmented AC map (PET-SegAC) containing soft-tissue and background air was also included in the evaluation. Quantitative analysis of PET images demonstrated superior performance of the DLAC approach compared to SegAC technique for all tracers. Despite the relatively low quantitative bias observed when using the DLAC approach, this approach appears vulnerable to outliers, resulting in noticeable local pseudo uptake and false cold regions. Direct AC in image-space using deep learning demonstrated quantitatively acceptable performance with less than 9% absolute SUV bias for the four different investigated neuroimaging radiotracers. However, this approach is vulnerable to outliers which result in large local quantitative bias.

Arabi Hossein, Bortolin Karin, Ginovart Nathalie, Garibotto Valentina, Zaidi Habib

2020-May-21

PET, attenuation correction, deep learning, neuroimaging tracers, quantification

Public Health Public Health

Expansion of the dimensions in the current management of acute ischemic stroke.

In Journal of neurology

Stroke is the fifth leading cause of death in the United States with a huge burden on health care. Acute ischemic stroke (AIS) accounts for 87% of all stroke. The use of thrombolytic agents in AIS treatment is well known since 1950 but no FDA approval until 1996, due to lack of strong evidence showing benefits outweigh the risk of intracranial hemorrhage. The NINDS trial led to the approval of intravenous tissue plasminogen activator treatment (IV recombinant tPA) within 3 h of stroke. Due to this limitation of 3-4.5 h. window, evolution began in the development of effective endovascular therapy (EVT). Multiple trials were unsuccessful in establishing the strong evidence for effectiveness of EVT. In 2015, MR CLEAN trial made progress and showed improved outcomes with EVT in AIS patients with large vessel occlusion (LVO), with 6-h window period. In 2018, two major trials-DAWN and DEFUSE 3-along with few other trials had shown improved outcomes with EVT and stretched window period from 6 to 24 h. AHA Stroke Council is constantly working to provide focused guidelines and recommendations in AIS management since 2013. SVIN had started the initiative "Mission Thrombectomy-2020" to increase global EVT utilization rate 202,000 procedures by 2020. Physicians are using safer and easier approach like brachial and radial approach for EVT. TeleNeurology and artificial intelligence also played a significant role in increasing the availability of IV recombinant tPA in AIS treatment in remote hospitals and also in screening, triaging and identifying LVO patients for EVT. In this review article, we aim to describe the history of stroke management along with the new technological advancements in AIS treatment.

Malik Preeti, Anwar Arsalan, Patel Ruti, Patel Urvish

2020-May-20

Acute ischemic stroke, Artificial intelligence and stem cell therapy, DAWN, DEFUSE 3, Endovascular therapy, Large vessel occlusion, Telestroke

General General

Viewpoint on Time Series and Interrupted Time Series Optimum Modeling for Predicting Arthritic Disease Outcomes.

In Current rheumatology reports ; h5-index 35.0

PURPOSE OF REVIEW : The propose of this viewpoint is to improve or facilitate the clinical decision-making in the management/treatment strategies of arthritis patients through knowing, understanding, and having access to an interactive process allowing assessment of the patient disease outcome in the future.

RECENT FINDINGS : In recent years, the time series (TS) concept has become the center of attention as a predictive model for making forecast of unseen data values. TS and one of its technologies, the interrupted TS (ITS) analysis (TS with one or more interventions), predict the next period(s) value(s) of a given patient based on their past and current information. Traditional TS/ITS methods involve segmented regression-based technologies (linear and nonlinear), while stochastic (linear modeling) and artificial intelligence approaches, including machine learning (complex nonlinear relationships between variables), are also used; however, each have limitations. We will briefly describe TS/ITS, provide examples of their application in arthritic diseases; describe their methods, challenges, and limitations; and propose a combined (stochastic and artificial intelligence) procedure in post-intervention that will optimize ITS modeling. This combined method will increase the accuracy of ITS modeling by profiting from the advantages of both stochastic and nonlinear models to capture all ITS deterministic and stochastic components. In addition, this combined method will allow ITS outcomes to be predicted as continuous variables without having to consider the time lag produced between the pre- and post-intervention periods, thus minimizing the prediction error not only for the given data but also for all possible future patterns in ITS. The use of reliable prediction methodologies for arthritis patients will permit treatment of not only the disease, but also the patient with the disease, ensuring the best outcome prediction for the patient.

Bonakdari Hossein, Pelletier Jean-Pierre, Martel-Pelletier Johanne

2020-May-20

Arthritis, Clinical decision-making, Data-driven, Interrupted time series, Management/treatment strategies, Time series

General General

Discontinuous rock slope stability analysis under blocky structural sliding by fuzzy key-block analysis method.

In Heliyon

This study presents a fuzzy logical decision-making algorithm based on block theory to effectively determine discontinuous rock slope reliability under various wedge and planar slip scenarios. The algorithm was developed to provide rapid response operations without the need for extensive quantitative stability evaluations based on the rock slope sustainability ratio. The fuzzy key-block analysis method utilises a weighted rational decision (multi-criteria decision-making) function to prepare the 'degree of reliability (degree of stability-instability contingency)' for slopes as implemented through the Mathematica software package. The central and analyst core of the proposed algorithm is provided as based on discontinuity network geometrical uncertainties and hierarchical decision-making. This algorithm uses block theory principles to proceed to rock block classification, movable blocks and key-block identifications under ambiguous terms which investigates the sustainability ratio with accurate, quick and appropriate decisions especially for novice engineers in the context of discontinuous rock slope stability analysis. The method with very high precision and speed has particular matches with the existing procedures and has the potential to be utilised as a continuous decision-making system for discrete parameters and to minimise the need to apply common practises. In order to justify the algorithm, a number of discontinuous rock mass slopes were considered as examples. In addition, the SWedge, RocPlane softwares and expert assignments (25-member specialist team) were utilised for verification of the applied algorithm which led to a conclusion that the algorithm was successful in providing rational decision-making.

Azarafza Mohammad, Akgün Haluk, Feizi-Derakhshi Mohammad-Reza, Azarafza Mehdi, Rahnamarad Jafar, Derakhshani Reza

2020-May

Applied mathematics, Artificial intelligence, Discontinuity network, Discontinuous rock slope, Earth sciences, Engineering, Fuzzy logic, Geological engineering, Geomechanics, Geotechnical engineering, Mathematics, Multi-criteria decision-making, Rock mechanics, Stability analysis, Weighted decision functions

Surgery Surgery

AI-doscopist: a real-time deep-learning-based algorithm for localising polyps in colonoscopy videos with edge computing devices.

In NPJ digital medicine

We have designed a deep-learning model, an "Artificial Intelligent Endoscopist (a.k.a. AI-doscopist)", to localise colonic neoplasia during colonoscopy. This study aims to evaluate the agreement between endoscopists and AI-doscopist for colorectal neoplasm localisation. AI-doscopist was pre-trained by 1.2 million non-medical images and fine-tuned by 291,090 colonoscopy and non-medical images. The colonoscopy images were obtained from six databases, where the colonoscopy images were classified into 13 categories and the polyps' locations were marked image-by-image by the smallest bounding boxes. Seven categories of non-medical images, which were believed to share some common features with colorectal polyps, were downloaded from an online search engine. Written informed consent were obtained from 144 patients who underwent colonoscopy and their full colonoscopy videos were prospectively recorded for evaluation. A total of 128 suspicious lesions were resected or biopsied for histological confirmation. When evaluated image-by-image on the 144 full colonoscopies, the specificity of AI-doscopist was 93.3%. AI-doscopist were able to localise 124 out of 128 polyps (polyp-based sensitivity = 96.9%). Furthermore, after reviewing the suspected regions highlighted by AI-doscopist in a 102-patient cohort, an endoscopist has high confidence in recognizing four missed polyps in three patients who were not diagnosed with any lesion during their original colonoscopies. In summary, AI-doscopist can localise 96.9% of the polyps resected by the endoscopists. If AI-doscopist were to be used in real-time, it can potentially assist endoscopists in detecting one more patient with polyp in every 20-33 colonoscopies.

Poon Carmen C Y, Jiang Yuqi, Zhang Ruikai, Lo Winnie W Y, Cheung Maggie S H, Yu Ruoxi, Zheng Yali, Wong John C T, Liu Qing, Wong Sunny H, Mak Tony W C, Lau James Y W

2020

Cancer, Translational research

General General

Cognitive plausibility in voice-based AI health counselors.

In NPJ digital medicine

Voice-based personal assistants using artificial intelligence (AI) have been widely adopted and used in home-based settings. Their success has created considerable interest for its use in healthcare applications; one area of prolific growth in AI is that of voice-based virtual counselors for mental health and well-being. However, in spite of its promise, building realistic virtual counselors to achieve higher-order maturity levels beyond task-based interactions presents considerable conceptual and pragmatic challenges. We describe one such conceptual challenge-cognitive plausibility, defined as the ability of virtual counselors to emulate the human cognitive system by simulating how a skill or function is accomplished. An important cognitive plausibility consideration for voice-based agents is its ability to engage in meaningful and seamless interactive communication. Drawing on a broad interdisciplinary research literature and based on our experiences with developing two voice-based (voice-only) prototypes that are in the early phases of testing, we articulate two conceptual considerations for their design and use-conceptualizing voice-based virtual counselors as communicative agents and establishing virtual co-presence. We discuss why these conceptual considerations are important and how it can lead to the development of voice-based counselors for real-world use.

Kannampallil Thomas, Smyth Joshua M, Jones Steve, Payne Philip R O, Ma Jun

2020

Health services, Translational research

General General

Week 53

General General

A Machine Learning Approach to Identification of Unhealthy Drinking.

In Journal of the American Board of Family Medicine : JABFM

INTRODUCTION : Unhealthy drinking is prevalent in the United States, and yet it is underidentified and undertreated. Identifying unhealthy drinkers can be time-consuming and uncomfortable for primary care providers. An automated rule for identification would focus attention on patients most likely to need care and, therefore, increase efficiency and effectiveness. The objective of this study was to build a clinical prediction tool for unhealthy drinking based on routinely available demographic and laboratory data.

METHODS : We obtained 38 demographic and laboratory variables from the National Health and Nutrition Examination Survey (1999 to 2016) on 43,545 nationally representative adults who had information on alcohol use available as a reference standard. Logistic regression, support vector machines, k-nearest neighbor, neural networks, decision trees, and random forests were used to build clinical prediction models. The model with the largest area under the receiver operator curve was selected to build the prediction tool.

RESULTS : A random forest model with 15 variables produced the largest area under the receiver operator curve (0.78) in the test set. The most influential predictors were age, current smoker, hemoglobin, sex, and high-density lipoprotein. The optimum operating point had a sensitivity of 0.50, specificity of 0.86, positive predictive value of 0.55, and negative predictive value of 0.83. Application of the tool resulted in a much smaller target sample (75% reduced).

