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Category articles

General General

Analyzing hCov Genome Sequences: Predicting Virulence and Mutation

bioRxiv Preprint

Covid-19 pandemic, caused by the SARS-CoV-2 genome sequence of coronavirus, has affected millions of people all over the world and taken thousands of lives. It is of utmost importance that the character of this deadly virus be studied and its nature be analyzed. We present here an analysis pipeline comprising a classification exercise to identify the virulence of the genome sequences and extraction of important features from its genetic material that are used subsequently to predict mutation at those interesting sites using deep learning techniques. We have classified the SARS-CoV-2 genome sequences with high accuracy and predicted the mutations in the sites of Interest. In a nutshell, we have prepared an analysis pipeline for hCov genome sequences leveraging the power of machine intelligence and uncovered what remained apparently shrouded by raw data.

Sawmya, S.; Saha, A.; Tasnim, S.; Toufikuzzaman, M.; Anjum, N.; Rafid, A. H. M.; Rahman, M. S.; Rahman, M. S.

2021-04-20

General General

Functional binding dynamics relevant to the evolution of zoonotic spillovers in endemic and emergent Betacoronavirus strains

bioRxiv Preprint

Comparative functional analysis of the dynamic interactions between various Betacoronavirus mutant strains and broadly utilized target proteins such as ACE2 and CD26, is crucial for a more complete understanding of zoonotic spillovers of viruses that cause diseases such as COVID-19. Here, we employ machine learning to replicated sets of nanosecond scale GPU accelerated molecular dynamics simulations to statistically compare and classify atom motions of these target proteins in both the presence and absence of different endemic and emergent strains of the viral receptor binding domain (RBD) of the S spike glycoprotein. Machine learning was used to identify functional binding dynamics that are evolutionarily conserved from bat CoV-HKU4 to human endemic/emergent strains. Conserved dynamics regions of ACE2 involve both the N-terminal helices, as well as a region of more transient dynamics encompassing K353, Q325 and a novel motif AAQPFLL 386-92 that appears to coordinate their dynamic interactions with the viral RBD at N501. We also demonstrate that the functional evolution of Betacoronavirus zoonotic spillovers involving ACE2 interaction dynamics are likely pre-adapted from two precise and stable binding sites involving the viral bat progenitor strain interaction with CD26 at SAMLI 291-5 and SS 333-334. Our analyses further indicate that the human endemic strains hCoV-HKU1 and hCoV-OC43 have evolved more stable N-terminal helix interactions through enhancement of an interfacing loop region on the viral RBD, whereas the highly transmissible SARS-CoV-2 variants (B.1.1.7, B.1.351 and P.1) have evolved more stable viral binding via more focused interactions between the viral N501 and ACE2 K353 alone.

Rynkiewicz, P.; Babbitt, G. A.; Cui, F.; Hudson, A. O.; Lynch, M. L.

2021-04-20

General General

Algoritmos de minería de datos en la industria sanitaria

ArXiv Preprint

In this paper, we review data mining approaches for health applications. Our focus is on hardware-centric approaches. Modern computers consist of multiple processors, each equipped with multiple cores, each with a set of arithmetic/logical units. Thus, a modern computer may be composed of several thousand units capable of doing arithmetic operations like addition and multiplication. Graphic processors, in addition may offer some thousand such units. In both cases, single instruction multiple data and multiple instruction multiple data parallelism must be exploited. We review the principles of algorithms which exploit this parallelism and focus also on the memory issues when multiple processing units access main memory through caches. This is important for many applications of health, such as ECG, EEG, CT, SPECT, fMRI, DTI, ultrasound, microscopy, dermascopy, etc.

Marta Li Wang

2021-04-19

General General

Masked Face Recognition using ResNet-50

ArXiv Preprint

Over the last twenty years, there have seen several outbreaks of different coronavirus diseases across the world. These outbreaks often led to respiratory tract diseases and have proved to be fatal sometimes. Currently, we are facing an elusive health crisis with the emergence of COVID-19 disease of the coronavirus family. One of the modes of transmission of COVID- 19 is airborne transmission. This transmission occurs as humans breathe in the droplets released by an infected person through breathing, speaking, singing, coughing, or sneezing. Hence, public health officials have mandated the use of face masks which can reduce disease transmission by 65%. For face recognition programs, commonly used for security verification purposes, the use of face mask presents an arduous challenge since these programs were typically trained with human faces devoid of masks but now due to the onset of Covid-19 pandemic, they are forced to identify faces with masks. Hence, this paper investigates the same problem by developing a deep learning based model capable of accurately identifying people with face-masks. In this paper, the authors train a ResNet-50 based architecture that performs well at recognizing masked faces. The outcome of this study could be seamlessly integrated into existing face recognition programs that are designed to detect faces for security verification purposes.

Bishwas Mandal, Adaeze Okeukwu, Yihong Theis

2021-04-19

General General

Masked Face Recognition using ResNet-50

ArXiv Preprint

Over the last twenty years, there have seen several outbreaks of different coronavirus diseases across the world. These outbreaks often led to respiratory tract diseases and have proved to be fatal sometimes. Currently, we are facing an elusive health crisis with the emergence of COVID-19 disease of the coronavirus family. One of the modes of transmission of COVID- 19 is airborne transmission. This transmission occurs as humans breathe in the droplets released by an infected person through breathing, speaking, singing, coughing, or sneezing. Hence, public health officials have mandated the use of face masks which can reduce disease transmission by 65%. For face recognition programs, commonly used for security verification purposes, the use of face mask presents an arduous challenge since these programs were typically trained with human faces devoid of masks but now due to the onset of Covid-19 pandemic, they are forced to identify faces with masks. Hence, this paper investigates the same problem by developing a deep learning based model capable of accurately identifying people with face-masks. In this paper, the authors train a ResNet-50 based architecture that performs well at recognizing masked faces. The outcome of this study could be seamlessly integrated into existing face recognition programs that are designed to detect faces for security verification purposes.

Bishwas Mandal, Adaeze Okeukwu, Yihong Theis

2021-04-19

General General

Leaf-inspired homeostatic cellulose biosensors.

In Science advances

An incompatibility between skin homeostasis and existing biosensor interfaces inhibits long-term electrophysiological signal measurement. Inspired by the leaf homeostasis system, we developed the first homeostatic cellulose biosensor with functions of protection, sensation, self-regulation, and biosafety. Moreover, we find that a mesoporous cellulose membrane transforms into homeostatic material with properties that include high ion conductivity, excellent flexibility and stability, appropriate adhesion force, and self-healing effects when swollen in a saline solution. The proposed biosensor is found to maintain a stable skin-sensor interface through homeostasis even when challenged by various stresses, such as a dynamic environment, severe detachment, dense hair, sweat, and long-term measurement. Last, we demonstrate the high usability of our homeostatic biosensor for continuous and stable measurement of electrophysiological signals and give a showcase application in the field of brain-computer interfacing where the biosensors and machine learning together help to control real-time applications beyond the laboratory at unprecedented versatility.

Kim Ji-Yong, Yun Yong Ju, Jeong Joshua, Kim C-Yoon, Müller Klaus-Robert, Lee Seong-Whan

2021-Apr

Public Health Public Health

Can synthetic data be a proxy for real clinical trial data? A validation study.

In BMJ open

OBJECTIVES : There are increasing requirements to make research data, especially clinical trial data, more broadly available for secondary analyses. However, data availability remains a challenge due to complex privacy requirements. This challenge can potentially be addressed using synthetic data.

SETTING : Replication of a published stage III colon cancer trial secondary analysis using synthetic data generated by a machine learning method.

PARTICIPANTS : There were 1543 patients in the control arm that were included in our analysis.

PRIMARY AND SECONDARY OUTCOME MEASURES : Analyses from a study published on the real dataset were replicated on synthetic data to investigate the relationship between bowel obstruction and event-free survival. Information theoretic metrics were used to compare the univariate distributions between real and synthetic data. Percentage CI overlap was used to assess the similarity in the size of the bivariate relationships, and similarly for the multivariate Cox models derived from the two datasets.

RESULTS : Analysis results were similar between the real and synthetic datasets. The univariate distributions were within 1% of difference on an information theoretic metric. All of the bivariate relationships had CI overlap on the tau statistic above 50%. The main conclusion from the published study, that lack of bowel obstruction has a strong impact on survival, was replicated directionally and the HR CI overlap between the real and synthetic data was 61% for overall survival (real data: HR 1.56, 95% CI 1.11 to 2.2; synthetic data: HR 2.03, 95% CI 1.44 to 2.87) and 86% for disease-free survival (real data: HR 1.51, 95% CI 1.18 to 1.95; synthetic data: HR 1.63, 95% CI 1.26 to 2.1).

CONCLUSIONS : The high concordance between the analytical results and conclusions from synthetic and real data suggests that synthetic data can be used as a reasonable proxy for real clinical trial datasets.

TRIAL REGISTRATION NUMBER : NCT00079274.

Azizi Zahra, Zheng Chaoyi, Mosquera Lucy, Pilote Louise, El Emam Khaled

2021-Apr-16

epidemiology, health informatics, information management, information technology, statistics & research methods

General General

High-resolution genomic comparisons within Salmonella enterica serotypes derived from beef feedlot cattle: parsing the roles of cattle source, pen, animal, sample type and production period.

In Applied and environmental microbiology

Salmonella enterica is a major foodborne pathogen, and contaminated beef products have been identified as one of the primary sources of Salmonella-related outbreaks. Pathogenicity and antibiotic resistance of Salmonella are highly serotype- and subpopulation-specific, which makes it essential to understand high-resolution Salmonella population dynamics in cattle. Time of year, source of cattle, pen, and sample type(i.e., feces, hide or lymph nodes) have previously been identified as important factors influencing the serotype distribution of Salmonella (e.g., Anatum, Lubbock, Cerro, Montevideo, Kentucky, Newport, and Norwich) that were isolated from a longitudinal sampling design in a research feedlot. In this study, we performed high-resolution genomic comparisons of Salmonella isolates within each serotype using both single-nucleotide polymorphism (SNP)-based maximum likelihood phylogeny and hierarchical clustering of core-genome multi-locus sequence typing. The importance of the aforementioned features on clonal Salmonella expansion was further explored using a supervised machine learning algorithm. In addition, we identified and compared the resistance genes, plasmids, and pathogenicity island profiles of the isolates within each sub-population. Our findings indicate that clonal expansion of Salmonella strains in cattle was mainly influenced by the randomization of block and pen, as well as the origin/source of the cattle; that is, regardless of sampling time and sample type (i.e., feces, lymph node or hide). Further research is needed concerning the role of the feedlot pen environment prior to cattle placement to better understand carry-over contributions of existing strains of Salmonella and their bacteriophages.IMPORTANCESalmonella serotypes isolated from outbreaks in humans can also be found in beef cattle and feedlots. Virulence factors and antibiotic resistance are among the primary defense mechanisms of Salmonella, and are often associated with clonal expansion. This makes understanding the subpopulation dynamics of Salmonella in cattle critical for effective mitigation. There remains a gap in the literature concerning subpopulation dynamics within Salmonella serotypes in feedlot cattle from the beginning of feeding up until slaughter. Here, we explore Salmonella population dynamics within each serotype using core genome phylogeny and hierarchical classifications. We used machine-learning to quantitatively parse the relative importance of both hierarchical and longitudinal clustering among cattle host samples. Our results reveal that Salmonella populations in cattle are highly clonal over a 6-month study period, and that clonal dissemination of Salmonella in cattle is mainly influenced spatially by experimental block and pen, as well by the geographical origin of the cattle.

Levent Gizem, Schlochtermeier Ashlynn, Ives Samuel E, Norman Keri N, Lawhon Sara D, Loneragan Guy H, Anderson Robin C, Vinasco Javier, Bakker Henk C den, Scott H Morgan

2021-Apr-16

Surgery Surgery

Understanding risk factors for postoperative mortality in neonates based on explainable machine learning technology.

In Journal of pediatric surgery ; h5-index 38.0

PURPOSE : We aimed to introduce an explainable machine learning technology to help clinicians understand the risk factors for neonatal postoperative mortality at different levels.

METHODS : A total of 1481 neonatal surgeries performed between May 2016 and December 2019 at a children's hospital were included in this study. Perioperative variables, including vital signs during surgery, were collected and used to predict postoperative mortality. Several widely used machine learning methods were trained and evaluated on split datasets. The model with the best performance was explained by SHAP (SHapley Additive exPlanations) at different levels.