CONCLUSION : Using commonly available data, a decision tool can identify a subset of patients who seem to warrant clinical attention for unhealthy drinking, potentially increasing the efficiency and reach of screening.

Bonnell Levi N, Littenberg Benjamin, Wshah Safwan R, Rose Gail L

Alcohol Drinking, Alcoholism, Area Under Curve, Clinical Decision Rules, Decision Trees, Logistic Models, Machine Learning, Neural Networks (Computer), Nutrition Surveys, Support Vector Machine

General General

QTG-Finder2: A Generalized Machine-Learning Algorithm for Prioritizing QTL Causal Genes in Plants.

In G3 (Bethesda, Md.)

Linkage mapping has been widely used to identify quantitative trait loci (QTL) in many plants and usually requires a time-consuming and labor-intensive fine mapping process to find the causal gene underlying the QTL. Previously, we described QTG-Finder, a machine-learning algorithm to rationally prioritize candidate causal genes in QTLs. While it showed good performance, QTG-Finder could only be used in Arabidopsis and rice because of the limited number of known causal genes in other species. Here we tested the feasibility of enabling QTG-Finder to work on species that have few or no known causal genes by using orthologs of known causal genes as training set. The model trained with orthologs could recall about 64% of Arabidopsis and 83% of rice causal genes when the top 20% ranked genes were considered, which is similar to the performance of models trained with known causal genes. The average precision was 0.027 for Arabidopsis and 0.029 for rice. We further extended the algorithm to include polymorphisms in conserved non-coding sequences and gene presence/absence variation as additional features. Using this algorithm, QTG-Finder2, we trained and cross-validated Sorghum bicolor and Setaria viridis models. The S. bicolor model was validated by causal genes curated from the literature and could recall 70% of causal genes when the top 20% ranked genes were considered. In addition, we applied the S. viridis model and public transcriptome data to prioritize a plant height QTL and identified 13 candidate genes. QTL-Finder2 can accelerate the discovery of causal genes in any plant species and facilitate agricultural trait improvement.

Lin Fan, Lazarus Elena Z, Rhee Seung Y

2020-May-19

Setaria viridis, Sorghum bicolor, causal genes, machine learning, quantitative trait loci

General General

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach.

In BMC psychiatry

BACKGROUND : Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models.

METHODS : This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses.

RESULTS : Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68, 66 and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD.

CONCLUSIONS : The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD.

TRIAL REGISTRATION : ClinicalTrials.gov ID: NCT02010619.

Flygare Oskar, Enander Jesper, Andersson Erik, Ljótsson Brjánn, Ivanov Volen Z, Mataix-Cols David, Rück Christian

2020-May-19

Body dysmorphic disorder, Cognitive behaviour therapy, Internet, Machine learning, Predictor

Radiology Radiology

Longitudinal functional and imaging outcome measures in FKRP limb-girdle muscular dystrophy.

In BMC neurology

BACKGROUND : Pathogenic variants in the FKRP gene cause impaired glycosylation of α-dystroglycan in muscle, producing a limb-girdle muscular dystrophy with cardiomyopathy. Despite advances in understanding the pathophysiology of FKRP-associated myopathies, clinical research in the limb-girdle muscular dystrophies has been limited by the lack of normative biomarker data to gauge disease progression.

METHODS : Participants in a phase 2 clinical trial were evaluated over a 4-month, untreated lead-in period to evaluate repeatability and to obtain normative data for timed function tests, strength tests, pulmonary function, and body composition using DEXA and whole-body MRI. Novel deep learning algorithms were used to analyze MRI scans and quantify muscle, fat, and intramuscular fat infiltration in the thighs. T-tests and signed rank tests were used to assess changes in these outcome measures.

RESULTS : Nineteen participants were observed during the lead-in period for this trial. No significant changes were noted in the strength, pulmonary function, or body composition outcome measures over the 4-month observation period. One timed function measure, the 4-stair climb, showed a statistically significant difference over the observation period. Quantitative estimates of muscle, fat, and intramuscular fat infiltration from whole-body MRI corresponded significantly with DEXA estimates of body composition, strength, and timed function measures.

CONCLUSIONS : We describe normative data and repeatability performance for multiple physical function measures in an adult FKRP muscular dystrophy population. Our analysis indicates that deep learning algorithms can be used to quantify healthy and dystrophic muscle seen on whole-body imaging.

TRIAL REGISTRATION : This study was retrospectively registered in clinicaltrials.gov (NCT02841267) on July 22, 2016 and data supporting this study has been submitted to this registry.

Leung Doris G, Bocchieri Alex E, Ahlawat Shivani, Jacobs Michael A, Parekh Vishwa S, Braverman Vladimir, Summerton Katherine, Mansour Jennifer, Bibat Genila, Morris Carl, Marraffino Shannon, Wagner Kathryn R

2020-May-19

Biomarkers, Convolutional neural network, Deep learning, FKRP, Limb-girdle muscular dystrophy, Tissue signatures, Whole-body MRI

oncology Oncology

Direct comparison shows that mRNA-based diagnostics incorporate information which cannot be learned directly from genomic mutations.

In BMC bioinformatics

BACKGROUND : Compared to the many uses of DNA-level testing in clinical oncology, development of RNA-based diagnostics has been more limited. An exception to this trend is the growing use of mRNA-based methods in early-stage breast cancer. Although DNA and mRNA are used together in breast cancer research, the distinct contribution of mRNA beyond that of DNA in clinical challenges has not yet been directly assessed. We hypothesize that mRNA harbors prognostically useful information independently of genomic variation. To validate this, we use both genomic mutations and gene expression to predict five-year breast cancer recurrence in an integrated test model. This is accomplished first by comparing the feature importance of DNA and mRNA features in a model trained on both, and second, by evaluating the difference in performance of models trained on DNA and mRNA data separately.

RESULTS : We find that models trained on DNA and mRNA data give more weight to mRNA features than to DNA features, and models trained only on mRNA outperform models trained on DNA alone.

CONCLUSIONS : The evaluation process presented here may serve as a framework for the interpretation of the relative contribution of individual molecular markers. It also suggests that mRNA has a distinct contribution in a diagnostic setting, beyond and independently of DNA mutation data.

Ravkin Hersh D, Givton Ofer, Geffen David B, Rubin Eitan

2020-May-19

Breast cancer recurrence, Data science, Gene expression, Genomics, Machine learning, Machine learning explainability, Oncology

Public Health Public Health

No Place Like Home: A Cross-National Assessment of the Efficacy of Social Distancing during the COVID-19 Pandemic.

In JMIR public health and surveillance

BACKGROUND : In the absence of a cure in the time of pandemics, social distancing measures seem to be the most effective intervention to slow down the spread of disease. Various simulation-based studies have been conducted in the past to investigate the effectiveness of such measures. While those studies unanimously confirm the mitigating effect of social distancing on the disease spread, the reported effectiveness varies from 10% to more than 90% reduction in the number of infections. This level of uncertainty is mostly due to the complex dynamics of epidemics and their time-variant parameters. A real transactional data, however, can reduce the uncertainty and provide a less noisy picture of social distancing effectiveness.

OBJECTIVE : In this paper, we integrate multiple transactional data sets (GPS mobility data from Google and Apple as well as disease statistics data from ECDC) to study the role of social distancing policies in 26 countries wherein the transmission rate of the COVID-19 pandemic is analyzed over the course of five weeks.

METHODS : Relying on the SIR model and official COVID-19 reports, we first calculated the weekly transmission rate (β) of the coronavirus disease in 26 countries for five consecutive weeks. Then we integrated that with the Google's and Apple's mobility data sets for the same time frame and used a machine learning approach to investigate the relationship between mobility factors and β values.

RESULTS : Gradient Boosted Trees (GBT) regression analysis showed that changes in mobility patterns, resulted from social distancing policies, explain around 47% of the variation in the disease transmission rate.

CONCLUSIONS : Consistent with simulation-based studies, real cross-national transactional data confirms the effectiveness of social distancing interventions in slowing down the spread of the disease. Apart from providing less noisy and more generalizable support for the whole social distancing idea, we provide specific insights for public health policy-makers as to what locations should be given a higher priority for enforcing social distancing measures.

CLINICALTRIAL :

Delen Dursun, Eryarsoy Enes, Davazdahemami Behrooz

2020-May-20

Ophthalmology Ophthalmology

DR|GRADUATE: Uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images.

In Medical image analysis

Diabetic retinopathy (DR) grading is crucial in determining the adequate treatment and follow up of patient, but the screening process can be tiresome and prone to errors. Deep learning approaches have shown promising performance as computer-aided diagnosis (CAD) systems, but their black-box behaviour hinders clinical application. We propose DR|GRADUATE, a novel deep learning-based DR grading CAD system that supports its decision by providing a medically interpretable explanation and an estimation of how uncertain that prediction is, allowing the ophthalmologist to measure how much that decision should be trusted. We designed DR|GRADUATE taking into account the ordinal nature of the DR grading problem. A novel Gaussian-sampling approach built upon a Multiple Instance Learning framework allow DR|GRADUATE to infer an image grade associated with an explanation map and a prediction uncertainty while being trained only with image-wise labels. DR|GRADUATE was trained on the Kaggle DR detection training set and evaluated across multiple datasets. In DR grading, a quadratic-weighted Cohen's kappa (κ) between 0.71 and 0.84 was achieved in five different datasets. We show that high κ values occur for images with low prediction uncertainty, thus indicating that this uncertainty is a valid measure of the predictions' quality. Further, bad quality images are generally associated with higher uncertainties, showing that images not suitable for diagnosis indeed lead to less trustworthy predictions. Additionally, tests on unfamiliar medical image data types suggest that DR|GRADUATE allows outlier detection. The attention maps generally highlight regions of interest for diagnosis. These results show the great potential of DR|GRADUATE as a second-opinion system in DR severity grading.

Araújo Teresa, Aresta Guilherme, Mendonça Luís, Penas Susana, Maia Carolina, Carneiro Ângela, Mendonça Ana Maria, Campilho Aurélio

2020-Apr-30

Deep learning, Diabetic retinopathy grading, Explainability, Uncertainty

General General

Acute and sub-acute stroke lesion segmentation from multimodal MRI.

In Computer methods and programs in biomedicine

BACKGROUND AND OBJECTIVE : Acute stroke lesion segmentation tasks are of great clinical interest as they can help doctors make better informed time-critical treatment decisions. Magnetic resonance imaging (MRI) is time demanding but can provide images that are considered the gold standard for diagnosis. Automated stroke lesion segmentation can provide with an estimate of the location and volume of the lesioned tissue, which can help in the clinical practice to better assess and evaluate the risks of each treatment.

METHODS : We propose a deep learning methodology for acute and sub-acute stroke lesion segmentation using multimodal MR imaging. We pre-process the data to facilitate learning features based on the symmetry of brain hemispheres. The issue of class imbalance is tackled using small patches with a balanced training patch sampling strategy and a dynamically weighted loss function. Moreover, a combination of whole patch predictions, using a U-Net based CNN architecture, and high degree of overlapping patches reduces the need for additional post-processing.

RESULTS : The proposed method is evaluated using two public datasets from the 2015 Ischemic Stroke Lesion Segmentation challenge (ISLES 2015). These involve the tasks of sub-acute stroke lesion segmentation (SISS) and acute stroke penumbra estimation (SPES) from multiple diffusion, perfusion and anatomical MRI modalities. The performance is compared against state-of-the-art methods with a blind online testing set evaluation on each of the challenges. At the time of submitting this manuscript, our approach is the first method in the online rankings for the SISS (DSC=0.59 ± 0.31) and SPES sub-tasks (DSC=0.84 ± 0.10). When compared with the rest of submitted strategies, we achieve top rank performance with a lower Hausdorff distance.

CONCLUSIONS : Better segmentation results are obtained by leveraging the anatomy and pathophysiology of acute stroke lesions and using a combined approach to minimize the effects of class imbalance. The same training procedure is used for both tasks, showing the proposed methodology can generalize well enough to deal with different unrelated tasks and imaging modalities without hyper-parameter tuning. In order to promote the reproducibility of our results, a public version of the proposed method has been released to the scientific community.

Clèrigues Albert, Valverde Sergi, Bernal Jose, Freixenet Jordi, Oliver Arnau, Lladó Xavier

2020-May-06

Automatic lesion segmentation, Brain, Convolutional neural networks, Deep learning, Ischemic stroke, MRI

General General

Identifying Suspect Bat Reservoirs of Emerging Infections.

In Vaccines

Bats host a number of pathogens that cause severe disease and onward transmission in humans and domestic animals. Some of these pathogens, including henipaviruses and filoviruses, are considered a concern for future pandemics. There has been substantial effort to identify these viruses in bats. However, the reservoir hosts for Ebola virus are still unknown and henipaviruses are largely uncharacterized across their distribution. Identifying reservoir species is critical in understanding the viral ecology within these hosts and the conditions that lead to spillover. We collated surveillance data to identify taxonomic patterns in prevalence and seroprevalence and to assess sampling efforts across species. We systematically collected data on filovirus and henipavirus detections and used a machine-learning algorithm, phylofactorization, in order to search the bat phylogeny for cladistic patterns in filovirus and henipavirus infection, accounting for sampling efforts. Across sampled bat species, evidence for filovirus infection was widely dispersed across the sampled phylogeny. We found major gaps in filovirus sampling in bats, especially in Western Hemisphere species. Evidence for henipavirus infection was clustered within the Pteropodidae; however, no other clades have been as intensely sampled. The major predictor of filovirus and henipavirus exposure or infection was sampling effort. Based on these results, we recommend expanding surveillance for these pathogens across the bat phylogenetic tree.

Crowley Daniel, Becker Daniel, Washburne Alex, Plowright Raina

2020-May-17

Ebola, Nipah, bats, phylofactor, phylogenetics

General General

Concentration estimation of dissolved oxygen in Pearl River Basin using input variable selection and machine learning techniques.

In The Science of the total environment

Dissolved oxygen (DO) concentration is an essential index for water environment assessment. Here, we present a modeling approach to estimate DO concentrations using input variable selection and data-driven models. Specifically, the input variable selection technique, the maximal information coefficient (MIC), was used to identify and screen the primary environmental factors driving variation in DO. The data-driven model, support vector regression (SVR), was then used to construct a robust model to estimate DO concentration. The approach was illustrated through a case study of the Pearl River Basin in China. We show that the MIC technique can effectively screen major local environmental factors affecting DO concentrations. MIC value tended to stabilize when the sample size >3000 and EC had the highest score with an MIC >0.3 at both of the stations. The variable-reduced datasets improved the performance of the SVR model by a reduction of 28.65% in RMSE, and increase of 22.16%, 56.27% in R2, NSE, respectively, relative to complete candidate sets. The MIC-SVR model constructed at the tidal river network performed better than nontidal river network by a reduction of approximately 63.01% in RMSE, an increase of 62.36% in NSE, and R2 >0.9. Overall, the proposed technique was able to handle nonlinearity among environmental factors and accurately estimate DO concentrations in tidal river network regions.

Li Wenjing, Fang Huaiyang, Qin Guangxiong, Tan Xiuqin, Huang Zhiwei, Zeng Fantang, Du Hongwei, Li Shuping

2020-May-04

DO, Maximal information coefficient, Sample size, Support vector regression, Temporal resolution

Surgery Surgery

The Evolution of Minimally Invasive Lumbar Spine Surgery.

In World neurosurgery ; h5-index 47.0

Spine surgery has evolved over centuries from first being practiced with Hippocratic boards and ladders to now being able to treat spinal pathologies with minimal tissue invasion. With the advent of new imaging and surgical technologies, spine surgeries can now be performed minimally invasively with smaller incisions, less blood loss, quicker return to daily activities, and increased visualization. Modern minimally invasive procedures include percutaneous pedicle screw fixation techniques and minimally invasive lateral approach for lumbar interbody fusion (i.e., minimally invasive transforaminal lumbar interbody fusion (MIS TLIF), extreme lateral interbody fusion [XLIF], oblique lateral interbody fusion [OLIF]), and midline lumbar fusion with cortical bone trajectory screws (MIDLIF with CBT). Just as evolutions in surgical techniques have helped revolutionize the field of spine surgery, imaging technologies have also contributed significantly. The advent of computer image guidance has allowed spine surgeons to advance their ability to refine surgical techniques, increase the accuracy of spinal hardware placement, and reduce radiation exposure to the OR staff. As the field of spine surgery looks to the future, many novel technologies are on the horizon, including robotic spine surgery, artificial intelligence (AI), and machine learning to help improve preoperative planning, surgical execution, and optimize patient selection to ensure improved postoperative outcomes and patient satisfaction. As more spine surgeons begin incorporating these novel minimally invasive techniques into practice, the field of minimally invasive spine surgery will continue to innovate and evolve over the coming years.

Momin Arbaz A, Steinmetz Michael P

2020-May-17

MIS TLIF, Minimally invasive spine surgery, OLIF, XLIF, image guidance

Dermatology Dermatology

Deep Learning for Dermatologists: Part I Fundamental Concepts.

In Journal of the American Academy of Dermatology ; h5-index 79.0

Artificial intelligence (AI) is generating substantial interest in the field of medicine. One form of artificial intelligence, deep learning, has led to rapid advances in automated image analysis. In 2017, an algorithm demonstrated the ability to diagnose certain skin cancers from clinical photographs with the accuracy of an expert dermatologist. Subsequently, deep learning has been applied to a range of dermatology applications. Though experts will never be replaced by AI, it will certainly impact the specialty of dermatology. In this first article of a two-part series, the basic concepts of deep learning will be reviewed with the goal of laying the groundwork for effective communication between clinicians and technical colleagues. In part two of the series, the clinical applications of deep learning in dermatology will be reviewed considering limitations and opportunities.

Murphree Dennis H, Puri Pranav, Shamim Huma, Bezalel Spencer A, Drage Lisa A, Wang Michael, Pittelkow Mark R, Carter Rickey E, Davis Mark D P, Bridges Alina G, Mangold Aaron R, Yiannias James A, Tollefson Megha M, Lehman Julia S, Meves Alexander, Otley Clark C, Sokumbi Olayemi, Hall Matthew R, Comfere Nneka

2020-May-17

artificial intelligence, deep learning, dermatology, machine learning

General General

Real-time data analysis using a machine learning model significantly improves prediction of successful vaginal deliveries.

In American journal of obstetrics and gynecology

BACKGROUND : The process of childbirth is one of the most crucial events in the future health and development of offspring. The vulnerability of parturients and fetuses during the delivery process led to the development of intrapartum monitoring methods and to the emergence of alternative methods of delivery. However, current monitoring methods fail to accurately discriminate between cases in which intervention is unnecessary, partly contributing to the high rates of cesarean deliveries world-wide. Machine-learning methods are applied in various medical fields to create personalized prediction models. These methods are used to analyze abundant, complex data with intricate associations to aid in decision making. Initial attempts to predict vaginal delivery vs. cesarean deliveries using machine learning tools did not utilize the vast amount of data recorded during labor. The data recorded during labor represents the dynamic process of labor and therefore may be invaluable for dynamic prediction of vaginal delivery.

OBJECTIVE : We aimed to create a personalized machine-learning based prediction model to predict successful vaginal deliveries using real time data acquired during the first stage of labor.

STUDY DESIGN : Electronic medical records of labors occurring during a 12-year period in a tertiary referral center were explored and labeled. Four different models were created using input from multiple maternal and fetal parameters. Initial risk assessments for vaginal delivery were calculated using data available at the time of admission to the delivery unit, followed by models incorporating cervical examination data and fetal heart rate data, and finally, a model that integrates additional data available during the first stage of labor was created.

RESULTS : A total of 94,480 cases in which a trial of labor was attempted were identified. Based on approximately 180 million data points from the first stage of labor, machine learning models were developed to predict successful vaginal deliveries. A model using data available at the time of admission to the delivery unit yielded an area under the curve of 0.817 (95% CI 0.811-0.823). Models that used real time data increased prediction accuracy. A model that includes real time cervical examination data had an initial AUC of 0.819 (95% CI 0.813-0.825) at first examination, which increased to an AUC of 0.917 (95% CI 0.913-0.921) by the end of the first stage. Adding the real-time fetal heart monitor data provided an AUC of 0.824 (95% CI 0.818-0.830) at first examination that increased to an AUC of 0.928 (95% CI 0.924-0.932) by the end of the first stage. Finally, adding additional real time data increased the area under the curve initially to an AUC of 0.833 (95% CI 0.827-0.838) at first cervical examination and up to an AUC of 0.932 (95% CI 0.928-0.935) by the end of the first stage.

CONCLUSION : Real-time data acquired throughout the process of labor significantly increases the prediction accuracy for vaginal delivery using machine-learning models. These models enable translation and quantification of the data gathered in the delivery unit into a clinical tool that yields a reliable personalized risk score and helps avoid unnecessary interventions.

Guedalia Joshua, Lipschuetz Michal, Novoselsky Persky Michal, Cohen Sarah M, Rottenstreich Amihai, Levin Gabriel, Yagel Simcha, Unger Ron, Sompolinsky Yishai

2020-May-17

cesarean delivery, machine learning, obstetrics, personalized medicine, prediction, trial of labor, vaginal delivery

General General

Predicting mRNA Abundance Directly from Genomic Sequence Using Deep Convolutional Neural Networks.