RESULTS : The random forest model achieved the best performance with an area under the receiver operating characteristic curve of 0.72 in the validation set. TreeExplainer of SHAP was used to identify the risk factors for neonatal postoperative mortality. The explainable machine learning model not only explains the risk factors identified by traditional statistical analysis but also identifies additional risk factors. The visualization of feature contributions at different levels by SHAP makes the "black-box" machine learning model easily understood by clinicians and families. Based on this explanation, vital signs during surgery play an important role in eventual survival.

CONCLUSIONS : The explainable machine learning model not only exhibited good performance in predicting neonatal surgical mortality but also helped clinicians understand each risk factor and each individual case.

Hu Yaoqin, Gong Xiaojue, Shu Liqi, Zeng Xian, Duan Huilong, Luo Qinyu, Zhang Baihui, Ji Yaru, Wang Xiaofeng, Shu Qiang, Li Haomin

2021-Apr-05

Machine learning, Neonatal surgery, Postoperative mortality

Radiology Radiology

Differentiation between multiple sclerosis and neuromyelitis optica spectrum disorders by multiparametric quantitative MRI using convolutional neural network.

In Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia

Multiple sclerosis and neuromyelitis optica spectrum disorders are both neuroinflammatory diseases and have overlapping clinical manifestations. We developed a convolutional neural network model that differentiates between the two based on magnetic resonance imaging data. Thirty-five patients with relapsing-remitting multiple sclerosis and eighteen age-, sex-, disease duration-, and Expanded Disease Status Scale-matched patients with anti-aquaporin-4 antibody-positive neuromyelitis optica spectrum disorders were included in this study. All patients were scanned on a 3-T scanner using a multi-dynamic multi-echo sequence that simultaneously measures R1 and R2 relaxation rates and proton density. R1, R2, and proton density maps were analyzed using our convolutional neural network model. To avoid overfitting on a small dataset, we aimed to separate features of images into those specific to an image and those common to the group, based on SqueezeNet. We used only common features for classification. Leave-one-out cross validation was performed to evaluate the performance of the model. The area under the receiver operating characteristic curve of the developed convolutional neural network model for differentiating between the two disorders was 0.859. The sensitivity to multiple sclerosis and neuromyelitis optica spectrum disorders, and accuracy were 80.0%, 83.3%, and 81.1%, respectively. In conclusion, we developed a convolutional neural network model that differentiates between multiple sclerosis and neuromyelitis optica spectrum disorders, and which is designed to avoid overfitting on small training datasets. Our proposed algorithm may facilitate a differential diagnosis of these diseases in clinical practice.

Hagiwara Akifumi, Otsuka Yujiro, Andica Christina, Kato Shimpei, Yokoyama Kazumasa, Hori Masaaki, Fujita Shohei, Kamagata Koji, Hattori Nobutaka, Aoki Shigeki

2021-May

Deep learning, Magnetic resonance imaging, Multiparametric quantitative imaging, Multiple sclerosis, Neuromyelitis optica spectrum disorder

Public Health Public Health

Selective colorimetric urine glucose detection by paper sensor functionalized with polyaniline nanoparticles and cell membrane.

In Analytica chimica acta

For the diabetes diagnosis, noninvasive methods are preferred to invasive methods; urine glucose measurement is an example of a noninvasive method. However, conventional noninvasive methods for urine glucose measurement are not intuitive. Furthermore, such methods exhibit low selectivity because they can detect interfering molecules in addition to glucose. Herein, we fabricate a noninvasive, intuitive, and highly selective paper sensor consisting of polyaniline nanoparticles (PAni-NPs) and red blood cell membranes (RBCMs). The PAni-NPs (adsorbed on the paper) are highly sensitive to hydrogen ions and change color from emeraldine blue to emeraldine green within a few seconds. The RBCM (coated on the PAni-NP-adsorbed paper) having the glucose transporter-1 protein plays the role of a smart filter that transports glucose but rejects other interfering molecules. In particular, the selectivity of the RBCM-coated PAni-NP-based paper sensor was approximately improved ∼85%, compared to the uncoated paper sensors. The paper sensor could detect urine glucose over the range of 0-10 mg/mL (0-56 mM), with a limit of detection of 0.54 mM. The proposed paper sensor will facilitate the development of a highly selective and colorimetric urine glucose monitoring system.

Lee Taeha, Kim Insu, Cheong Da Yeon, Roh Seokbeom, Jung Hyo Gi, Lee Sang Won, Kim Hyun Soo, Yoon Dae Sung, Hong Yoochan, Lee Gyudo

2021-May-08

Colorimetric paper sensor, High selectivity, Noninvasive, Polyaniline nanoparticles, Red blood cell membrane, Urine glucose

General General

Long non-coding RNA pairs to assist in diagnosing sepsis.

In BMC genomics ; h5-index 78.0

BACKGROUND : Sepsis is the major cause of death in Intensive Care Unit (ICU) globally. Molecular detection enables rapid diagnosis that allows early intervention to minimize the death rate. Recent studies showed that long non-coding RNAs (lncRNAs) regulate proinflammatory genes and are related to the dysfunction of organs in sepsis. Identifying lncRNA signature with absolute abundance is challenging because of the technical variation and the systematic experimental bias.

RESULTS : Cohorts (n = 768) containing whole blood lncRNA profiling of sepsis patients in the Gene Expression Omnibus (GEO) database were included. We proposed a novel diagnostic strategy that made use of the relative expressions of lncRNA pairs, which are reversed between sepsis patients and normal controls (eg. lncRNAi > lncRNAj in sepsis patients and lncRNAi < lncRNAj in normal controls), to identify 14 lncRNA pairs as a sepsis diagnostic signature. The signature was then applied to independent cohorts (n = 644) to evaluate its predictive performance across different ages and normalization methods. Comparing to common machine learning models and existing signatures, SepSigLnc consistently attains better performance on the validation cohorts from the same age group (AUC = 0.990 & 0.995 in two cohorts) and across different groups (AUC = 0.878 on average), as well as cohorts processed by an alternative normalization method (AUC = 0.953 on average). Functional analysis demonstrates that the lncRNA pairs in SepsigLnc are functionally similar and tend to implicate in the same biological processes including cell fate commitment and cellular response to steroid hormone stimulus.

CONCLUSION : Our study identified 14 lncRNA pairs as signature that can facilitate the diagnosis of septic patients at an intervenable point when clinical manifestations are not dramatic. Also, the computational procedure can be generalized to a standard procedure for discovering diagnostic molecule signatures.

Zheng Xubin, Leung Kwong-Sak, Wong Man-Hon, Cheng Lixin

2021-Apr-16

Diagnostics, Long non-coding RNA, Relative expression, Sepsis, Signature

Pathology Pathology

Exploring the changes of brain immune microenvironment in Alzheimer's disease based on PANDA algorithm combined with blood brain barrier injury-related genes.

In Biochemical and biophysical research communications

Studies have shown that the specific entry of peripheral cells into the brain parenchyma caused by BBB injury and the imbalance of the immune microenvironment in the brain are closely related to the pathogenesis of Alzheimer's disease (AD). Because of the difficulty of obtaining data inside the brain, it is urgent to find out the relationship between the peripheral and intracerebral data and their influence on the development of AD by machine learning methods. However, in the actual algorithm design, it is still a challenge to extract relevant information from a variety of data to establish a complete and accurate regulatory network. In order to overcome the above difficulties, we presented a method based on a message passing model (Passing Attributes between Networks for Data Assimilation, PANDA) to discover the correlation between internal and external brain by the BBB injury-related genes, and further explore their regulatory mechanism of the brain immune environment for AD pathology. The Biological analysis of the results showed that pathways such as immune response pathway, inflammatory response pathway and chemokine signaling pathway are closely related to the pathogenesis of AD. Especially, some significant genes such as RELA, LAMA4, PPBP were found play certain roles in the injury of BBB and the change of permeability in AD patients, thus leading to the change of immune microenvironment in AD brain.

Lu Shiting, Kong Wei, Wang Shuaiqun

2021-Apr-14

Alzheimer’s disease, Blood brain barrier, Mutual information, PANDA

General General

Automated caries detection with smartphone color photography using machine learning.

In Health informatics journal ; h5-index 25.0

Untreated caries is significant problem that affected billion people over the world. Therefore, the appropriate method and accuracy of caries detection in clinical decision-making in dental practices as well as in oral epidemiology or caries research, are required urgently. The aim of this study was to introduce a computational algorithm that can automate recognize carious lesions on tooth occlusal surfaces in smartphone images according to International Caries Detection and Assessment System (ICDAS). From a group of extracted teeth, 620 unrestored molars/premolars were photographed using smartphone. The obtained images were evaluated for caries diagnosis with the ICDAS II codes, and were labeled into three classes: "No Surface Change" (NSC); "Visually Non-Cavitated" (VNC); "Cavitated" (C). Then, a two steps detection scheme using Support Vector Machine (SVM) has been proposed: "C versus (VNC+NSC)" classification, and "VNC versus NSC" classification. The accuracy, sensitivity, and specificity of best model were 92.37%, 88.1%, and 96.6% for "C versus (VNC+NSC)," whereas they were 83.33%, 82.2%, and 66.7% for "VNC versus NSC." Although the proposed SVM system required further improvement and verification, with the data only imaged from the smartphone, it performed an auspicious potential for clinical diagnostics with reasonable accuracy and minimal cost.

Duong Duc Long, Kabir Malitha Humayun, Kuo Rong Fu

artificial intelligence, caries detection, computer modeling, digital imaging, image analysis, machine learning

General General

Groundwater level modeling framework by combining the wavelet transform with a long short-term memory data-driven model.

In The Science of the total environment

Developing models that can accurately simulate groundwater level is important for water resource management and aquifer protection. In particular, machine learning tools provide a new and promising approach to efficiently forecast long-term groundwater table fluctuations without the computational burden of building a detailed flow model. This study proposes a multistep modeling framework for simulating groundwater levels by combining the wavelet transform (WT) with the long short-term memory (LSTM) network; the framework is named the combined WT-multivariate LSTM (WT-MLSTM) method. First, the WT decomposes the groundwater level time series (i.e., the training stage) into a self-control term and a set of external-control terms. Second, Pearson correlation analysis reveals the correlations between the influencing factors (i.e., river stage) and the groundwater table, and the multivariate LSTM model incorporating external factors is built to simulate the external-control terms. Third, the spatiotemporal evolution of the groundwater level is modeled by reconstructing the sequence of each term of the groundwater level time series. Methodological applications in the Liangshui River Basin, Beijing, China and the Cibola National Wildlife Refuge along the lower Colorado River, United States, show that the combined WT-MLSTM model has a higher simulation accuracy than the standard LSTM, MLSTM, and WT-LSTM models. A comparison between the combined WT-MLSTM model and support vector machine (SVM) also demonstrates the advantage of the proposed model. Additional comparison between model forecasts and observed groundwater levels shows the model predictability for short-term time series. Further analysis reveals that the applicability of the combined WT-MLSTM model decreases with increasing distance between the groundwater well and adjacent river channel, or with the increasing complexity of the changing groundwater level patterns, which may be driven by additional controlling factors. This study therefore provides a new methodology/approach for the rapid and accurate simulation and prediction of groundwater level.

Wu Chengcheng, Zhang Xiaoqin, Wang Wanjie, Lu Chengpeng, Zhang Yong, Qin Wei, Tick Geoffrey R, Liu Bo, Shu Longcang

2021-Apr-08

Groundwater level simulation, Long short-term memory (LSTM) model, Surface water, Wavelet transform (WT)

Radiology Radiology

Improved outcome prediction of oropharyngeal cancer by combining clinical and MRI features in machine learning models.

In European journal of radiology ; h5-index 47.0

OBJECTIVES : New markers are required to predict chemoradiation response in oropharyngeal squamous cell carcinoma (OPSCC) patients. This study evaluated the ability of magnetic resonance (MR) radiomics to predict locoregional control (LRC) and overall survival (OS) after chemoradiation and aimed to determine whether this has added value to traditional clinical outcome predictors.

METHODS : 177 OPSCC patients were eligible for this study. Radiomic features were extracted from the primary tumor region in T1-weighted postcontrast MRI acquired before chemoradiation. Logistic regression models were created using either clinical variables (clinical model), radiomic features (radiomic model) or clinical and radiomic features combined (combined model) to predict LRC and OS 2-years posttreatment. Model performance was evaluated using area under the curve (AUC), 95 % confidence intervals were calculated using 500 iterations of bootstrap. All analyses were performed for the total population and the Human papillomavirus (HPV) negative tumor subgroup.