In Cell reports ; h5-index 119.0

Algorithms that accurately predict gene structure from primary sequence alone were transformative for annotating the human genome. Can we also predict the expression levels of genes based solely on genome sequence? Here, we sought to apply deep convolutional neural networks toward that goal. Surprisingly, a model that includes only promoter sequences and features associated with mRNA stability explains 59% and 71% of variation in steady-state mRNA levels in human and mouse, respectively. This model, termed Xpresso, more than doubles the accuracy of alternative sequence-based models and isolates rules as predictive as models relying on chromatic immunoprecipitation sequencing (ChIP-seq) data. Xpresso recapitulates genome-wide patterns of transcriptional activity, and its residuals can be used to quantify the influence of enhancers, heterochromatic domains, and microRNAs. Model interpretation reveals that promoter-proximal CpG dinucleotides strongly predict transcriptional activity. Looking forward, we propose cell-type-specific gene-expression predictions based solely on primary sequences as a grand challenge for the field.

Agarwal Vikram, Shendure Jay

2020-May-19

deep learning, gene regulation, predicting gene expression

General General

Shapley Homology: Topological Analysis of Sample Influence for Neural Networks.

In Neural computation

Data samples collected for training machine learning models are typically assumed to be independent and identically distributed (i.i.d.). Recent research has demonstrated that this assumption can be problematic as it simplifies the manifold of structured data. This has motivated different research areas such as data poisoning, model improvement, and explanation of machine learning models. In this work, we study the influence of a sample on determining the intrinsic topological features of its underlying manifold. We propose the Shapley homology framework, which provides a quantitative metric for the influence of a sample of the homology of a simplicial complex. Our proposed framework consists of two main parts: homology analysis, where we compute the Betti number of the target topological space, and Shapley value calculation, where we decompose the topological features of a complex built from data points to individual points. By interpreting the influence as a probability measure, we further define an entropy that reflects the complexity of the data manifold. Furthermore, we provide a preliminary discussion of the connection of the Shapley homology to the Vapnik-Chervonenkis dimension. Empirical studies show that when the zero-dimensional Shapley homology is used on neighboring graphs, samples with higher influence scores have a greater impact on the accuracy of neural networks that determine graph connectivity and on several regular grammars whose higher entropy values imply greater difficulty in being learned.

Zhang Kaixuan, Wang Qinglong, Liu Xue, Giles C Lee

2020-May-20

General General

The Dreem Headband compared to Polysomnography for EEG Signal Acquisition and Sleep Staging.

In Sleep

OBJECTIVES : The development of ambulatory technologies capable of monitoring brain activity during sleep longitudinally is critical for advancing sleep science. The aim of this study was to assess the signal acquisition and the performance of the automatic sleep staging algorithms of a reduced-montage dry-EEG device (Dreem headband, DH) compared to the gold-standard polysomnography (PSG) scored by 5 sleep experts.

METHODS : Twenty-five subjects who completed an overnight sleep study at a sleep center while wearing both a PSG and the DH simultaneously have been included in the analysis. We assessed 1) similarity of measured EEG brain waves between the DH and the PSG 2) the heart rate, breathing frequency, and respiration rate variability (RRV) agreement between the DH and the PSG, and 3) the performance of the DH's automatic sleep staging according to AASM guidelines vs. PSG sleep experts manual scoring.

RESULTS : The mean percentage error between the EEG signals acquired by the DH and those from the PSG for the monitoring of alpha was 15±3.5%, 16±4.3% for beta, 16±6.1% for delta, and 10±1.4% for theta frequencies during sleep. The mean absolute error for heart rate, breathing frequency and RRV was 1.2±0.5 bpm, 0.3±0.2 cpm and 3.2±0.6%, respectively. Automatic sleep staging reached an overall accuracy of 83.5±6.4% (F1 score: 83.8±6.3) for the DH to be compared with an average of 86.4±8.0% (F1 score: 86.3±7.4) for the 5 sleep experts.

CONCLUSION : These results demonstrate the capacity of the DH to both monitor sleep-related physiological signals and process them accurately into sleep stages. This device paves the way for, large-scale, longitudinal sleep studies.

Arnal Pierrick J, Thorey Valentin, Debellemaniere Eden, Ballard Michael E, Bou Hernandez Albert, Guillot Antoine, Jourde Hugo, Harris Mason, Guillard Mathias, Van Beers Pascal, Chennaoui Mounir, Sauvet Fabien

2020-May-20

Device, EEG, Machine learning, Sleep, Sleep stages

General General

Laboratory-based and free-living algorithms for energy expenditure estimation in preschool children: A free-living evaluation.

In PloS one ; h5-index 176.0

PURPOSE : To evaluate the accuracy of LAB EE prediction models in preschool children completing a free-living active play session. Performance was benchmarked against EE prediction models trained on free living (FL) data.

METHODS : 25 children (mean age = 4.1±1.0 y) completed a 20-minute active play session while wearing a portable indirect calorimeter and ActiGraph GT3X+ accelerometers on their right hip and non-dominant wrist. EE was predicted using LAB models which included Random Forest (RF) and Support Vector Machine (SVM) models for the wrist, and RF and Artificial Neural Network (ANN) models for the hip. Two variations of the LAB models were evaluated; 1) an "off the shelf" model without additional training; 2) models retrained on free-living data, replicating the methodology used in the original calibration study (retrained LAB). Prediction errors were evaluated in a hold-out sample of 10 children.

RESULTS : Root mean square error (RMSE) for the FL and retrained LAB models ranged from 0.63-0.67 kcals/min. In the hold out sample, RMSE's for the hip LAB (0.62-0.71), retrained LAB (0.58-0.62) and FL models (0.61-0.65) were similar. For the wrist placement, FL SVM had a significantly higher RMSE (0.73 ± 0.29 kcals/min) than the retrained LAB SVM (0.63 ± 0.30 kcals/min) and LAB SVM (0.64 ± 0.18 kcals/min). The LAB (0.64 ± 0.28), retrained LAB (0.64 ± 0.25), and FL (0.62 ± 0.26) RF exhibited comparable accuracy.

CONCLUSION : Machine learning EE prediction models trained on LAB and FL data had similar accuracy under free-living conditions.

Ahmadi Matthew N, Chowdhury Alok, Pavey Toby, Trost Stewart G

2020

General General

Artificial intelligence may offer insight into factors determining individual TSH level.

In PloS one ; h5-index 176.0

The factors that determine Serum Thyrotropin (TSH) levels have been examined through different methods, using different covariates. However, the use of machine learning methods has so far not been studied in population databases like NHANES (National Health and Nutritional Examination Survey) to predict TSH. In this study, we performed a comparative analysis of different machine learning methods like Linear regression, Random forest, Support vector machine, multilayer perceptron and stacking regression to predict TSH and classify individuals with normal, low and high TSH levels. We considered Free T4, Anti-TPO antibodies, T3, Body Mass Index (BMI), Age and Ethnicity as the predictor variables. A total of 9818 subjects were included in this comparative analysis. We used coefficient of determination (r2) value to compare the results for predicting the TSH and show that the Random Forest, Gradient Boosting and Stacking Regression perform equally well in predicting TSH and achieve the highest r2 value = 0.13, with mean absolute error of 0.78. Moreover, we found that Anti-TPO is the most important feature in predicting TSH followed by Age, BMI, T3 and Free-T4 for the regression analysis. While classifying TSH into normal, high or low levels, our comparative analysis also shows that Random forest performs the best in the classification study, performed with individuals with normal, high and low levels of TSH. We found the following Areas Under Curve (AUC); for low TSH, AUC = 0.61, normal TSH, AUC = 0.61 and elevated TSH AUC = 0.69. Additionally, we found that Anti-TPO was the most important feature in classifying TSH. In this study, we suggest that artificial intelligence and machine learning methods might offer an insight into the complex hypothalamic-pituitary -thyroid axis and may be an invaluable tool that guides us in making appropriate therapeutic decisions (thyroid hormone dosing) for the individual patient.

Santhanam Prasanna, Nath Tanmay, Mohammad Faiz Khan, Ahima Rexford S

2020

General General

Dynamical footprints enable detection of disease emergence.

In PLoS biology

Developing methods for anticipating the emergence or reemergence of infectious diseases is both important and timely; however, traditional model-based approaches are stymied by uncertainty surrounding the underlying drivers. Here, we demonstrate an operational, mechanism-agnostic detection algorithm for disease (re-)emergence based on early warning signals (EWSs) derived from the theory of critical slowing down. Specifically, we used computer simulations to train a supervised learning algorithm to detect the dynamical footprints of (re-)emergence present in epidemiological data. Our algorithm was then challenged to forecast the slowly manifesting, spatially replicated reemergence of mumps in England in the mid-2000s and pertussis post-1980 in the United States. Our method successfully anticipated mumps reemergence 4 years in advance, during which time mitigation efforts could have been implemented. From 1980 onwards, our model identified resurgent states with increasing accuracy, leading to reliable classification starting in 1992. Additionally, we successfully applied the detection algorithm to 2 vector-transmitted case studies, namely, outbreaks of dengue serotypes in Puerto Rico and a rapidly unfolding outbreak of plague in 2017 in Madagascar. Taken together, these findings illustrate the power of theoretically informed machine learning techniques to develop early warning systems for the (re-)emergence of infectious diseases.

Brett Tobias S, Rohani Pejman

2020-May

General General

Using Machine Learning to Predict Bacteremia in Febrile Children Presented to the Emergency Department.

In Diagnostics (Basel, Switzerland)

Blood culture is frequently used to detect bacteremia in febrile children. However, a high rate of negative or false-positive blood culture results is common at the pediatric emergency department (PED). The aim of this study was to use machine learning to build a model that could predict bacteremia in febrile children. We conducted a retrospective case-control study of febrile children who presented to the PED from 2008 to 2015. We adopted machine learning methods and cost-sensitive learning to establish a predictive model of bacteremia. We enrolled 16,967 febrile children with blood culture tests during the eight-year study period. Only 146 febrile children had true bacteremia, and more than 99% of febrile children had a contaminant or negative blood culture result. The maximum area under the curve of logistic regression and support vector machines to predict bacteremia were 0.768 and 0.832, respectively. Using the predictive model, we can categorize febrile children by risk value into five classes. Class 5 had the highest probability of having bacteremia, while class 1 had no risk. Obtaining blood cultures in febrile children at the PED rarely identifies a causative pathogen. Prediction models can help physicians determine whether patients have bacteremia and may reduce unnecessary expenses.

Tsai Chih-Min, Lin Chun-Hung Richard, Zhang Huan, Chiu I-Min, Cheng Chi-Yung, Yu Hong-Ren, Huang Ying-Hsien

2020-May-15

bacteremia, children, emergency department, machine learning, predict

Surgery Surgery

Multi-agent model for risk prediction in surgery

ArXiv Preprint

Risk management resulting from the actions and states of the different elements making up a operating room is a major concern during a surgical procedure. Agent-based simulation shows an interest through its interaction concepts, interactivity and autonomy of different simulator entities. We want in our study to implement a generator of alerts to listen the evolution of different settings applied to the simulator of agents (human fatigue, material efficiency, infection rate ...). This article presents our model, its implementation and the first results obtained. It should be noted that this study also made it possible to identify several scientific obstacles, such as the integration of different levels of abstraction, the coupling of species, the coexistence of several scales in the same environment and the deduction of unpredictable alerts. Case-based reasoning (CBR) is a beginning of response relative to the last lock mentioned and will be discussed in this paper.