RESULTS : A combined model predicted treatment outcome with a higher AUC (LRC: 0.745 [0.734-0.757], OS: 0.744 [0.735-0.753]) than the clinical model (LRC: 0.607 [0.594-0.620], OS: 0.708 [0.697-0.719]). Performance of the radiomic model was comparable to the combined model for LRC (AUC: 0.740 [0.729-0.750]), but not for OS prediction (AUC: 0.654 [0.646-0.662]). In HPV negative patients, the performance of all models was not sufficient with AUCs ranging from 0.587 to 0.660 for LRC and 0.559 to 0.600 for OS prediction.

CONCLUSION : Predictive models that include clinical variables and radiomic tumor features derived from MR images of OPSCC better predict LRC after chemoradiation than models based on only clinical variables. Predictive models that include clinical variables perform better than models based on only radiomic features for the prediction of OS.

Bos Paula, van den Brekel Michiel W M, Gouw Zeno A R, Al-Mamgani Abrahim, Taghavi Marjaneh, Waktola Selam, Aerts Hugo J W L, Castelijns Jonas A, Beets-Tan Regina G H, Jasperse Bas

2021-Apr-08

Head and neck neoplasms, Machine learning, Magnetic Resonance Imaging, Oropharyngeal neoplasms, Radiomics, Treatment outcome

General General

Multi-scale modeling for systematically understanding the key roles of microglia in AD development.

In Computers in biology and medicine

Alzheimer's disease (AD) is the leading cause of age-related dementia, affecting over 5 million people in the United States. Unfortunately, current therapies are largely palliative and several potential drug candidates have failed in late-stage clinical trials. Studies suggest that microglia-mediated neuroinflammation might be responsible for the failures of various therapies. Microglia contribute to Aβ clearance in the early stage of neurodegeneration and may contribute to AD development at the late stage by releasing pro-inflammatory cytokines. However, the activation profile and phenotypic changes of microglia during the development of AD are poorly understood. To systematically understand the key role of microglia in AD progression and predict the optimal therapeutic strategy in silico, we developed a 3D multi-scale model of AD (MSMAD) by integrating multi-level experimental data, to manipulate the neurodegeneration in a simulated system. Based on our analysis, we revealed that how TREM2-related signal transduction leads to an imbalance in the activation of different microglia phenotypes, thereby promoting AD development. Our MSMAD model also provides an optimal therapeutic strategy for improving the outcome of AD treatment.

Ji Zhiwei, Liu Changan, Zhao Weiling, Soto Claudio, Zhou Xiaobo

2021-Apr-05

AD, Agent-based model, Microglia, Plaque, TREM2

Surgery Surgery

Automatic tip detection of surgical instruments in biportal endoscopic spine surgery.

In Computers in biology and medicine

BACKGROUND : Recent advances in robotics and deep learning can be used in endoscopic surgeries and can provide numerous advantages by freeing one of the surgeon's hands. This study aims to automatically detect the tip of the instrument, localize a point, and evaluate the detection accuracy in biportal endoscopic spine surgery (BESS). The tip detection could serve as a preliminary study for the development of vision intelligence in robotic endoscopy.

METHODS : The dataset contains 2310 frames from 9 BESS videos with x and y coordinates of the tip annotated by an expert. We trained two state-of-the-art detectors, RetinaNet and YOLOv2, with bounding boxes centered around the tip annotations with specific margin sizes to determine the optimal margin size for detecting the tip of the instrument and localizing the point. We calculated the recall, precision, and F1-score with a fixed box size for both ground truth tip coordinates and predicted midpoints to compare the performance of the models trained with different margin size bounding boxes.

RESULTS : For RetinaNet, a margin size of 150 pixels was optimal with a recall of 1.000, precision of 0.733, and F1-score of 0.846. For YOLOv2, a margin size of 150 pixels was optimal with a recall of 0.864, precision of 0.808, F1-score of 0.835. Also, the optimal margin size of 150 pixels of RetinaNet was used to cross-validate its overall robustness. The resulting mean recall, precision, and F1-score were 1.000 ± 0.000, 0.767 ± 0.033, and 0.868 ± 0.022, respectively.

CONCLUSIONS : In this study, we evaluated an automatic tip detection method for surgical instruments in endoscopic surgery, compared two state-of-the-art detection algorithms, RetinaNet and YOLOv2, and validated the robustness with cross-validation. This method can be applied in different types of endoscopy tip detection.

Cho Sue Min, Kim Young-Gon, Jeong Jinhoon, Kim Inhwan, Lee Ho-Jin, Kim Namkug

2021-Apr-14

Convolutional neural network, Deep learning, Detection, Endoscopic surgery, Localization

Cardiology Cardiology

A deep learning approach for the automatic recognition of prosthetic mitral valve in echocardiographic images.

In Computers in biology and medicine

The first step in the automatic evaluation of the cardiac prosthetic valve is the recognition of such valves in echocardiographic images. This research surveyed whether a deep convolutional neural network (DCNN) could improve the recognition of prosthetic mitral valve in conventional 2D echocardiographic images. An efficient intervention to decrease the misreading rate of the prosthetic mitral valve is required for non-expert cardiologists. This intervention could serve as a section of a fully-automated analysis chain, alleviate the cardiologist's workload, and improve precision and time management, especially in an emergent situation. Additionally, it might be suitable for pre-labeling large databases of unclassified images. We, therefore, introduce a large publicly-available annotated dataset for the purpose of prosthetic mitral valve recognition. We utilized 2044 comprehensive non-stress transthoracic echocardiographic studies. Totally, 1597 patients had natural mitral valves and 447 patients had prosthetic valves. Each case contained 1 cycle of echocardiographic images from the apical 4-chamber (A4C) and the parasternal long-axis (PLA) views. Thirteen versions of the state-of-the-art models were independently trained, and the ensemble predictions were performed using those versions. For the recognition of prosthetic mitral valves from natural mitral valves, the area under the receiver-operating characteristic curve (AUC) made by the deep learning algorithm was similar to that made by cardiologists (0.99). In this research, EfficientNetB3 architecture in the A4C view and the EfficientNetB4 architecture in the PLA view were the best models among the other pre-trained DCNN models.

Vafaeezadeh Majid, Behnam Hamid, Hosseinsabet Ali, Gifani Parisa

2021-Apr-14

DCNN, Echocardiographic, EfficientNet, Prosthetic mitral valve

Surgery Surgery

CaDIS: Cataract dataset for surgical RGB-image segmentation.

In Medical image analysis

Video feedback provides a wealth of information about surgical procedures and is the main sensory cue for surgeons. Scene understanding is crucial to computer assisted interventions (CAI) and to post-operative analysis of the surgical procedure. A fundamental building block of such capabilities is the identification and localization of surgical instruments and anatomical structures through semantic segmentation. Deep learning has advanced semantic segmentation techniques in the recent years but is inherently reliant on the availability of labelled datasets for model training. This paper introduces a dataset for semantic segmentation of cataract surgery videos complementing the publicly available CATARACTS challenge dataset. In addition, we benchmark the performance of several state-of-the-art deep learning models for semantic segmentation on the presented dataset. The dataset is publicly available at https://cataracts-semantic-segmentation2020.grand-challenge.org/.

Grammatikopoulou Maria, Flouty Evangello, Kadkhodamohammadi Abdolrahim, Quellec Gwenolé, Chow Andre, Nehme Jean, Luengo Imanol, Stoyanov Danail

2021-Mar-31

Cataract surgery, Dataset, Semantic segmentation

General General

Decoding imagined speech and computer control using brain waves.

In Journal of neuroscience methods

BACKGROUND : . In this work, we explore the possibility of decoding Imagined Speech (IS) brain waves using machine learning techniques.

APPROACH : . We design two finite state machines to create an interface for controlling a computer system using an IS-based brain-computer interface. To decode IS signals, we propose a covariance matrix of Electroencephalogram channels as input features, covariance matrices projection to tangent space for obtaining vectors from matrices, principal component analysis for dimension reduction of vectors, an artificial neural network (ANN) as a classification model, and bootstrap aggregation for creating an ensemble of ANN models.

RESULT : . Based on these findings, we are first to use an IS-based system to operate a computer and obtain an information transfer rate of 21-bits-per-minute. The proposed approach can decode the IS signal with a mean classification accuracy of 85% on classifying one long vs. short word. Our proposed approach can also differentiate between IS and rest state brain signals with a mean classification accuracy of 94%. COMPARISON: . After comparison, we show that our approach performs equivalent to the state-of-the-art approach (SOTA) on decoding long vs. short word classification task. We also show that the proposed method outperforms SOTA significantly on decoding three short words and vowels with an average margin of 11% and 9%, respectively.

CONCLUSION : . These results show that the proposed approach can decode a wide variety of IS signals and is practically applicable in a real-time environment.

Singh Abhiram, Gumaste Ashwin

2021-Apr-14

Artificial neural network, Brain-computer interface, Electroencephalogram, Finite state MACHINE, Imagined speech

General General

Convolutional Neural Networks Ensemble Model for Neonatal Seizure Detection.

In Journal of neuroscience methods

BACKGROUND : Neonatal seizures are a common occurrence in clinical settings, requiring immediate attention and detection. Previous studies have proposed using manual feature extraction coupled with machine learning, or deep learning to classify between seizure and non-seizure states.

NEW METHOD : In this paper a deep learning based approach is used for neonatal seizure classification using electroencephalogram (EEG) signals. The architecture detects seizure activity in raw EEG signals as opposed to common state-of-art, where manual feature extraction with machine learning algorithms is used. The architecture is a two-dimensional (2D) convolutional neural network (CNN) to classify between seizure/non-seizure states.

RESULTS : The dataset used for this study is annotated by three experts and as such three separate models are trained on individual annotations, resulting in average accuracies (ACC) of 95.6%, 94.8% and 90.1% respectively, and average area under the receiver operating characteristic curve (AUC) of 99.2%, 98.4% and 96.7% respectively. The testing was done using 10-cross fold validation, so that the performance can be an accurate representation of the architectures classification capability in a clinical setting. After training/testing of the three individual models, a final ensemble model is made consisting of the three models. The ensemble model gives an average ACC and AUC of 96.3% and 99.3% respectively.

COMPARISON WITH EXISTING METHODS : This study outperforms previous studies, with increased ACC and AUC results coupled with use of small time windows (1 second) used for evaluation.

CONCLUSION : The proposed approach is promising for detecting seizure activity in unseen neonate data in a clinical setting.

Tanveer M Asjid, Khan Muhammad Jawad, Sajid Hasan, Naseer Noman

2021-Apr-14

EEG, convolutional neural network, detection, ensemble model, neonatal, seizures

Surgery Surgery

Semiparametric analysis of clustered interval-censored survival data using soft Bayesian Additive Regression Trees (SBART).

In Biometrics

Popular parametric and semiparametric hazards regression models for clustered survival data are inappropriate and inadequate when the unknown effects of different covariates and clustering are complex. This calls for a flexible modeling framework to yield efficient survival prediction. Moreover, for some survival studies involving time to occurrence of some asymptomatic events, survival times are typically interval censored between consecutive clinical inspections. In this article, we propose a robust semiparametric model for clustered interval-censored survival data under a paradigm of Bayesian ensemble learning, called Soft Bayesian Additive Regression Trees or SBART (Linero and Yang, 2018), which combines multiple sparse (soft) decision trees to attain excellent predictive accuracy. We develop a novel semiparametric hazards regression model by modeling the hazard function as a product of a parametric baseline hazard function and a nonparametric component that uses SBART to incorporate clustering, unknown functional forms of the main effects, and interaction effects of various covariates. In addition to being applicable for left-censored, right-censored, and interval-censored survival data, our methodology is implemented using a data augmentation scheme which allows for existing Bayesian backfitting algorithms to be used. We illustrate the practical implementation and advantages of our method via simulation studies and an analysis of a prostate cancer surgery study where dependence on the experience and skill level of the physicians leads to clustering of survival times. We conclude by discussing our method's applicability in studies involving high dimensional data with complex underlying associations.

Basak Piyali, Linero Antonio, Sinha Debajyoti, Lipsitz Stuart

2021-Apr-17

Bayesian Additive Regression Trees, machine learning, nonproportional hazards, semiparametric, survival analysis

Radiology Radiology

Feasibility and prognostic role of machine learning-based FFRCT in patients with stent implantation.

In European radiology ; h5-index 62.0

OBJECTIVES : To investigate the feasibility and prognostic implications of coronary CT angiography (CCTA) derived fractional flow reserve (FFRCT) in patients who have undergone stents implantation.

METHODS : Firstly, the feasibility of FFRCT in stented vessels was validated. The diagnostic performance of FFRCT in identifying hemodynamically in-stent restenosis (ISR) in 33 patients with invasive FFR ≤ 0.88 as reference standard, intra-group correlation coefficient (ICC) between FFRCT and FFR was calculated. Secondly, prognostic value was assessed with 115 patients with serial CCTA scans after PCI. Stent characteristics (location, diameter, length, etc.), CCTA measurements (minimum lumen diameter [MLD], minimum lumen area [MLA], ISR), and FFRCT measurements (FFRCT, ΔFFRCT, ΔFFRCT/stent length) both at baseline and follow-up were recorded. Longitudinal analysis included changes of MLD, MLA, ISR, and FFRCT. The primary endpoint was major adverse cardiovascular events (MACE).

RESULTS : Per-patient accuracy of FFRCT was 0.85 in identifying hemodynamically ISR. FFRCT had a good correlation with FFR (ICC = 0.84). 15.7% (18/115) developed MACE during 25 months since follow-up CCTA. Lasso regression identified age and follow-up ΔFFRCT/length as candidate variables. In the Cox proportional hazards model, age (hazard ratio [HR], 1.102 [95% CI, 1.032-1.177]; p = 0.004) and follow-up ΔFFRCT/length (HR, 1.014 [95% CI, 1.006-1.023]; p = 0.001) were independently associated with MACE (c-index = 0.856). Time-dependent ROC analysis showed AUC was 0.787 (95% CI, 0.594-0.980) at 25 months to predict adverse outcome. After bootstrap validation with 1000 resamplings, the bias-corrected c-index was 0.846.

CONCLUSIONS : Noninvasive ML-based FFRCT is feasible in patients following stents implantation and shows prognostic value in predicting adverse events after stents implantation in low-moderate risk patients.

KEY POINTS : • Machine-learning-based FFRCT is feasible to evaluate the functional significance of in-stent restenosis in patients with stent implantation. • Follow-up △FFRCT along with the stent length might have prognostic implication in patients with stent implantation and low-to-moderate risk after 2 years follow-up. The prognostic role of FFRCT in patients with moderate-to-high or high risk needs to be further studied. • FFRCT might refine the clinical pathway of patients with stent implantation to invasive catheterization.

Tang Chun Xiang, Guo Bang Jun, Schoepf Joseph U, Bayer Richard R, Liu Chun Yu, Qiao Hong Yan, Zhou Fan, Lu Guang Ming, Zhou Chang Sheng, Zhang Long Jiang

2021-Apr-17

Computed tomography angiography, Coronary artery disease, Coronary restenosis, Myocardial fractional flow reserve, Stents

General General

Exact solution to the random sequential dynamics of a message passing algorithm.

In Physical review. E

We analyze the random sequential dynamics of a message passing algorithm for Ising models with random interactions in the large system limit. We derive exact results for the two-time correlation functions and the speed of convergence. The de Almedia-Thouless stability criterion of the static problem is found to be necessary and sufficient for the global convergence of the random sequential dynamics.

Çakmak Burak, Opper Manfred

2021-Mar

General General

H3ABioNet genomic medicine and microbiome data portals hackathon proceedings.

In Database : the journal of biological databases and curation

African genomic medicine and microbiome datasets are usually not well characterized in terms of their origin, making it difficult to find and extract data for specific African ethnic groups or even countries. The Pan-African H3Africa Bioinformatics Network (H3ABioNet) recognized the need for developing data portals for African genomic medicine and African microbiomes to address this and ran a hackathon to initiate their development. The two portals were designed and significant progress was made in their development during the hackathon. All the participants worked in a very synergistic and collaborative atmosphere in order to achieve the hackathon's goals. The participants were divided into content and technical teams and worked over a period of 6 days. In response to one of the survey questions of what the participants liked the most during the hackathon, 55% of the hackathon participants highlighted the familial and friendly atmosphere, the team work and the diversity of team members and their expertise. This paper describes the preparations for the portals hackathon and the interaction between the participants and reflects upon the lessons learned about its impact on successfully developing the two data portals as well as building scientific expertise of younger African researchers. Database URL: The code for developing the two portals was made publicly available in GitHub repositories: [https://github.com/codemeleon/Database; https://github.com/codemeleon/AfricanMicrobiomePortal].

Fadlelmola Faisal M, Ghedira Kais, Hamdi Yosr, Hanachi Mariem, Radouani Fouzia, Allali Imane, Kiran Anmol, Zass Lyndon, Alsayed Nihad, Fassatoui Meriem, Samtal Chaimae, Ahmed Samah, Da Rocha Jorge, Chaqsare Souad, Sallam Reem M, Chaouch Melek, Farahat Mohammed, Ssekagiri Alfred, Parker Ziyaad, Adil Mai, Turkson Michael, Benchaalia Aymen, Benkahla Alia, Panji Sumir, Kassim Samar, Souiai Oussema, Mulder Nicola

2021-Apr-17

Dermatology Dermatology

Accuracy of a convolutional neural network for dermatologic diagnosis of tumors and skin lesions in a clinical setting.

In Clinical and experimental dermatology

Artificial intelligence (AI) will likely have an increasingly important role in dermatology given recent advances in machine learning, particularly for image analysis and identification. The convolutional neural network (CNN), a type of deep neural network with multiple layers between the input and output parameters, is commonly used to analyze images.1 A recent CNN trained on almost 130,000 clinical images of skin tumors achieved a binary classification accuracy (i.e., benign vs. malignant) on par with 21 U.S. board-certified dermatologists.

Agarwala S, Mata D A, Hafeez F

2021-Apr-17

General General

Current and future carbon stocks in coastal wetlands within the Great Barrier Reef catchments.

In Global change biology

Australia's Great Barrier Reef (GBR) catchments include some of the world's most intact coastal wetlands comprising diverse mangrove, seagrass and tidal marsh ecosystems. Although these ecosystems are highly efficient at storing carbon in marine sediments, their soil organic carbon (SOC) stocks and the potential changes resulting from climate impacts, including sea level rise are not well understood. For the first time, we estimated SOC stocks and their drivers within the range of coastal wetlands of GBR catchments using boosted regression trees (i.e., a machine learning approach and ensemble method for modelling the relationship between response and explanatory variables) and identified the potential changes in future stocks due to sea level rise. We found levels of SOC stocks of mangrove and seagrass meadows have different drivers, with climatic variables such as temperature, rainfall, and solar radiation, showing significant contributions in accounting for variation in SOC stocks in mangroves. In contrast, soil type accounted for most of the variability in seagrass meadows. Total SOC stock in the GBR catchments, including mangroves, seagrass meadows and tidal marshes, is approximately 137 Tg C, which represents 9-13% of Australia's total SOC stock while encompassing only 4-6% of the total extent of Australian coastal wetlands. In a global context, this could represent 0.5-1.4% of global SOC stock. Our study suggests that landward migration due to projected sea level rise has the potential to enhance carbon accumulation with total carbon gains between 0.16-0.46 Tg C and provides an opportunity for future restoration to enhance blue carbon.

Duarte de Paula Costa Micheli, Lovelock Catherine E, Waltham Nathan J, Young Mary, Adame Maria Fernanda, Bryant Catherine V, Butler Don, Green David, Rasheed Michael A, Salinas Cristian, Serrano Oscar, York Paul H, Whitt Ashley A, Macreadie Peter I

2021-Apr-16

blue carbon, climate change, coastal wetlands, mangroves, seagrass meadows, soil carbon stocks, tidal marshes

General General

Exploring communication between the thalamus and cognitive control-related functional networks in the cerebral cortex.

In Cognitive, affective & behavioral neuroscience

It has been suggested by multiple studies (postmortem studies, invasive animal studies, and diffusion tensor imaging in the human brain) that the thalamus is important for communication among cortical regions. Many functional magnetic resonance imaging (fMRI) studies, including noninvasive and whole-brain studies, have reported thalamic co-activation with several cognitive control-related cortical systems. This forms a complex network that may be important for advanced cognitive control-related processes, such as working memory and attention. Nevertheless, how the thalamus communicates with the cognitive control-related network in the intact human brain is an essential question and needs further investigation. To address this question, we conducted a study using dynamic functional connectivity analysis and effective connectivity analysis based on fMRI data from young, healthy adult participants. The results showed that the middle thalamus exhibited both high in- and out-degree regarding the complex network related to cognitive control during both rest and task conditions. Furthermore, intrinsic communication via the middle thalamic regions showed dynamically co-varying patterns, and the thalamic regions showed high flexibility in dynamic community analysis. These results indicated that the mid-thalamic region is an important station for communication between nodes in cognitive control-related networks.

Wen Xiaotong, Li Wen, Liu Yuan, Liu Zhenghao, Zhao Ping, Zhu Zhiyuan, Wu Xia

2021-Apr-17

Cognitive control, Dynamic functional connectivity, Effective connectivity, Middle thalamus

Surgery Surgery

Deep learning to segment pelvic bones: large-scale CT datasets and baseline models.

In International journal of computer assisted radiology and surgery

PURPOSE : Pelvic bone segmentation in CT has always been an essential step in clinical diagnosis and surgery planning of pelvic bone diseases. Existing methods for pelvic bone segmentation are either hand-crafted or semi-automatic and achieve limited accuracy when dealing with image appearance variations due to the multi-site domain shift, the presence of contrasted vessels, coprolith and chyme, bone fractures, low dose, metal artifacts, etc. Due to the lack of a large-scale pelvic CT dataset with annotations, deep learning methods are not fully explored.

METHODS : In this paper, we aim to bridge the data gap by curating a large pelvic CT dataset pooled from multiple sources, including 1184 CT volumes with a variety of appearance variations. Then, we propose for the first time, to the best of our knowledge, to learn a deep multi-class network for segmenting lumbar spine, sacrum, left hip, and right hip, from multiple-domain images simultaneously to obtain more effective and robust feature representations. Finally, we introduce a post-processor based on the signed distance function (SDF).

RESULTS : Extensive experiments on our dataset demonstrate the effectiveness of our automatic method, achieving an average Dice of 0.987 for a metal-free volume. SDF post-processor yields a decrease of 15.1% in Hausdorff distance compared with traditional post-processor.

CONCLUSION : We believe this large-scale dataset will promote the development of the whole community and open source the images, annotations, codes, and trained baseline models at https://github.com/ICT-MIRACLE-lab/CTPelvic1K .

Liu Pengbo, Han Hu, Du Yuanqi, Zhu Heqin, Li Yinhao, Gu Feng, Xiao Honghu, Li Jun, Zhao Chunpeng, Xiao Li, Wu Xinbao, Zhou S Kevin

2021-Apr-16

CT dataset, Deep learning, Pelvic segmentation, SDF post-processing

Internal Medicine Internal Medicine

Artificial intelligence-assisted analysis of endoscopic retrograde cholangiopancreatography image for identifying ampulla and difficulty of selective cannulation.

In Scientific reports ; h5-index 158.0

The advancement of artificial intelligence (AI) has facilitated its application in medical fields. However, there has been little research for AI-assisted endoscopy, despite the clinical significance of the efficiency and safety of cannulation in the endoscopic retrograde cholangiopancreatography (ERCP). In this study, we aim to assist endoscopists performing ERCP through automatic detection of the ampulla and the identification of cannulation difficulty. We developed a novel AI-assisted system based on convolutional neural networks that predict the location of the ampulla and the difficulty of cannulation to the ampulla. ERCP data of 531 and 451 patients were utilized in the evaluation of our model for each task. Our model detected the ampulla with mean intersection-over-union 64.1%, precision 76.2%, recall 78.4%, and centroid distance 0.021. In classifying the cannulation difficulty, it achieved the recall of 71.9% for the class of easy cases and that of 61.1% for that of difficult cases. Remarkably, our model accurately detected AOV with varying morphological shape, size, and texture on par with the level of a human expert and showed promising results for recognizing cannulation difficulty. It demonstrated its potential to improve the quality of ERCP by assisting endoscopists.