Bruno Perez, Julien Henriet, Christophe Lang, Laurent Philippe

2020-05-21

Radiology Radiology

Machine-learning based exploration of determinants of gray matter volume in the KORA-MRI study.

In Scientific reports ; h5-index 158.0

To identify the most important factors that impact brain volume, while accounting for potential collinearity, we used a data-driven machine-learning approach. Gray Matter Volume (GMV) was derived from magnetic resonance imaging (3T, FLAIR) and adjusted for intracranial volume (ICV). 93 potential determinants of GMV from the categories sociodemographics, anthropometric measurements, cardio-metabolic variables, lifestyle factors, medication, sleep, and nutrition were obtained from 293 participants from a population-based cohort from Southern Germany. Elastic net regression was used to identify the most important determinants of ICV-adjusted GMV. The four variables age (selected in each of the 1000 splits), glomerular filtration rate (794 splits), diabetes (323 splits) and diabetes duration (122 splits) were identified to be most relevant predictors of GMV adjusted for intracranial volume. The elastic net model showed better performance compared to a constant linear regression (mean squared error = 1.10 vs. 1.59, p < 0.001). These findings are relevant for preventive and therapeutic considerations and for neuroimaging studies, as they suggest to take information on metabolic status and renal function into account as potential confounders.

Galiè Franziska, Rospleszcz Susanne, Keeser Daniel, Beller Ebba, Illigens Ben, Lorbeer Roberto, Grosu Sergio, Selder Sonja, Auweter Sigrid, Schlett Christopher L, Rathmann Wolfgang, Schwettmann Lars, Ladwig Karl-Heinz, Linseisen Jakob, Peters Annette, Bamberg Fabian, Ertl-Wagner Birgit, Stoecklein Sophia

2020-May-20

General General

Automated detection of the head-twitch response using wavelet scalograms and a deep convolutional neural network.

In Scientific reports ; h5-index 158.0

Hallucinogens induce the head-twitch response (HTR), a rapid reciprocal head movement, in mice. Although head twitches are usually identified by direct observation, they can also be assessed using a head-mounted magnet and a magnetometer. Procedures have been developed to automate the analysis of magnetometer recordings by detecting events that match the frequency, duration, and amplitude of the HTR. However, there is considerable variability in the features of head twitches, and behaviors such as jumping have similar characteristics, reducing the reliability of these methods. We have developed an automated method that can detect head twitches unambiguously, without relying on features in the amplitude-time domain. To detect the behavior, events are transformed into a visual representation in the time-frequency domain (a scalogram), deep features are extracted using the pretrained convolutional neural network (CNN) ResNet-50, and then the images are classified using a Support Vector Machine (SVM) algorithm. These procedures were used to analyze recordings from 237 mice containing 11,312 HTR. After transformation to scalograms, the multistage CNN-SVM approach detected 11,244 (99.4%) of the HTR. The procedures were insensitive to other behaviors, including jumping and seizures. Deep learning based on scalograms can be used to automate HTR detection with robust sensitivity and reliability.

Halberstadt Adam L

2020-May-20

General General

Real-time detection of colon polyps during colonoscopy using deep learning: systematic validation with four independent datasets.

In Scientific reports ; h5-index 158.0

We developed and validated a deep-learning algorithm for polyp detection. We used a YOLOv2 to develop the algorithm for automatic polyp detection on 8,075 images (503 polyps). We validated the algorithm using three datasets: A: 1,338 images with 1,349 polyps; B: an open, public CVC-clinic database with 612 polyp images; and C: 7 colonoscopy videos with 26 polyps. To reduce the number of false positives in the video analysis, median filtering was applied. We tested the algorithm performance using 15 unaltered colonoscopy videos (dataset D). For datasets A and B, the per-image polyp detection sensitivity was 96.7% and 90.2%, respectively. For video study (dataset C), the per-image polyp detection sensitivity was 87.7%. False positive rates were 12.5% without a median filter and 6.3% with a median filter with a window size of 13. For dataset D, the sensitivity and false positive rate were 89.3% and 8.3%, respectively. The algorithm detected all 38 polyps that the endoscopists detected and 7 additional polyps. The operation speed was 67.16 frames per second. The automatic polyp detection algorithm exhibited good performance, as evidenced by the high detection sensitivity and rapid processing. Our algorithm may help endoscopists improve polyp detection.

Lee Ji Young, Jeong Jinhoon, Song Eun Mi, Ha Chunae, Lee Hyo Jeong, Koo Ja Eun, Yang Dong-Hoon, Kim Namkug, Byeon Jeong-Sik

2020-May-20

General General

Applying Machine Learning to Kinematic and Eye Movement Features of a Movement Imitation Task to Predict Autism Diagnosis.

In Scientific reports ; h5-index 158.0

Autism is a developmental condition currently identified by experts using observation, interview, and questionnaire techniques and primarily assessing social and communication deficits. Motor function and movement imitation are also altered in autism and can be measured more objectively. In this study, motion and eye tracking data from a movement imitation task were combined with supervised machine learning methods to classify 22 autistic and 22 non-autistic adults. The focus was on a reliable machine learning application. We have used nested validation to develop models and further tested the models with an independent data sample. Feature selection was aimed at selection stability to assure result interpretability. Our models predicted diagnosis with 73% accuracy from kinematic features, 70% accuracy from eye movement features and 78% accuracy from combined features. We further explored features which were most important for predictions to better understand movement imitation differences in autism. Consistent with the behavioural results, most discriminative features were from the experimental condition in which non-autistic individuals tended to successfully imitate unusual movement kinematics while autistic individuals tended to fail. Machine learning results show promise that future work could aid in the diagnosis process by providing quantitative tests to supplement current qualitative ones.

Vabalas Andrius, Gowen Emma, Poliakoff Ellen, Casson Alexander J

2020-May-20

General General

Environmental DNA can act as a biodiversity barometer of anthropogenic pressures in coastal ecosystems.

In Scientific reports ; h5-index 158.0

Loss of biodiversity from lower to upper trophic levels reduces overall productivity and stability of coastal ecosystems in our oceans, but rarely are these changes documented across both time and space. The characterisation of environmental DNA (eDNA) from sediment and seawater using metabarcoding offers a powerful molecular lens to observe marine biota and provides a series of 'snapshots' across a broad spectrum of eukaryotic organisms. Using these next-generation tools and downstream analytical innovations including machine learning sequence assignment algorithms and co-occurrence network analyses, we examined how anthropogenic pressures may have impacted marine biodiversity on subtropical coral reefs in Okinawa, Japan. Based on 18 S ribosomal RNA, but not ITS2 sequence data due to inconsistent amplification for this marker, as well as proxies for anthropogenic disturbance, we show that eukaryotic richness at the family level significantly increases with medium and high levels of disturbance. This change in richness coincides with compositional changes, a decrease in connectedness among taxa, an increase in fragmentation of taxon co-occurrence networks, and a shift in indicator taxa. Taken together, these findings demonstrate the ability of eDNA to act as a barometer of disturbance and provide an exemplar of how biotic networks and coral reefs may be impacted by anthropogenic activities.

DiBattista Joseph D, Reimer James D, Stat Michael, Masucci Giovanni D, Biondi Piera, De Brauwer Maarten, Wilkinson Shaun P, Chariton Anthony A, Bunce Michael

2020-May-20

Radiology Radiology

Artificial Intelligence Algorithm Detecting Lung Infection in Supine Chest Radiographs of Critically Ill Patients With a Diagnostic Accuracy Similar to Board-Certified Radiologists.

In Critical care medicine ; h5-index 87.0

OBJECTIVES : Interpretation of lung opacities in ICU supine chest radiographs remains challenging. We evaluated a prototype artificial intelligence algorithm to classify basal lung opacities according to underlying pathologies.

DESIGN : Retrospective study. The deep neural network was trained on two publicly available datasets including 297,541 images of 86,876 patients.

PATIENTS : One hundred sixty-six patients received both supine chest radiograph and CT scans (reference standard) within 90 minutes without any intervention in between.

MEASUREMENTS AND MAIN RESULTS : Algorithm accuracy was referenced to board-certified radiologists who evaluated supine chest radiographs according to side-separate reading scores for pneumonia and effusion (0 = absent, 1 = possible, and 2 = highly suspected). Radiologists were blinded to the supine chest radiograph findings during CT interpretation. Performances of radiologists and the artificial intelligence algorithm were quantified by receiver-operating characteristic curve analysis. Diagnostic metrics (sensitivity, specificity, positive predictive value, negative predictive value, and accuracy) were calculated based on different receiver-operating characteristic operating points. Regarding pneumonia detection, radiologists achieved a maximum diagnostic accuracy of up to 0.87 (95% CI, 0.78-0.93) when considering only the supine chest radiograph reading score 2 as positive for pneumonia. Radiologist's maximum sensitivity up to 0.87 (95% CI, 0.76-0.94) was achieved by additionally rating the supine chest radiograph reading score 1 as positive for pneumonia and taking previous examinations into account. Radiologic assessment essentially achieved nonsignificantly higher results compared with the artificial intelligence algorithm: artificial intelligence-area under the receiver-operating characteristic curve of 0.737 (0.659-0.815) versus radiologist's area under the receiver-operating characteristic curve of 0.779 (0.723-0.836), diagnostic metrics of receiver-operating characteristic operating points did not significantly differ. Regarding the detection of pleural effusions, there was no significant performance difference between radiologist's and artificial intelligence algorithm: artificial intelligence-area under the receiver-operating characteristic curve of 0.740 (0.662-0.817) versus radiologist's area under the receiver-operating characteristic curve of 0.698 (0.646-0.749) with similar diagnostic metrics for receiver-operating characteristic operating points.

CONCLUSIONS : Considering the minor level of performance differences between the algorithm and radiologists, we regard artificial intelligence as a promising clinical decision support tool for supine chest radiograph examinations in the clinical routine with high potential to reduce the number of missed findings in an artificial intelligence-assisted reading setting.

Rueckel Johannes, Kunz Wolfgang G, Hoppe Boj F, Patzig Maximilian, Notohamiprodjo Mike, Meinel Felix G, Cyran Clemens C, Ingrisch Michael, Ricke Jens, Sabel Bastian O

2020-May-20

oncology Oncology

How Does the Skeletal Oncology Research Group Algorithm's Prediction of 5-year Survival in Patients with Chondrosarcoma Perform on International Validation?