Kim Taesung, Kim Jinhee, Choi Hyuk Soon, Kim Eun Sun, Keum Bora, Jeen Yoon Tae, Lee Hong Sik, Chun Hoon Jai, Han Sung Yong, Kim Dong Uk, Kwon Soonwook, Choo Jaegul, Lee Jae Min

2021-Apr-16

Radiology Radiology

Data valuation for medical imaging using Shapley value and application to a large-scale chest X-ray dataset.

In Scientific reports ; h5-index 158.0

The reliability of machine learning models can be compromised when trained on low quality data. Many large-scale medical imaging datasets contain low quality labels extracted from sources such as medical reports. Moreover, images within a dataset may have heterogeneous quality due to artifacts and biases arising from equipment or measurement errors. Therefore, algorithms that can automatically identify low quality data are highly desired. In this study, we used data Shapley, a data valuation metric, to quantify the value of training data to the performance of a pneumonia detection algorithm in a large chest X-ray dataset. We characterized the effectiveness of data Shapley in identifying low quality versus valuable data for pneumonia detection. We found that removing training data with high Shapley values decreased the pneumonia detection performance, whereas removing data with low Shapley values improved the model performance. Furthermore, there were more mislabeled examples in low Shapley value data and more true pneumonia cases in high Shapley value data. Our results suggest that low Shapley value indicates mislabeled or poor quality images, whereas high Shapley value indicates data that are valuable for pneumonia detection. Our method can serve as a framework for using data Shapley to denoise large-scale medical imaging datasets.

Tang Siyi, Ghorbani Amirata, Yamashita Rikiya, Rehman Sameer, Dunnmon Jared A, Zou James, Rubin Daniel L

2021-Apr-16

General General

LoDoPaB-CT, a benchmark dataset for low-dose computed tomography reconstruction.

In Scientific data

Deep learning approaches for tomographic image reconstruction have become very effective and have been demonstrated to be competitive in the field. Comparing these approaches is a challenging task as they rely to a great extent on the data and setup used for training. With the Low-Dose Parallel Beam (LoDoPaB)-CT dataset, we provide a comprehensive, open-access database of computed tomography images and simulated low photon count measurements. It is suitable for training and comparing deep learning methods as well as classical reconstruction approaches. The dataset contains over 40000 scan slices from around 800 patients selected from the LIDC/IDRI database. The data selection and simulation setup are described in detail, and the generating script is publicly accessible. In addition, we provide a Python library for simplified access to the dataset and an online reconstruction challenge. Furthermore, the dataset can also be used for transfer learning as well as sparse and limited-angle reconstruction scenarios.

Leuschner Johannes, Schmidt Maximilian, Baguer Daniel Otero, Maass Peter

2021-Apr-16

General General

Detecting protein and DNA/RNA structures in cryo-EM maps of intermediate resolution using deep learning.

In Nature communications ; h5-index 260.0

An increasing number of density maps of macromolecular structures, including proteins and DNA/RNA complexes, have been determined by cryo-electron microscopy (cryo-EM). Although lately maps at a near-atomic resolution are routinely reported, there are still substantial fractions of maps determined at intermediate or low resolutions, where extracting structure information is not trivial. Here, we report a new computational method, Emap2sec+, which identifies DNA or RNA as well as the secondary structures of proteins in cryo-EM maps of 5 to 10 Å resolution. Emap2sec+ employs the deep Residual convolutional neural network. Emap2sec+ assigns structural labels with associated probabilities at each voxel in a cryo-EM map, which will help structure modeling in an EM map. Emap2sec+ showed stable and high assignment accuracy for nucleotides in low resolution maps and improved performance for protein secondary structure assignments than its earlier version when tested on simulated and experimental maps.

Wang Xiao, Alnabati Eman, Aderinwale Tunde W, Maddhuri Venkata Subramaniya Sai Raghavendra, Terashi Genki, Kihara Daisuke

2021-Apr-16

General General

Association between renin-angiotensin system and chronic lung allograft dysfunction.

In The European respiratory journal

Chronic lung allograft dysfunction (CLAD) is the major cause of death after lung transplantation. Angiotensin II (AngII), the main effector of the renin-angiotensin (RA) system, elicits fibrosis in both kidney and lung. We identified 6 AngII-regulated proteins (RHOB, BST1, LYPA1, GLNA, TSP1, LAMB1) increased in urine of patients with kidney allograft fibrosis. We hypothesized that RA system is active in CLAD and that AngII-regulated proteins are increased in bronchoalveolar lavage fluid (BAL) of CLAD patients.We performed immunostaining of AngII receptors (AGTR1 and AGTR2) and TSP1/GLNA in 10 CLAD lungs and 5 controls. Using mass spectrometry, we quantified peptides corresponding to AngII-regulated proteins in BAL of 40 lung transplant recipients (CLAD, stable and acute lung allograft dysfunction (ALAD)). Machine learning algorithms were developed to predict CLAD based on BAL peptide concentrations.Immunostaining demonstrated significantly more AGTR1+ cells in CLAD versus control lungs (p=0.02). TSP1 and GLNA immunostaining positively correlated with the degree of lung fibrosis (R2=0.42 and 0.57, respectively). In BAL, we noted a trend toward higher concentrations of AngII-regulated peptides in patients with CLAD at the time of bronchoscopy, and significantly higher concentrations of BST1, GLNA and RHOB peptides in patients that developed CLAD at follow-up (p<0.05). Support vector machine classifier discriminated CLAD from stable and ALAD patients at the time of bronchoscopy with AUC 0.86, and accurately predicted subsequent CLAD development (AUC 0.97).Proteins involved in the RA system are increased in CLAD lung and BAL. AngII-regulated peptides measured in BAL may accurately identify patients with CLAD and predict subsequent CLAD development.

Berra Gregory, Farkona Sofia, Mohammed-Ali Zahraa, Kotlyar Max, Levy Liran, Clotet-Freixas Sergi, Ly Phillip, Renaud-Picard Benjamin, Zehong Guan, Daigneault Tina, Duong Allen, Batruch Ihor, Jurisica Igor, Konvalinka Ana, Martinu Tereza

2021-Apr-16

Surgery Surgery

Survival Analysis Using Surgeon Skill Metrics and Patient Factors to Predict Urinary Continence Recovery After Robot-assisted Radical Prostatectomy.

In European urology focus

BACKGROUND : It has been shown that metrics recorded for instrument kinematics during robotic surgery can predict urinary continence outcomes.

OBJECTIVE : To evaluate the contributions of patient and treatment factors, surgeon efficiency metrics, and surgeon technical skill scores, especially for vesicourethral anastomosis (VUA), to models predicting urinary continence recovery following robot-assisted radical prostatectomy (RARP).

DESIGN, SETTING, AND PARTICIPANTS : Automated performance metrics (APMs; instrument kinematics and system events) and patient data were collected for RARPs performed from July 2016 to December 2017. Robotic Anastomosis Competency Evaluation (RACE) scores during VUA were manually evaluated. Training datasets included: (1) patient factors; (2) summarized APMs (reported over RARP steps); (3) detailed APMs (reported over suturing phases of VUA); and (4) technical skills (RACE). Feature selection was used to compress the dimensionality of the inputs.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS : The study outcome was urinary continence recovery, defined as use of 0 or 1 safety pads per day. Two predictive models (Cox proportional hazards [CoxPH] and deep learning survival analysis [DeepSurv]) were used.

RESULTS AND LIMITATIONS : Of 115 patients undergoing RARP, 89 (77.4%) recovered their urinary continence and the median recovery time was 166 d (interquartile range [IQR] 82-337). VUAs were performed by 23 surgeons. The median RACE score was 28/30 (IQR 27-29). Among the individual datasets, technical skills (RACE) produced the best models (C index: CoxPH 0.695, DeepSurv: 0.708). Among summary APMs, posterior/anterior VUA yielded superior model performance over other RARP steps (C index 0.543-0.592). Among detailed APMs, metrics for needle driving yielded top-performing models (C index 0.614-0.655) over other suturing phases. DeepSurv models consistently outperformed CoxPH; both approaches performed best when provided with all the datasets. Limitations include feature selection, which may have excluded relevant information but prevented overfitting.

CONCLUSIONS : Technical skills and "needle driving" APMs during VUA were most contributory. The best-performing model used synergistic data from all datasets.

PATIENT SUMMARY : One of the steps in robot-assisted surgical removal of the prostate involves joining the bladder to the urethra. Detailed information on surgeon performance for this step improved the accuracy of predicting recovery of urinary continence among men undergoing this operation for prostate cancer.

Trinh Loc, Mingo Samuel, Vanstrum Erik B, Sanford Daniel I, Aastha Ma, Runzhuo Nguyen, Jessica H Liu, Yan Hung

2021-Apr-12

Artificial intelligence, Machine learning, Prostatectomy, Robotics, Survival analysis, Urinary incontinence

General General

Patients at high risk of suicide before and during a COVID-19 lockdown: ecological momentary assessment study.

In BJPsych open

The coronavirus disease 2019 (COVID-19) outbreak may have affected the mental health of patients at high risk of suicide. In this study we explored the wish to die and other suicide risk factors using smartphone-based ecological momentary assessment (EMA) in patients with a history of suicidal thoughts and behaviour. Contrary to our expectations we found a decrease in the wish to die during lockdown. This is consistent with previous studies showing that suicide rates decrease during periods of social emergency. Smartphone-based EMA can allow us to remotely assess patients and overcome the physical barriers imposed by lockdown.

Cobo Aurora, Porras-Segovia Alejandro, Pérez-Rodríguez María Mercedes, Artés-Rodríguez Antonio, Barrigón Maria Luisa, Courtet Philippe, Baca-García Enrique

2021-Apr-16

COVID-19, Suicide, ecological momentary assessment, machine learning, suicide attempt

General General

QSAR-Co-X: an open source toolkit for multitarget QSAR modelling.

In Journal of cheminformatics

Quantitative structure activity relationships (QSAR) modelling is a well-known computational tool, often used in a wide variety of applications. Yet one of the major drawbacks of conventional QSAR modelling is that models are set up based on a limited number of experimental and/or theoretical conditions. To overcome this, the so-called multitasking or multitarget QSAR (mt-QSAR) approaches have emerged as new computational tools able to integrate diverse chemical and biological data into a single model equation, thus extending and improving the reliability of this type of modelling. We have developed QSAR-Co-X, an open source python-based toolkit (available to download at https://github.com/ncordeirfcup/QSAR-Co-X ) for supporting mt-QSAR modelling following the Box-Jenkins moving average approach. The new toolkit embodies several functionalities for dataset selection and curation plus computation of descriptors, for setting up linear and non-linear models, as well as for a comprehensive results analysis. The workflow within this toolkit is guided by a cohort of multiple statistical parameters and graphical outputs onwards assessing both the predictivity and the robustness of the derived mt-QSAR models. To monitor and demonstrate the functionalities of the designed toolkit, four case-studies pertaining to previously reported datasets are examined here. We believe that this new toolkit, along with our previously launched QSAR-Co code, will significantly contribute to make mt-QSAR modelling widely and routinely applicable.

Halder Amit Kumar, Dias Soeiro Cordeiro M Natália

2021-Apr-15

Feature selection, Machine learning, Multitarget models, QSAR, Software tools

General General

Multi-PLI: interpretable multi-task deep learning model for unifying protein-ligand interaction datasets.

In Journal of cheminformatics

The assessment of protein-ligand interactions is critical at early stage of drug discovery. Computational approaches for efficiently predicting such interactions facilitate drug development. Recently, methods based on deep learning, including structure- and sequence-based models, have achieved impressive performance on several different datasets. However, their application still suffers from a generalizability issue because of insufficient data, especially for structure based models, as well as a heterogeneity problem because of different label measurements and varying proteins across datasets. Here, we present an interpretable multi-task model to evaluate protein-ligand interaction (Multi-PLI). The model can run classification (binding or not) and regression (binding affinity) tasks concurrently by unifying different datasets. The model outperforms traditional docking and machine learning on both binary classification and regression tasks and achieves competitive results compared with some structure-based deep learning methods, even with the same training set size. Furthermore, combined with the proposed occlusion algorithm, the model can predict the important amino acids of proteins that are crucial for binding, thus providing a biological interpretation.

Hu Fan, Jiang Jiaxin, Wang Dongqi, Zhu Muchun, Yin Peng

2021-Apr-15

Deep learning, Drug discovery, Interpretable, Multi‐task

General General

Biomimetic detection of dynamic signatures in foliage echoes.

In Bioinspiration & biomimetics

Certain bat species (family Rhinolophidae) dynamically deform their emission baffles (noseleaves) and reception baffles (pinnae) during echolocation. Prior research using numerical models, laboratory characterizations, and experiments with simple targets have suggested that this dynamics may manifest itself in time-variant echo signatures. Since the pronounced random nature of echoes from natural targets such as foliage has not been reflected in these experiments, we have collected a large number (>55,000) of foliage echoes outdoors with a sonar head that mimics the dynamic periphery in bats. The echo data was processed with a custom auditory processing model to create spike-based echo representations. Deep-learning classifiers were able to estimate the dynamic state of the periphery, i.e., static or dynamic, based on single echoes with accuracies of up to 80%. This suggests that the effects of the peripheral dynamics are present in the bat brains and could hence be used by the animals. The best classification performances were obtained for data that was obtained within a spatially confined area. Hence, if the bat brains suffer from the same generalization issues, they would have to have a way to adapt their neural echo processing to such local fluctuations to exploit the dynamic effects successfully.

Bhardwaj Ananya, Khyam Mohammad Omar, Mueller Rolf

2021-Apr-16

bats, biosonar, dynamics, machine learning, sensing, soft robotics

Ophthalmology Ophthalmology

Adopting low-shot deep learning for the detection of conjunctival melanoma using ocular surface images.

In Computer methods and programs in biomedicine

BACKGROUND AND OBJECTIVE : The purpose of the present study was to investigate low-shot deep learning models applied to conjunctival melanoma detection using a small dataset with ocular surface images.

METHODS : A dataset was composed of anonymized images of four classes; conjunctival melanoma (136), nevus or melanosis (93), pterygium (75), and normal conjunctiva (94). Before training involving conventional deep learning models, two generative adversarial networks (GANs) were constructed to augment the training dataset for low-shot learning. The collected data were randomly divided into training (70%), validation (10%), and test (20%) datasets. Moreover, 3D melanoma phantoms were designed to build an external validation set using a smartphone. The GoogleNet, InceptionV3, NASNet, ResNet50, and MobileNetV2 architectures were trained through transfer learning and validated using the test and external validation datasets.

RESULTS : The deep learning model demonstrated a significant improvement in the classification accuracy of conjunctival lesions using synthetic images generated by the GAN models. MobileNetV2 with GAN-based augmentation displayed the highest accuracy of 87.5% in the four-class classification and 97.2% in the binary classification for the detection of conjunctival melanoma. It showed an accuracy of 94.0% using 3D melanoma phantom images captured using a smartphone camera.

CONCLUSIONS : The present study described a low-shot deep learning model that can detect conjunctival melanomas using ocular surface images. To the best of our knowledge, this study is the first to develop a deep learning model to detect conjunctival melanoma using a digital imaging device such as smartphone camera.

Yoo Tae Keun, Choi Joon Yul, Kim Hong Kyu, Ryu Ik Hee, Kim Jin Kuk

2021-Apr-03

Conjunctival melanoma, Conjunctival nevus, Deep learning, Low-shot learning, Melanosis

Radiology Radiology

Cystic cervical lymph nodes of papillary thyroid carcinoma, tuberculosis and human papillomavirus positive oropharyngeal squamous cell carcinoma: utility of deep learning in their differentiation on CT.

In American journal of otolaryngology ; h5-index 23.0

OBJECTIVES : Cervical lymph nodes with internal cystic changes are seen with several pathologies, including papillary thyroid carcinoma (PTC), tuberculosis (TB), and HPV-positive oropharyngeal squamous cell carcinoma (HPV+OPSCC). Differentiating these lymph nodes is difficult in the absence of a known primary tumor or reliable medical history. In this study, we assessed the utility of deep learning in differentiating the pathologic lymph nodes of PTC, TB, and HPV+OPSCC on CT.

METHODS : A total of 173 lymph nodes (55 PTC, 58 TB, and 60 HPV+OPSCC) were selected based on pathology records and suspicious morphological features. These lymph nodes were divided into the training set (n = 131) and the test set (n = 42). In deep learning analysis, JPEG lymph node images were extracted from the CT slice that included the largest area of each node and fed into a deep learning training session to create a diagnostic model. Transfer learning was used with the deep learning model architecture of ResNet-101. Using the test set, the diagnostic performance of the deep learning model was compared against the histopathological diagnosis and to the diagnostic performances of two board-certified neuroradiologists.

RESULTS : Diagnostic accuracy of the deep learning model was 0.76 (=32/42), whereas those of Radiologist 1 and Radiologist 2 were 0.48 (=20/42) and 0.41 (=17/42), respectively. Deep learning derived diagnostic accuracy was significantly higher than both of the two neuroradiologists (P < 0.01, respectively).

CONCLUSION : Deep learning algorithm holds promise to become a useful diagnostic support tool in interpreting cervical lymphadenopathy.

Onoue Keita, Fujima Noriyuki, Andreu-Arasa V Carlota, Setty Bindu N, Sakai Osamu

2021-Apr-09

Cervical lymphadenopathy, Computed tomography, Deep learning, Human papillomavirus, Machine learning, Papillary thyroid carcinoma, Tuberculosis

General General

Modeling patient-related workload in the emergency department using electronic health record data.

In International journal of medical informatics ; h5-index 49.0

INTRODUCTION : Understanding and managing clinician workload is important for clinician (nurses, physicians and advanced practice providers) occupational health as well as patient safety. Efforts have been made to develop strategies for managing clinician workload by improving patient assignment. The goal of the current study is to use electronic health record (EHR) data to predict the amount of work that individual patients contribute to clinician workload (patient-related workload).

METHODS : One month of EHR data was retrieved from an emergency department (ED). A list of workload indicators and five potential workload proxies were extracted from the data. Linear regression and four machine learning classification algorithms were utilized to model the relationship between the indicators and the proxies.

RESULTS : Linear regression proved that the indicators explained a substantial amount of variance of the proxies (four out of five proxies were modeled with R2 > 0.80). Classification algorithms also showed success in classifying a patient as having high or low task demand based on data from early in the ED visit (e.g. 80 % accurate binary classification with data from the first hour).

CONCLUSION : The main contribution of this study is demonstrating the potential of using EHR data to predict patient-related workload automatically in the ED. The predicted workload can potentially help in managing clinician workload by supporting decisions around the assignment of new patients to providers. Future work should focus on identifying the relationship between workload proxies and actual workload, as well as improving prediction performance of regression and multi-class classification.

Wang Xiaomei, Blumenthal H Joseph, Hoffman Daniel, Benda Natalie, Kim Tracy, Perry Shawna, Franklin Ella S, Roth Emilie M, Hettinger A Zachary, Bisantz Ann M

2021-Apr-09

Electronic health record, Emergency department, Machine learning, Workload

General General

Trends in sensor development toward next-generation point-of-care testing for mercury.

In Biosensors & bioelectronics

Mercury is one of the most common heavy metals and a major environmental pollutant that affects ecosystems. Since mercury and its compounds are toxic to humans, even at low concentrations, it is very important to monitor mercury contamination in water and foods. Although conventional mercury detection methods, including inductively coupled plasma mass spectrometry, atomic absorption spectroscopy, and gas chromatography-mass spectrometry, exhibit excellent sensitivity and accuracy, they require operation by an expert in a sophisticated and fully controlled laboratory environment. To overcome these limitations and realize point-of-care testing, many novel methods for direct sample analysis in the field have recently been developed by improving the speed and simplicity of detection. Commonly, these unconventional sensors rely on colorimetric, fluorescence, or electrochemical mechanisms to transduce signals from mercury. In the case of colorimetric and fluorescent sensors, benchtop methods have gradually evolved through technology convergence to give standalone platforms, such as paper-based assays and lab-on-a-chip systems, and portable measurement devices, such as smartphones. Electrochemical sensors that use screen-printed electrodes with carbon or metal nanomaterials or hybrid materials to improve sensitivity and stability also provide promising detection platforms. This review summarizes the current state of sensor platforms for the on-field detection of mercury with a focus on key features and recent developments. Furthermore, trends for next-generation mercury sensors are suggested based on a paradigm shift to the active integration of cutting-edge technologies, such as drones, systems based on artificial intelligence, machine learning, and three-dimensional printing, and high-quality smartphones.

Lim Ji Won, Kim Tai-Yong, Woo Min-Ah

2021-Apr-07

Colorimetry, Electrochemistry, Field detection, Fluorescence, Mercury, Sensor platform

General General

Hyperspectral monitor of soil chromium contaminant based on deep learning network model in the Eastern Junggar coalfield.

In Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

In China, over 10% of cultivated land is polluted by heavy metals, which can affect crop growth, food safety and human health. Therefore, how to effectively and quickly detect soil heavy metal pollution has become a critical issue. This study provides a novel data preprocessing method that can extract vital information from soil hyperspectra and uses different classification algorithms to detect levels of heavy metal contamination in soil. In this experiment, 160 soil samples from the Eastern Junggar Coalfield in Xinjiang were employed for verification, including 143 noncontaminated samples and 17 contaminated soil samples. Because the concentration of chromium in the soil exists in trace amounts, combined with the fact that spectral characteristics are easily influenced by other types of impurity in the soil, the evaluation of chromium concentrations in the soil through hyperspectral analysis is not satisfactory. To avoid this phenomenon, the pretreatment method of this experiment includes a combination of second derivative and data enhancement (DA) approaches. Then, support vector machine (SVM), k-nearest neighbour (KNN) and deep neural network (DNN) algorithms are used to create the discriminant models. The accuracies of the DA-SVM, DA-KNN and DA-DNN models were 95.61%, 95.62% and 96.25%, respectively. The results of this experiment demonstrate that soil hyperspectral technology combined with deep learning can be used to instantly monitor soil chromium pollution levels on a large scale. This research can be used for the management of polluted areas and agricultural insurance applications.

Wang Yuan, Ma Hongbing, Wang Jingzhe, Liu Li, Pietikäinen Matti, Zhang Zipeng, Chen Xiangyue

2021-Mar-26

Data enhancement (DA), Deep neural network (DNN), Soil heavy metal pollution, Soil hyperspectrum, Support vector machine (SVM), k-nearest neighbour (KNN)

Radiology Radiology

A predictive framework based on brain volume trajectories enabling early detection of Alzheimer's disease.

In Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

We present a framework for constructing predictive models of cognitive decline from longitudinal MRI examinations, based on mixed effects models and machine learning. We apply the framework to detect conversion from cognitively normal (CN) to mild cognitive impairment (MCI) and from MCI to Alzheimer's disease (AD), using a large collection of subjects sourced from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Australian Imaging, Biomarkers and Lifestyle Flagship Study of Aging (AIBL). We extract subcortical segmentation and cortical parcellation from corresponding T1-weighted images using FreeSurfer v.6.0, select bilateral 3D regions of interest relevant to neurodegeneration/dementia, and fit their longitudinal volume trajectories using linear mixed effects models. Features describing these model-based trajectories are then used to train an ensemble of machine learning classifiers to distinguish stable CN from converters to MCI, and stable MCI from converters to AD. On separate test sets the models achieved an average of accuracy/precision/recall score of 69/73/60% for converted to MCI and 75/74/77% for converted to AD, illustrating the framework's ability to extract predictive imaging-based biomarkers from routine T1-weighted MRI acquisitions.

Mofrad Samaneh Abolpour, Lundervold Arvid, Lundervold Alexander Selvikvåg

2021-Apr-02

“Alzheimers disease”, Longitudinal data analysis, MRI, Machine learning, Mild cognitive impairment, Mixed effects models

Public Health Public Health

BS-Net: Learning COVID-19 pneumonia severity on a large chest X-ray dataset.