In Clinical orthopaedics and related research ; h5-index 71.0

BACKGROUND : The Skeletal Oncology Research Group (SORG) machine learning algorithm for predicting survival in patients with chondrosarcoma was developed using data from the Surveillance, Epidemiology, and End Results (SEER) registry. This algorithm was externally validated on a dataset of patients from the United States in an earlier study, where it demonstrated generally good performance but overestimated 5-year survival. In addition, this algorithm has not yet been validated in patients outside the United States; doing so would be important because external validation is necessary as algorithm performance may be misleading when applied in different populations.

QUESTIONS/PURPOSES : Does the SORG algorithm retain validity in patients who underwent surgery for primary chondrosarcoma outside the United States, specifically in Italy?

METHODS : A total of 737 patients were treated for chondrosarcoma between January 2000 and October 2014 at the Italian tertiary care center which was used for international validation. We excluded patients whose first surgical procedure was performed elsewhere (n = 25), patients who underwent nonsurgical treatment (n = 27), patients with a chondrosarcoma of the soft tissue or skull (n = 60), and patients with peripheral, periosteal, or mesenchymal chondrosarcoma (n = 161). Thus, 464 patients were ultimately included in this external validation study, as the earlier performed SEER study was used as the training set. Therefore, this study-unlike most of this type-does not have a training and validation set. Although the earlier study overestimated 5-year survival, we did not modify the algorithm in this report, as this is the first international validation and the prior performance in the single-institution validation study from the United States may have been driven by a small sample or non-generalizable patterns related to its single-center setting. Variables needed for the SORG algorithm were manually collected from electronic medical records. These included sex, age, histologic subtype, tumor grade, tumor size, tumor extension, and tumor location. By inputting these variables into the algorithm, we calculated the predicted probabilities of survival for each patient. The performance of the SORG algorithm was assessed in this study through discrimination (the ability of a model to distinguish between a binary outcome), calibration (the agreement of observed and predicted outcomes), overall performance (the accuracy of predictions), and decision curve analysis (establishment on the ability of a model to make a decision better than without using the model). For discrimination, the c-statistic (commonly known as the area under the receiver operating characteristic curve for binary classification) was calculated; this ranged from 0.5 (no better than chance) to 1.0 (excellent discrimination). The agreement between predicted and observed outcomes was visualized with a calibration plot, and the calibration slope and intercept were calculated. Perfect calibration results in a slope of 1 and an intercept of 0. For overall performance, the Brier score and the null-model Brier score were calculated. The Brier score ranges from 0 (perfect prediction) to 1 (poorest prediction). Appropriate interpretation of the Brier score requires comparison with the null-model Brier score. The null-model Brier score is the score for an algorithm that predicts a probability equal to the population prevalence of the outcome for every patient. A decision curve analysis was performed to compare the potential net benefit of the algorithm versus other means of decision support, such as treating all or none of the patients. There were several differences between this study and the earlier SEER study, and such differences are important because they help us to determine the performance of the algorithm in a group different from the initial study population. In this study from Italy, 5-year survival was different from the earlier SEER study (71% [319 of 450 patients] versus 76% [1131 of 1487 patients]; p = 0.03). There were more patients with dedifferentiated chondrosarcoma than in the earlier SEER study (25% [118 of 464 patients] versus 8.5% [131 of 1544 patients]; p < 0.001). In addition, in this study patients were older, tumor size was larger, and there were higher proportions of high-grade tumors than the earlier SEER study (age: 56 years [interquartile range {IQR} 42 to 67] versus 52 years [IQR 40 to 64]; p = 0.007; tumor size: 80 mm [IQR 50 to 120] versus 70 mm [IQR 42 to 105]; p < 0.001; tumor grade: 22% [104 of 464 had Grade 1], 42% [196 of 464 had Grade 2], and 35% [164 of 464 had Grade 3] versus 41% [592 of 1456 had Grade 1], 40% [588 of 1456 had Grade 2], and 19% [276 of 1456 had Grade 3]; p ≤ 0.001).

RESULTS : Validation of the SORG algorithm in a primarily Italian population achieved a c-statistic of 0.86 (95% confidence interval 0.82 to 0.89), suggesting good-to-excellent discrimination. The calibration plot showed good agreement between the predicted probability and observed survival in the probability thresholds of 0.8 to 1.0. With predicted survival probabilities lower than 0.8, however, the SORG algorithm underestimated the observed proportion of patients with 5-year survival, reflected in the overall calibration intercept of 0.82 (95% CI 0.67 to 0.98) and calibration slope of 0.68 (95% CI 0.42 to 0.95). The Brier score for 5-year survival was 0.15, compared with a null-model Brier of 0.21. The algorithm showed a favorable decision curve analysis in the validation cohort.

CONCLUSIONS : The SORG algorithm to predict 5-year survival for patients with chondrosarcoma held good discriminative ability and overall performance on international external validation; however, it underestimated 5-year survival for patients with predicted probabilities from 0 to 0.8 because the calibration plot was not perfectly aligned for the observed outcomes, which resulted in a maximum underestimation of 20%. The differences may reflect the baseline differences noted between the two study populations. The overall performance of the algorithm supports the utility of the algorithm and validation presented here. The freely available digital application for the algorithm is available here: https://sorg-apps.shinyapps.io/extremitymetssurvival/.

LEVEL OF EVIDENCE : Level III, prognostic study.

Bongers Michiel E R, Karhade Aditya V, Setola Elisabetta, Gambarotti Marco, Groot Olivier Q, Erdoğan Kıvılcım E, Picci Piero, Donati Davide M, Schwab Joseph H, Palmerini Emanuela

2020-May-18

General General

Rise of the Robots: Is Artificial Intelligence a Friend or Foe to Nursing Practice?

In Critical care nursing quarterly

Much like other aspects of health care, nursing has become increasingly saturated with technology over the past several decades. Existing technology has advanced nursing in many ways and contributed to patient safety but at the cost of decreasing nurse-patient interaction. As health care technology progresses to the inclusion of artificial intelligence (AI), the future impact on nursing and direct patient care remains largely unknown, unexplored, and difficult to predict. This article aims to explore the relevance of nursing in a technologically advanced postmodern health care system. The relevance of nursing in the future is solidified by the unique nature of nursing that includes the embodiment of human caring and emotional intelligence. Nurses' abilities to intervene before patient deterioration, care for patients holistically, and manage various aspects of care will be heightened by the adoption of AI. Nurses should embrace AI technology, as we predict that it will decrease nurse workload and cognitive overload and allow for increased patient-nurse interaction. Current and future nurses should take the lead on determining how it augments nursing practice.

Watson Daniel, Womack Joshua, Papadakos Suzanne

General General

Deep Learning-Based HCS Image Analysis for the Enterprise.

In SLAS discovery : advancing life sciences R & D

Drug discovery programs are moving increasingly toward phenotypic imaging assays to model disease-relevant pathways and phenotypes in vitro. These assays offer richer information than target-optimized assays by investigating multiple cellular pathways simultaneously and producing multiplexed readouts. However, extracting the desired information from complex image data poses significant challenges, preventing broad adoption of more sophisticated phenotypic assays. Deep learning-based image analysis can address these challenges by reducing the effort required to analyze large volumes of complex image data at a quality and speed adequate for routine phenotypic screening in pharmaceutical research. However, while general purpose deep learning frameworks are readily available, they are not readily applicable to images from automated microscopy. During the past 3 years, we have optimized deep learning networks for this type of data and validated the approach across diverse assays with several industry partners. From this work, we have extracted five essential design principles that we believe should guide deep learning-based analysis of high-content images and multiparameter data: (1) insightful data representation, (2) automation of training, (3) multilevel quality control, (4) knowledge embedding and transfer to new assays, and (5) enterprise integration. We report a new deep learning-based software that embodies these principles, Genedata Imagence, which allows screening scientists to reliably detect stable endpoints for primary drug response, assess toxicity and safety-relevant effects, and discover new phenotypes and compound classes. Furthermore, we show how the software retains expert knowledge from its training on a particular assay and successfully reapplies it to different, novel assays in an automated fashion.

Steigele Stephan, Siegismund Daniel, Fassler Matthias, Kustec Marusa, Kappler Bernd, Hasaka Tom, Yee Ada, Brodte Annette, Heyse Stephan

2020-May-20

cell-based assays, high-content screening, image analysis, imaging technologies, phenotypic drug discovery

General General

Non - invasive modelling methodology for the diagnosis of coronary artery disease using fuzzy cognitive maps.

In Computer methods in biomechanics and biomedical engineering

Cardiovascular diseases (CVD) and strokes produce immense health and economic burdens globally. Coronary Artery Disease (CAD) is the most common type of cardiovascular disease. Coronary Angiography, which is an invasive approach for detection and treatment, is also the standard procedure for diagnosing CAD. In this work, we illustrate a Medical Decision Support System for the prediction of Coronary Artery Disease (CAD) using Fuzzy Cognitive Maps (FCM). FCMs are a promising modeling methodology, based on human knowledge, capable of dealing with ambiguity and uncertainty and learning how to adapt to the unknown or changing environment. The newly proposed MDSS is developed using the basic notions of Fuzzy Cognitive Maps and is intended to diagnose CAD utilizing specific inputs related to the patient's clinical conditions. We show that the proposed model, when tested on a dataset collected from the Laboratory of Nuclear Medicine of the University Hospital of Patras achieves accuracy of 78.2% outmatching several state-of-the-art classification algorithms.

Apostolopoulos Ioannis D, Groumpos Peter P

2020-May-20

Coronary Artery Disease, decision support system, fuzzy cognitive maps, machine learning

Pathology Pathology

Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources.

In Metabolites

As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study.

Eicher Tara, Kinnebrew Garrett, Patt Andrew, Spencer Kyle, Ying Kevin, Ma Qin, Machiraju Raghu, Mathé And Ewy A

2020-May-15

biological pathways, clustering, co-regulation, deep learning, dimensionality reduction, machine learning, multi-omics integration, network analysis, pathway enrichment, visualization

General General

Symptom extraction from the narratives of personal experiences with COVID-19 on Reddit

ArXiv Preprint

Social media discussion of COVID-19 provides a rich source of information into how the virus affects people's lives that is qualitatively different from traditional public health datasets. In particular, when individuals self-report their experiences over the course of the virus on social media, it can allow for identification of the emotions each stage of symptoms engenders in the patient. Posts to the Reddit forum r/COVID19Positive contain first-hand accounts from COVID-19 positive patients, giving insight into personal struggles with the virus. These posts often feature a temporal structure indicating the number of days after developing symptoms the text refers to. Using topic modelling and sentiment analysis, we quantify the change in discussion of COVID-19 throughout individuals' experiences for the first 14 days since symptom onset. Discourse on early symptoms such as fever, cough, and sore throat was concentrated towards the beginning of the posts, while language indicating breathing issues peaked around ten days. Some conversation around critical cases was also identified and appeared at a roughly constant rate. We identified two clear clusters of positive and negative emotions associated with the evolution of these symptoms and mapped their relationships. Our results provide a perspective on the patient experience of COVID-19 that complements other medical data streams and can potentially reveal when mental health issues might appear.