In Medical image analysis

In this work we design an end-to-end deep learning architecture for predicting, on Chest X-rays images (CXR), a multi-regional score conveying the degree of lung compromise in COVID-19 patients. Such semi-quantitative scoring system, namely Brixia score, is applied in serial monitoring of such patients, showing significant prognostic value, in one of the hospitals that experienced one of the highest pandemic peaks in Italy. To solve such a challenging visual task, we adopt a weakly supervised learning strategy structured to handle different tasks (segmentation, spatial alignment, and score estimation) trained with a "from-the-part-to-the-whole" procedure involving different datasets. In particular, we exploit a clinical dataset of almost 5,000 CXR annotated images collected in the same hospital. Our BS-Net demonstrates self-attentive behavior and a high degree of accuracy in all processing stages. Through inter-rater agreement tests and a gold standard comparison, we show that our solution outperforms single human annotators in rating accuracy and consistency, thus supporting the possibility of using this tool in contexts of computer-assisted monitoring. Highly resolved (super-pixel level) explainability maps are also generated, with an original technique, to visually help the understanding of the network activity on the lung areas. We also consider other scores proposed in literature and provide a comparison with a recently proposed non-specific approach. We eventually test the performance robustness of our model on an assorted public COVID-19 dataset, for which we also provide Brixia score annotations, observing good direct generalization and fine-tuning capabilities that highlight the portability of BS-Net in other clinical settings. The CXR dataset along with the source code and the trained model are publicly released for research purposes.

Signoroni Alberto, Savardi Mattia, Benini Sergio, Adami Nicola, Leonardi Riccardo, Gibellini Paolo, Vaccher Filippo, Ravanelli Marco, Borghesi Andrea, Maroldi Roberto, Farina Davide

2021-Mar-31

COVID-19 severity assessment, Chest X-rays, Convolutional neural networks, End-to-end learning, Semi-quantitative rating

Public Health Public Health

Machine learning and statistical approaches for classification of risk of coronary artery disease using plasma cytokines.

In BioData mining

BACKGROUND : As per the 2017 WHO fact sheet, Coronary Artery Disease (CAD) is the primary cause of death in the world, and accounts for 31% of total fatalities. The unprecedented 17.6 million deaths caused by CAD in 2016 underscores the urgent need to facilitate proactive and accelerated pre-emptive diagnosis. The innovative and emerging Machine Learning (ML) techniques can be leveraged to facilitate early detection of CAD which is a crucial factor in saving lives. The standard techniques like angiography, that provide reliable evidence are invasive and typically expensive and risky. In contrast, ML model generated diagnosis is non-invasive, fast, accurate and affordable. Therefore, ML algorithms can be used as a supplement or precursor to the conventional methods. This research demonstrates the implementation and comparative analysis of K Nearest Neighbor (k-NN) and Random Forest ML algorithms to achieve a targeted "At Risk" CAD classification using an emerging set of 35 cytokine biomarkers that are strongly indicative predictive variables that can be potential targets for therapy. To ensure better generalizability, mechanisms such as data balancing, repeated k-fold cross validation for hyperparameter tuning, were integrated within the models. To determine the separability efficacy of "At Risk" CAD versus Control achieved by the models, Area under Receiver Operating Characteristic (AUROC) metric is used which discriminates the classes by exhibiting tradeoff between the false positive and true positive rates.

RESULTS : A total of 2 classifiers were developed, both built using 35 cytokine predictive features. The best AUROC score of .99 with a 95% Confidence Interval (CI) (.982,.999) was achieved by the Random Forest classifier using 35 cytokine biomarkers. The second-best AUROC score of .954 with a 95% Confidence Interval (.929,.979) was achieved by the k-NN model using 35 cytokines. A p-value of less than 7.481e-10 obtained by an independent t-test validated that Random Forest classifier was significantly better than the k-NN classifier with regards to the AUROC score. Presently, as large-scale efforts are gaining momentum to enable early, fast, reliable, affordable, and accessible detection of individuals at risk for CAD, the application of powerful ML algorithms can be leveraged as a supplement to conventional methods such as angiography. Early detection can be further improved by incorporating 65 novel and sensitive cytokine biomarkers. Investigation of the emerging role of cytokines in CAD can materially enhance the detection of risk and the discovery of mechanisms of disease that can lead to new therapeutic modalities.

Saharan Seema Singh, Nagar Pankaj, Creasy Kate Townsend, Stock Eveline O, Feng James, Malloy Mary J, Kane John P

2021-Apr-15

AUROC, CAD, Classification, Confidence interval, Distance metrics, ML, Plasma cytokines, ROSE, Random Forest, k-NN, k-fold cross validation

General General

Multimodal brain predictors of current weight and weight gain in children enrolled in the ABCD study ®.

In Developmental cognitive neuroscience ; h5-index 46.0

Multimodal neuroimaging assessments were utilized to identify generalizable brain correlates of current body mass index (BMI) and predictors of pathological weight gain (i.e., beyond normative development) one year later. Multimodal data from children enrolled in the Adolescent Brain Cognitive Development Study® at 9-to-10-years-old, consisted of structural magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), resting state (rs), and three task-based functional (f) MRI scans assessing reward processing, inhibitory control, and working memory. Cross-validated elastic-net regression revealed widespread structural associations with BMI (e.g., cortical thickness, surface area, subcortical volume, and DTI), which explained 35% of the variance in the training set and generalized well to the test set (R2 = 0.27). Widespread rsfMRI inter- and intra-network correlations were related to BMI (R2train = 0.21; R2test = 0.14), as were regional activations on the working memory task (R2train = 0.20; (R2test = 0.16). However, reward and inhibitory control tasks were unrelated to BMI. Further, pathological weight gain was predicted by structural features (Area Under the Curve (AUC)train = 0.83; AUCtest = 0.83, p < 0.001), but not by fMRI nor rsfMRI. These results establish generalizable brain correlates of current weight and future pathological weight gain. These results also suggest that sMRI may have particular value for identifying children at risk for pathological weight gain.

Adise Shana, Allgaier Nicholas, Laurent Jennifer, Hahn Sage, Chaarani Bader, Owens Max, Yuan DeKang, Nyugen Philip, Mackey Scott, Potter Alexandra, Garavan Hugh P

2021-Mar-30

Childhood obesity, Inhibitory control, Machine-learning, Reward, Weight gain, Weight stability, fMRI

General General

Facial emotions are accurately encoded in the neural signal of those with Autism Spectrum Disorder: A deep learning approach.

In Biological psychiatry. Cognitive neuroscience and neuroimaging

BACKGROUND : Individuals with autism spectrum disorder (ASD) exhibit frequent behavioral deficits in facial emotion recognition (FER). It remains unknown whether these deficits arise because facial emotion information is not encoded in their neural signal, or because it is encoded, but fails to translate to FER behavior (deployment). This distinction has functional implications, including constraining when differences in social information processing occur in ASD, and guiding interventions (i.e., developing prosthetic FER-vs.-reinforcing existing skills).

METHODS : We utilized a discriminative and contemporary machine learning approach - Deep Convolutional Neural Networks (CNN) - to classify facial emotions viewed by individuals with and without ASD(N= 88) from concurrently-recorded electroencephalography signals.

RESULTS : The CNN classified facial emotions with high accuracy for both ASD and non-ASD groups, even though individuals with ASD performed more poorly on the concurrent FER task. In fact, CNN accuracy was greater in the ASD group, and was not related to behavioral performance. This pattern of results replicated across three independent participant samples. Moreover, feature-importance analyses suggest that a late temporal window of neural activity (1000-1500ms) may be uniquely important in facial emotion classification for individuals for ASD.

CONCLUSIONS : Our results reveal for the first time that facial emotion information is encoded in the neural signal of individuals with (and without) ASD. Thus, observed difficulties in behavioral FER associated with ASD likely arise from difficulties in decoding or deployment of facial emotion information within the neural signal. Interventions should focus on capitalizing on this intact encoding rather than promoting compensation or FER prosthetics.

Mayor Torres Juan Manuel, Clarkson Tessa, Hauschild Kathryn M, Luhmann Christian C, Lerner Matthew D, Riccardi Giuseppe

2021-Apr-13

Autism Spectrum Disorder, Deep Convolutional Neural Networks, Electroencephalography, Facial Emotion Recognition

General General

Simulation-Derived Best Practices for Clustering Clinical Data.

In Journal of biomedical informatics ; h5-index 55.0

INTRODUCTION : Clustering analyses in clinical contexts hold promise to improve the understanding of patient phenotype and disease course in chronic and acute clinical medicine. However, work remains to ensure that solutions are rigorous, valid, and reproducible. In this paper, we evaluate best practices for dissimilarity matrix calculation and clustering on mixed-type, clinical data.

METHODS : We simulate clinical data to represent problems in clinical trials, cohort studies, and EHR data, including single-type datasets (binary, continuous, categorical) and 4 data mixtures. We test 5 single distance metrics (Jaccard, Hamming, Gower, Manhattan, Euclidean) and 3 mixed distance metrics (DAISY, Supersom, and Mercator) with 3 clustering algorithms (hierarchical (HC), k-medoids, self-organizing maps (SOM)). We quantitatively and visually validate by Adjusted Rand Index (ARI) and silhouette width (SW).

RESULTS : HC outperformed k-medoids and SOM by ARI across data types. DAISY produced the highest mean ARI for mixed data types for all mixtures except unbalanced mixtures dominated by continuous data. Compared to the Hamming distance, a real data application of DAISY with HC uncovered superior, separable clusters.

DISCUSSION : Selecting an appropriate mixed-type metric allows the investigator to obtain optimal separation of patient clusters and get maximum use of their data. Superior metrics for mixed-type data handle multiple data types using multiple, type-focused distances. Better subclassification of disease opens avenues for targeted treatments, precision medicine, clinical decision support, and improved patient outcomes.

Coombes Caitlin E, Abrams Zachary B, Coombes Kevin R, Brock Guy

2021-Apr-13

Clustering, clinical informatics, clinical trial, electronic health record, unsupervised machine learning

General General

Toward a fine-scale population health monitoring system.

In Cell ; h5-index 250.0

Understanding population health disparities is an essential component of equitable precision health efforts. Epidemiology research often relies on definitions of race and ethnicity, but these population labels may not adequately capture disease burdens and environmental factors impacting specific sub-populations. Here, we propose a framework for repurposing data from electronic health records (EHRs) in concert with genomic data to explore the demographic ties that can impact disease burdens. Using data from a diverse biobank in New York City, we identified 17 communities sharing recent genetic ancestry. We observed 1,177 health outcomes that were statistically associated with a specific group and demonstrated significant differences in the segregation of genetic variants contributing to Mendelian diseases. We also demonstrated that fine-scale population structure can impact the prediction of complex disease risk within groups. This work reinforces the utility of linking genomic data to EHRs and provides a framework toward fine-scale monitoring of population health.

Belbin Gillian M, Cullina Sinead, Wenric Stephane, Soper Emily R, Glicksberg Benjamin S, Torre Denis, Moscati Arden, Wojcik Genevieve L, Shemirani Ruhollah, Beckmann Noam D, Cohain Ariella, Sorokin Elena P, Park Danny S, Ambite Jose-Luis, Ellis Steve, Auton Adam, Bottinger Erwin P, Cho Judy H, Loos Ruth J F, Abul-Husn Noura S, Zaitlen Noah A, Gignoux Christopher R, Kenny Eimear E

2021-Apr-15

biobanks, computational genomics, electronic health records, genetic ancestry, genomic medicine, health disparities, machine learning, population health

Surgery Surgery

Predicting breast cancer 5-year survival using machine learning: A systematic review.

In PloS one ; h5-index 176.0

BACKGROUND : Accurately predicting the survival rate of breast cancer patients is a major issue for cancer researchers. Machine learning (ML) has attracted much attention with the hope that it could provide accurate results, but its modeling methods and prediction performance remain controversial. The aim of this systematic review is to identify and critically appraise current studies regarding the application of ML in predicting the 5-year survival rate of breast cancer.

METHODS : In accordance with the PRISMA guidelines, two researchers independently searched the PubMed (including MEDLINE), Embase, and Web of Science Core databases from inception to November 30, 2020. The search terms included breast neoplasms, survival, machine learning, and specific algorithm names. The included studies related to the use of ML to build a breast cancer survival prediction model and model performance that can be measured with the value of said verification results. The excluded studies in which the modeling process were not explained clearly and had incomplete information. The extracted information included literature information, database information, data preparation and modeling process information, model construction and performance evaluation information, and candidate predictor information.

RESULTS : Thirty-one studies that met the inclusion criteria were included, most of which were published after 2013. The most frequently used ML methods were decision trees (19 studies, 61.3%), artificial neural networks (18 studies, 58.1%), support vector machines (16 studies, 51.6%), and ensemble learning (10 studies, 32.3%). The median sample size was 37256 (range 200 to 659820) patients, and the median predictor was 16 (range 3 to 625). The accuracy of 29 studies ranged from 0.510 to 0.971. The sensitivity of 25 studies ranged from 0.037 to 1. The specificity of 24 studies ranged from 0.008 to 0.993. The AUC of 20 studies ranged from 0.500 to 0.972. The precision of 6 studies ranged from 0.549 to 1. All of the models were internally validated, and only one was externally validated.