Curtis Murray, Lewis Mitchell, Jonathan Tuke, Mark Mackay

2020-05-21

oncology Oncology

Use of Deep Learning to Develop and Analyze Computational Hematoxylin and Eosin Staining of Prostate Core Biopsy Images for Tumor Diagnosis.

In JAMA network open

Importance : Histopathological diagnoses of tumors from tissue biopsy after hematoxylin and eosin (H&E) dye staining is the criterion standard for oncological care, but H&E staining requires trained operators, dyes and reagents, and precious tissue samples that cannot be reused.

Objectives : To use deep learning algorithms to develop models that perform accurate computational H&E staining of native nonstained prostate core biopsy images and to develop methods for interpretation of H&E staining deep learning models and analysis of computationally stained images by computer vision and clinical approaches.

Design, Setting, and Participants : This cross-sectional study used hundreds of thousands of native nonstained RGB (red, green, and blue channel) whole slide image (WSI) patches of prostate core tissue biopsies obtained from excess tissue material from prostate core biopsies performed in the course of routine clinical care between January 7, 2014, and January 7, 2017, at Brigham and Women's Hospital, Boston, Massachusetts. Biopsies were registered with their H&E-stained versions. Conditional generative adversarial neural networks (cGANs) that automate conversion of native nonstained RGB WSI to computational H&E-stained images were then trained. Deidentified whole slide images of prostate core biopsy and medical record data were transferred to Massachusetts Institute of Technology, Cambridge, for computational research. Results were shared with physicians for clinical evaluations. Data were analyzed from July 2018 to February 2019.

Main Outcomes and Measures : Methods for detailed computer vision image analytics, visualization of trained cGAN model outputs, and clinical evaluation of virtually stained images were developed. The main outcome was interpretable deep learning models and computational H&E-stained images that achieved high performance in these metrics.

Results : Among 38 patients who provided samples, single core biopsy images were extracted from each whole slide, resulting in 102 individual nonstained and H&E dye-stained image pairs that were compared with matched computationally stained and unstained images. Calculations showed high similarities between computationally and H&E dye-stained images, with a mean (SD) structural similarity index (SSIM) of 0.902 (0.026), Pearson correlation coefficient (PCC) of 0.962 (0.096), and peak signal to noise ratio (PSNR) of 22.821 (1.232) dB. A second cGAN performed accurate computational destaining of H&E-stained images back to their native nonstained form, with a mean (SD) SSIM of 0.900 (0.030), PCC of 0.963 (0.011), and PSNR of 25.646 (1.943) dB compared with native nonstained images. A single blind prospective study computed approximately 95% pixel-by-pixel overlap among prostate tumor annotations provided by 5 board certified pathologists on computationally stained images, compared with those on H&E dye-stained images. This study also used the first visualization and explanation of neural network kernel activation maps during H&E staining and destaining of RGB images by cGANs. High similarities between kernel activation maps of computationally and H&E-stained images (mean-squared errors <0.0005) provide additional mathematical and mechanistic validation of the staining system.

Conclusions and Relevance : These findings suggest that computational H&E staining of native unlabeled RGB images of prostate core biopsy could reproduce Gleason grade tumor signatures that were easily assessed and validated by clinicians. Methods for benchmarking, visualization, and clinical validation of deep learning models and virtually H&E-stained images communicated in this study have wide applications in clinical informatics and oncology research. Clinical researchers may use these systems for early indications of possible abnormalities in native nonstained tissue biopsies prior to histopathological workflows.

Rana Aman, Lowe Alarice, Lithgow Marie, Horback Katharine, Janovitz Tyler, Da Silva Annacarolina, Tsai Harrison, Shanmugam Vignesh, Bayat Akram, Shah Pratik

2020-May-01

General General

Placental Flattening via Volumetric Parameterization.

In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

We present a volumetric mesh-based algorithm for flattening the placenta to a canonical template to enable effective visualization of local anatomy and function. Monitoring placental function in vivo promises to support pregnancy assessment and to improve care outcomes. We aim to alleviate visualization and interpretation challenges presented by the shape of the placenta when it is attached to the curved uterine wall. To do so, we flatten the volumetric mesh that captures placental shape to resemble the well-studied ex vivo shape. We formulate our method as a map from the in vivo shape to a flattened template that minimizes the symmetric Dirichlet energy to control distortion throughout the volume. Local injectivity is enforced via constrained line search during gradient descent. We evaluate the proposed method on 28 placenta shapes extracted from MRI images in a clinical study of placental function. We achieve sub-voxel accuracy in mapping the boundary of the placenta to the template while successfully controlling distortion throughout the volume. We illustrate how the resulting mapping of the placenta enhances visualization of placental anatomy and function. Our implementation is freely available at https://github.com/mabulnaga/placenta-flattening.

Abulnaga S Mazdak, Turk Esra Abaci, Bessmeltsev Mikhail, Grant P Ellen, Solomon Justin, Golland Polina

2019-Oct

Anatomy visualization, Fetal MRI, Flattening, Injective maps, Placenta, Volumetric mesh parameterization

General General

Unsupervised Deep Learning for Bayesian Brain MRI Segmentation.

In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

Probabilistic atlas priors have been commonly used to derive adaptive and robust brain MRI segmentation algorithms. Widely-used neuroimage analysis pipelines rely heavily on these techniques, which are often computationally expensive. In contrast, there has been a recent surge of approaches that leverage deep learning to implement segmentation tools that are computationally efficient at test time. However, most of these strategies rely on learning from manually annotated images. These supervised deep learning methods are therefore sensitive to the intensity profiles in the training dataset. To develop a deep learning-based segmentation model for a new image dataset (e.g., of different contrast), one usually needs to create a new labeled training dataset, which can be prohibitively expensive, or rely on suboptimal ad hoc adaptation or augmentation approaches. In this paper, we propose an alternative strategy that combines a conventional probabilistic atlas-based segmentation with deep learning, enabling one to train a segmentation model for new MRI scans without the need for any manually segmented images. Our experiments include thousands of brain MRI scans and demonstrate that the proposed method achieves good accuracy for a brain MRI segmentation task for different MRI contrasts, requiring only approximately 15 seconds at test time on a GPU.

Dalca Adrian V, Yu Evan, Golland Polina, Fischl Bruce, Sabuncu Mert R, Iglesias Juan Eugenio

2019-Oct

Bayesian Modeling, Brain MRI, Convolutional Neural Networks, Deep Learning, Segmentation, Unsupervised learning

General General

Disease Knowledge Transfer across Neurodegenerative Diseases.

In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

We introduce Disease Knowledge Transfer (DKT), a novel technique for transferring biomarker information between related neurodegenerative diseases. DKT infers robust multimodal biomarker trajectories in rare neurodegenerative diseases even when only limited, unimodal data is available, by transferring information from larger multimodal datasets from common neurodegenerative diseases. DKT is a joint-disease generative model of biomarker progressions, which exploits biomarker relationships that are shared across diseases. Our proposed method allows, for the first time, the estimation of plausible multimodal biomarker trajectories in Posterior Cortical Atrophy (PCA), a rare neurodegenerative disease where only unimodal MRI data is available. For this we train DKT on a combined dataset containing subjects with two distinct diseases and sizes of data available: 1) a larger, multimodal typical AD (tAD) dataset from the TADPOLE Challenge, and 2) a smaller unimodal Posterior Cortical Atrophy (PCA) dataset from the Dementia Research Centre (DRC), for which only a limited number of Magnetic Resonance Imaging (MRI) scans are available. Although validation is challenging due to lack of data in PCA, we validate DKT on synthetic data and two patient datasets (TADPOLE and PCA cohorts), showing it can estimate the ground truth parameters in the simulation and predict unseen biomarkers on the two patient datasets. While we demonstrated DKT on Alzheimer's variants, we note DKT is generalisable to other forms of related neurodegenerative diseases. Source code for DKT is available online: https://github.com/mrazvan22/dkt.

Marinescu Răzvan V, Lorenzi Marco, Blumberg Stefano B, Young Alexandra L, Planell-Morell Pere, Oxtoby Neil P, Eshaghi Arman, Yong Keir X, Crutch Sebastian J, Golland Polina, Alexander Daniel C

2019-Oct

Alzheimer’s Disease, Disease Progression Modelling, Manifold Learning, Posterior Cortical Atrophy, Transfer Learning

General General

Ensembles of Hydrophobicity Scales as Potent Classifiers for Chimeric Virus-Like Particle Solubility - An Amino Acid Sequence-Based Machine Learning Approach.

In Frontiers in bioengineering and biotechnology

Virus-like particles (VLPs) are protein-based nanoscale structures that show high potential as immunotherapeutics or cargo delivery vehicles. Chimeric VLPs are decorated with foreign peptides resulting in structures that confer immune responses against the displayed epitope. However, insertion of foreign sequences often results in insoluble proteins, calling for methods capable of assessing a VLP candidate's solubility in silico. The prediction of VLP solubility requires a model that can identify critical hydrophobicity-related parameters, distinguishing between VLP-forming aggregation and aggregation leading to insoluble virus protein clusters. Therefore, we developed and implemented a soft ensemble vote classifier (sEVC) framework based on chimeric hepatitis B core antigen (HBcAg) amino acid sequences and 91 publicly available hydrophobicity scales. Based on each hydrophobicity scale, an individual decision tree was induced as classifier in the sEVC. An embedded feature selection algorithm and stratified sampling proved beneficial for model construction. With a learning experiment, model performance in the space of model training set size and number of included classifiers in the sEVC was explored. Additionally, seven models were created from training data of 24-384 chimeric HBcAg constructs, which were validated by 100-fold Monte Carlo cross-validation. The models predicted external test sets of 184-544 chimeric HBcAg constructs. Best models showed a Matthew's correlation coefficient of >0.6 on the validation and the external test set. Feature selection was evaluated for classifiers with best and worst performance in the chimeric HBcAg VLP solubility scenario. Analysis of the associated hydrophobicity scales allowed for retrieval of biological information related to the mechanistic backgrounds of VLP solubility, suggesting a special role of arginine for VLP assembly and solubility. In the future, the developed sEVC could further be applied to hydrophobicity-related problems in other domains, such as monoclonal antibodies.

Vormittag Philipp, Klamp Thorsten, Hubbuch Jürgen

2020

feature selection, hydrophobicity, hydrophobicity scales, machine learning, solubility, virus-like particles

General General

Protein Interaction Network Reconstruction Through Ensemble Deep Learning With Attention Mechanism.

In Frontiers in bioengineering and biotechnology

Protein interactions play an essential role in studying living systems and life phenomena. A considerable amount of literature has been published on analyzing and predicting protein interactions, such as support vector machine method, homology-based method and similarity-based method, each has its pros and cons. Most existing methods for predicting protein interactions require prior domain knowledge, making it difficult to effectively extract protein features. Single method is dissatisfactory in predicting protein interactions, declaring the need for a comprehensive method that combines the advantages of various methods. On this basis, a deep ensemble learning method called EnAmDNN (Ensemble Deep Neural Networks with Attention Mechanism) is proposed to predict protein interactions which is an appropriate candidate for comprehensive learning, combining multiple models, and considering the advantages of various methods. Particularly, it encode protein sequences by the local descriptor, auto covariance, conjoint triad, pseudo amino acid composition and combine the vector representation of each protein in the protein interaction network. Then it takes advantage of the multi-layer convolutional neural networks to automatically extract protein features and construct an attention mechanism to analyze deep-seated relationships between proteins. We set up four different structures of deep learning models. In the ensemble learning model, second layer data sets are generated with five-fold cross validation from basic learners, then predict the protein interaction network by combining 16 models. Results on five independent PPI data sets demonstrate that EnAmDNN achieves superior prediction performance than other comparing methods.

Li Feifei, Zhu Fei, Ling Xinghong, Liu Quan

2020

attention mechanism, deep learning, ensemble learning, multi-layer convolutional neural network, protein-protein interaction, protein-protein interaction network

General General

A Method for Prediction of Thermophilic Protein Based on Reduced Amino Acids and Mixed Features.

In Frontiers in bioengineering and biotechnology

The thermostability of proteins is a key factor considered during enzyme engineering, and finding a method that can identify thermophilic and non-thermophilic proteins will be helpful for enzyme design. In this study, we established a novel method combining mixed features and machine learning to achieve this recognition task. In this method, an amino acid reduction scheme was adopted to recode the amino acid sequence. Then, the physicochemical characteristics, auto-cross covariance (ACC), and reduced dipeptides were calculated and integrated to form a mixed feature set, which was processed using correlation analysis, feature selection, and principal component analysis (PCA) to remove redundant information. Finally, four machine learning methods and a dataset containing 500 random observations out of 915 thermophilic proteins and 500 random samples out of 793 non-thermophilic proteins were used to train and predict the data. The experimental results showed that 98.2% of thermophilic and non-thermophilic proteins were correctly identified using 10-fold cross-validation. Moreover, our analysis of the final reserved features and removed features yielded information about the crucial, unimportant and insensitive elements, it also provided essential information for enzyme design.

Feng Changli, Ma Zhaogui, Yang Deyun, Li Xin, Zhang Jun, Li Yanjuan

2020

machine learning methods, mixed features, non-thermophilic protein, reduced amino acids, thermophilic protein

General General

Multi-Habitat Based Radiomics for the Prediction of Treatment Response to Concurrent Chemotherapy and Radiation Therapy in Locally Advanced Cervical Cancer.

In Frontiers in oncology

Objectives: To develop a radiomic model based on multiparametric magnetic resonance imaging (MRI) for predicting treatment response prior to commencing concurrent chemotherapy and radiation therapy (CCRT) for locally advanced cervical cancer. Materials and methods: The retrospective study enrolled 120 patients (allocated to a training or a test set) with locally advanced cervical cancer who underwent CCRT between December 2014 and June 2017. All patients enrolled underwent MRI with nine sequences before treatment and again at the end of the fourth week of treatment. Responses were evaluated by MRI according to RECIST standards, and patients were divided into a responder group or non-responder group. For every MRI sequence, a total of 114 radiomic features were extracted from the outlined tumor habitat. On the training set, the least absolute shrinkage and selection operator method was used to select key features and to construct nine habitat signatures. Then, three kinds of machine learning models were compared and applied to integrate these predictive signatures and the clinical characteristics into a radiomic model. The discrimination ability, reliability, and calibration of our radiomic model were evaluated. Results: The radiomic model, which consisted of three habitat signatures from sagittal T2 image, axial T1 enhanced-MRI image, and ADC image, respectively, has shown good predictive performance, with area under the curve of 0.820 (95% CI: 0.713-0.927) in the training set and 0.798 (95% CI: 0.678-0.917) in the test set. Meanwhile, the model proved to perform better than each single signature or clinical characteristic. Conclusions: A radiomic model employing features from multiple tumor habitats held the ability for predicting treatment response in patients with locally advanced cervical cancer before commencing CCRT. These results illustrated a potential new tool for improving medical decision-making and therapeutic strategies.

Fang Mengjie, Kan Yangyang, Dong Di, Yu Tao, Zhao Nannan, Jiang Wenyan, Zhong Lianzhen, Hu Chaoen, Luo Yahong, Tian Jie

2020

MRI, cervical cancer, concurrent chemotherapy and radiation therapy, precision medicine, radiomics, treatment response prediction

General General

A CNN-LSTM neural network for recognition of puffing in smoking episodes using wearable sensors.

In Biomedical engineering letters

A detailed assessment of smoking behavior under free-living conditions is a key challenge for health behavior research. A number of methods using wearable sensors and puff topography devices have been developed for smoking and individual puff detection. In this paper, we propose a novel algorithm for automatic detection of puffs in smoking episodes by using a combination of Respiratory Inductance Plethysmography and Inertial Measurement Unit sensors. The detection of puffs was performed by using a deep network containing convolutional and recurrent neural networks. Convolutional neural networks (CNN) were utilized to automate feature learning from raw sensor streams. Long Short Term Memory (LSTM) network layers were utilized to obtain the temporal dynamics of sensor signals and classify sequence of time segmented sensor streams. An evaluation was performed by using a large, challenging dataset containing 467 smoking events from 40 participants under free-living conditions. The proposed approach achieved an F1-score of 78% in leave-one-subject-out cross-validation. The results suggest that CNN-LSTM based neural network architecture sufficiently detect puffing episodes in free-living condition. The proposed model be used as a detection tool for smoking cessation programs and scientific research.

Senyurek Volkan Y, Imtiaz Masudul H, Belsare Prajakta, Tiffany Stephen, Sazonov Edward

2020-May

CNN, Cigarette smoking, Deep learning, IMU, LSTM, PACT, Puff, Respiration

Cardiology Cardiology

Severity detection tool for patients with infectious disease.

In Healthcare technology letters

Hand foot and mouth disease (HFMD) and tetanus are serious infectious diseases in low- and middle-income countries. Tetanus, in particular, has a high mortality rate and its treatment is resource-demanding. Furthermore, HFMD often affects a large number of infants and young children. As a result, its treatment consumes enormous healthcare resources, especially when outbreaks occur. Autonomic nervous system dysfunction (ANSD) is the main cause of death for both HFMD and tetanus patients. However, early detection of ANSD is a difficult and challenging problem. The authors aim to provide a proof-of-principle to detect the ANSD level automatically by applying machine learning techniques to physiological patient data, such as electrocardiogram waveforms, which can be collected using low-cost wearable sensors. Efficient features are extracted that encode variations in the waveforms in the time and frequency domains. The proposed approach is validated on multiple datasets of HFMD and tetanus patients in Vietnam. Results show that encouraging performance is achieved. Moreover, the proposed features are simple, more generalisable and outperformed the standard heart rate variability analysis. The proposed approach would facilitate both the diagnosis and treatment of infectious diseases in low- and middle-income countries, and thereby improve patient care.

Tadesse Girmaw Abebe, Zhu Tingting, Le Nguyen Thanh Nhan, Hung Nguyen Thanh, Duong Ha Thi Hai, Khanh Truong Huu, Quang Pham Van, Tran Duc Duong, Yen Lam Minh, Doorn Rogier Van, Hao Nguyen Van, Prince John, Javed Hamza, Kiyasseh Dani, Tan Le Van, Thwaites Louise, Clifton David A

2020-Apr

ANSD level, HFMD, autonomic nervous system dysfunction, cardiology, classifying ANSD levels, difficult problem, diseases, electrocardiogram, electrocardiography, enormous healthcare resources, feature extraction, frequency domains, health care, high mortality rate, infectious disease, learning (artificial intelligence), low-cost wearable sensors, medical computing, medical signal processing, middle-income countries, neurophysiology, patient care, patient diagnosis, patient treatment, photoplethysmogram waveforms, physiological patient data, proof-of-principle, resource-demanding, serious infectious diseases, severity detection tool, standard heart rate variability analysis, support vector machine, support vector machines, tetanus patients, young children

General General

Using machine learning techniques to develop risk prediction models to predict graft failure following kidney transplantation: protocol for a retrospective cohort study.

In F1000Research

Background: A mechanism to predict graft failure before the actual kidney transplantation occurs is crucial to clinical management of chronic kidney disease patients.  Several kidney graft outcome prediction models, developed using machine learning methods, are available in the literature.  However, most of those models used small datasets and none of the machine learning-based prediction models available in the medical literature modelled time-to-event (survival) information, but instead used the binary outcome of failure or not. The objective of this study is to develop two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using time-to-event data in a large national dataset from Australia.   Methods: The dataset provided by the Australia and New Zealand Dialysis and Transplant Registry will be used for the analysis. This retrospective dataset contains the cohort of patients who underwent a kidney transplant in Australia from January 1 st, 2007, to December 31 st, 2017. This included 3,758 live donor transplants and 7,365 deceased donor transplants. Three machine learning methods (survival tree, random survival forest and survival support vector machine) and one traditional regression method, Cox proportional regression, will be used to develop the two predictive models (for live donor and deceased donor transplants). The best predictive model will be selected based on the model's performance. Discussion: This protocol describes the development of two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using a large national dataset from Australia. Furthermore, these two models will be the most comprehensive kidney graft failure predictive models that have used survival data to model using machine learning techniques. Thus, these models are expected to provide valuable insight into the complex interactions between graft failure and donor and recipient characteristics.

Senanayake Sameera, Barnett Adrian, Graves Nicholas, Healy Helen, Baboolal Keshwar, Kularatna Sanjeewa

2019

Graft failure, Kidney transplant, Machine learning, Risk prediction models

Public Health Public Health

No Place Like Home: A Cross-National Assessment of the Efficacy of Social Distancing during the COVID-19 Pandemic.

In JMIR public health and surveillance

BACKGROUND : In the absence of a cure in the time of pandemics, social distancing measures seem to be the most effective intervention to slow down the spread of disease. Various simulation-based studies have been conducted in the past to investigate the effectiveness of such measures. While those studies unanimously confirm the mitigating effect of social distancing on the disease spread, the reported effectiveness varies from 10% to more than 90% reduction in the number of infections. This level of uncertainty is mostly due to the complex dynamics of epidemics and their time-variant parameters. A real transactional data, however, can reduce the uncertainty and provide a less noisy picture of social distancing effectiveness.

OBJECTIVE :