CONCLUSIONS : Overall, compared with traditional statistical methods, the performance of ML models does not necessarily show any improvement, and this area of research still faces limitations related to a lack of data preprocessing steps, the excessive differences of sample feature selection, and issues related to validation. Further optimization of the performance of the proposed model is also needed in the future, which requires more standardization and subsequent validation.

Li Jiaxin, Zhou Zijun, Dong Jianyu, Fu Ying, Li Yuan, Luan Ze, Peng Xin

2021

Pathology Pathology

Emulative, coherent, and causal dynamics between large-scale brain networks are neurobiomarkers of Accelerated Cognitive Ageing in epilepsy.

In PloS one ; h5-index 176.0

Accelerated cognitive ageing (ACA) is an ageing co-morbidity in epilepsy that is diagnosed through the observation of an evident IQ decline of more than 1 standard deviation (15 points) around the age of 50 years old. To understand the mechanism of action of this pathology, we assessed brain dynamics with the use of resting-state fMRI data. In this paper, we present novel and promising methods to extract brain dynamics between large-scale resting-state networks: the emulative power, wavelet coherence, and granger causality between the networks were extracted in two resting-state sessions of 24 participants (10 ACA, 14 controls). We also calculated the widely used static functional connectivity to compare the methods. To find the best biomarkers of ACA, and have a better understanding of this epilepsy co-morbidity we compared the aforementioned between-network neurodynamics using classifiers and known machine learning algorithms; and assessed their performance. Results show that features based on the evolutionary game theory on networks approach, the emulative powers, are the best descriptors of the co-morbidity, using dynamics associated with the default mode and dorsal attention networks. With these dynamic markers, linear discriminant analysis could identify ACA patients at 82.9% accuracy. Using wavelet coherence features with decision-tree algorithm, and static functional connectivity features with support vector machine, ACA could be identified at 77.1% and 77.9% accuracy respectively. Granger causality fell short of being a relevant biomarker with best classifiers having an average accuracy of 67.9%. Combining the features based on the game theory, wavelet coherence, Granger-causality, and static functional connectivity- approaches increased the classification performance up to 90.0% average accuracy using support vector machine with a peak accuracy of 95.8%. The dynamics of the networks that lead to the best classifier performances are known to be challenged in elderly. Since our groups were age-matched, the results are in line with the idea of ACA patients having an accelerated cognitive decline. This classification pipeline is promising and could help to diagnose other neuropsychiatric disorders, and contribute to the field of psychoradiology.

Bernas Antoine, Breuer Lisanne E M, Aldenkamp Albert P, Zinger Svitlana

2021

General General

Automatic image annotation for fluorescent cell nuclei segmentation.

In PloS one ; h5-index 176.0

Dataset annotation is a time and labor-intensive task and an integral requirement for training and testing deep learning models. The segmentation of images in life science microscopy requires annotated image datasets for object detection tasks such as instance segmentation. Although the amount of annotated image data has been steadily reduced due to methods such as data augmentation, the process of manual or semi-automated data annotation is the most labor and cost intensive task in the process of cell nuclei segmentation with deep neural networks. In this work we propose a system to fully automate the annotation process of a custom fluorescent cell nuclei image dataset. By that we are able to reduce nuclei labelling time by up to 99.5%. The output of our system provides high quality training data for machine learning applications to identify the position of cell nuclei in microscopy images. Our experiments have shown that the automatically annotated dataset provides coequal segmentation performance compared to manual data annotation. In addition, we show that our system enables a single workflow from raw data input to desired nuclei segmentation and tracking results without relying on pre-trained models or third-party training datasets for neural networks.

Englbrecht Fabian, Ruider Iris E, Bausch Andreas R

2021

Ophthalmology Ophthalmology

Predicting eyes at risk for rapid glaucoma progression based on an initial visual field test using machine learning.

In PloS one ; h5-index 176.0

OBJECTIVE : To assess whether machine learning algorithms (MLA) can predict eyes that will undergo rapid glaucoma progression based on an initial visual field (VF) test.

DESIGN : Retrospective analysis of longitudinal data.

SUBJECTS : 175,786 VFs (22,925 initial VFs) from 14,217 patients who completed ≥5 reliable VFs at academic glaucoma centers were included.

METHODS : Summary measures and reliability metrics from the initial VF and age were used to train MLA designed to predict the likelihood of rapid progression. Additionally, the neural network model was trained with point-wise threshold data in addition to summary measures, reliability metrics and age. 80% of eyes were used for a training set and 20% were used as a test set. MLA test set performance was assessed using the area under the receiver operating curve (AUC). Performance of models trained on initial VF data alone was compared to performance of models trained on data from the first two VFs.

MAIN OUTCOME MEASURES : Accuracy in predicting future rapid progression defined as MD worsening more than 1 dB/year.

RESULTS : 1,968 eyes (8.6%) underwent rapid progression. The support vector machine model (AUC 0.72 [95% CI 0.70-0.75]) most accurately predicted rapid progression when trained on initial VF data. Artificial neural network, random forest, logistic regression and naïve Bayes classifiers produced AUC of 0.72, 0.70, 0.69, 0.68 respectively. Models trained on data from the first two VFs performed no better than top models trained on the initial VF alone. Based on the odds ratio (OR) from logistic regression and variable importance plots from the random forest model, older age (OR: 1.41 per 10 year increment [95% CI: 1.34 to 1.08]) and higher pattern standard deviation (OR: 1.31 per 5-dB increment [95% CI: 1.18 to 1.46]) were the variables in the initial VF most strongly associated with rapid progression.

CONCLUSIONS : MLA can be used to predict eyes at risk for rapid progression with modest accuracy based on an initial VF test. Incorporating additional clinical data to the current model may offer opportunities to predict patients most likely to rapidly progress with even greater accuracy.

Shuldiner Scott R, Boland Michael V, Ramulu Pradeep Y, De Moraes C Gustavo, Elze Tobias, Myers Jonathan, Pasquale Louis, Wellik Sarah, Yohannan Jithin

2021

General General

A framework model using multifilter feature selection to enhance colon cancer classification.

In PloS one ; h5-index 176.0

Gene expression profiles can be utilized in the diagnosis of critical diseases such as cancer. The selection of biomarker genes from these profiles is significant and crucial for cancer detection. This paper presents a framework proposing a two-stage multifilter hybrid model of feature selection for colon cancer classification. Colon cancer is being extremely common nowadays among other types of cancer. There is a need to find fast and an accurate method to detect the tissues, and enhance the diagnostic process and the drug discovery. This paper reports on a study whose objective has been to improve the diagnosis of cancer of the colon through a two-stage, multifilter model of feature selection. The model described deals with feature selection using a combination of Information Gain and a Genetic Algorithm. The next stage is to filter and rank the genes identified through this method using the minimum Redundancy Maximum Relevance (mRMR) technique. The final phase is to further analyze the data using correlated machine learning algorithms. This two-stage approach, which involves the selection of genes before classification techniques are used, improves success rates for the identification of cancer cells. It is found that Decision Tree, K-Nearest Neighbor, and Naïve Bayes classifiers had showed promising accurate results using the developed hybrid framework model. It is concluded that the performance of our proposed method has achieved a higher accuracy in comparison with the existing methods reported in the literatures. This study can be used as a clue to enhance treatment and drug discovery for the colon cancer cure.

Al-Rajab Murad, Lu Joan, Xu Qiang

2021

General General

Genome-wide analysis of lncRNA stability in human.

In PLoS computational biology

Transcript stability is associated with many biological processes, and the factors affecting mRNA stability have been extensively studied. However, little is known about the features related to human long noncoding RNA (lncRNA) stability. By inhibiting transcription and collecting samples in 10 time points, genome-wide RNA-seq studies was performed in human lung adenocarcinoma cells (A549) and RNA half-life datasets were constructed. The following observations were obtained. First, the half-life distributions of both lncRNAs and messanger RNAs (mRNAs) with one exon (lnc-human1 and m-human1) were significantly different from those of both lncRNAs and mRNAs with more than one exon (lnc-human2 and m-human2). Furthermore, some factors such as full-length transcript secondary structures played a contrary role in lnc-human1 and m-human2. Second, through the half-life comparisons of nucleus- and cytoplasm-specific and common lncRNAs and mRNAs, lncRNAs (mRNAs) in the nucleus were found to be less stable than those in the cytoplasm, which was derived from transcripts themselves rather than cellular location. Third, kmers-based protein-RNA or RNA-RNA interactions promoted lncRNA stability from lnc-human1 and decreased mRNA stability from m-human2 with high probability. Finally, through applying deep learning-based regression, a non-linear relationship was found to exist between the half-lives of lncRNAs (mRNAs) and related factors. The present study established lncRNA and mRNA half-life regulation networks in the A549 cell line and shed new light on the degradation behaviors of both lncRNAs and mRNAs.

Shi Kaiwen, Liu Tao, Fu Hanjiang, Li Wuju, Zheng Xiaofei

2021-Apr-16

General General

Semantic categorization of Chinese eligibility criteria in clinical trials using machine learning methods.

In BMC medical informatics and decision making ; h5-index 38.0

BACKGROUND : Semantic categorization analysis of clinical trials eligibility criteria based on natural language processing technology is crucial for the task of optimizing clinical trials design and building automated patient recruitment system. However, most of related researches focused on English eligibility criteria, and to the best of our knowledge, there are no researches studied the Chinese eligibility criteria. Thus in this study, we aimed to explore the semantic categories of Chinese eligibility criteria.

METHODS : We downloaded the clinical trials registration files from the website of Chinese Clinical Trial Registry (ChiCTR) and extracted both the Chinese eligibility criteria and corresponding English eligibility criteria. We represented the criteria sentences based on the Unified Medical Language System semantic types and conducted the hierarchical clustering algorithm for the induction of semantic categories. Furthermore, in order to explore the classification performance of Chinese eligibility criteria with our developed semantic categories, we implemented multiple classification algorithms, include four baseline machine learning algorithms (LR, NB, kNN, SVM), three deep learning algorithms (CNN, RNN, FastText) and two pre-trained language models (BERT, ERNIE).

RESULTS : We totally developed 44 types of semantic categories, summarized 8 topic groups, and investigated the average incidence and prevalence in 272 hepatocellular carcinoma related Chinese clinical trials. Compared with the previous proposed categories in English eligibility criteria, 13 novel categories are identified in Chinese eligibility criteria. The classification result shows that most of semantic categories performed quite well, the pre-trained language model ERNIE achieved best performance with macro-average F1 score of 0.7980 and micro-average F1 score of 0.8484.

CONCLUSION : As a pilot study of Chinese eligibility criteria analysis, we developed the 44 semantic categories by hierarchical clustering algorithms for the first times, and validated the classification capacity with multiple classification algorithms.

Zong Hui, Yang Jinxuan, Zhang Zeyu, Li Zuofeng, Zhang Xiaoyan

2021-Apr-15

Classification, Clinical trials, Clustering, Eligibility criteria, Semantic category

General General

Knowledge-Based Prediction of Network Controllability Robustness.

In IEEE transactions on neural networks and learning systems

Network controllability robustness (CR) reflects how well a networked system can maintain its controllability against destructive attacks. Its measure is quantified by a sequence of values that record the remaining controllability of the network after a sequence of node-removal or edge-removal attacks. Traditionally, the CR is determined by attack simulations, which is computationally time-consuming or even infeasible. In this article, an improved method for predicting the network CR is developed based on machine learning using a group of convolutional neural networks (CNNs). In this scheme, a number of training data generated by simulations are used to train the group of CNNs for classification and prediction, respectively. Extensive experimental studies are carried out, which demonstrate that 1) the proposed method predicts more precisely than the classical single-CNN predictor; 2) the proposed CNN-based predictor provides a better predictive measure than the traditional spectral measures and network heterogeneity.

Lou Yang, He Yaodong, Wang Lin, Tsang Kim Fung, Chen Guanrong

2021-Apr-16

General General

Evaluating User and Machine Learning in Short-and Long-term Pattern Recognition-based Myoelectric Control.

In IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

Proper training is essential to achieve reliable pattern recognition (PR) based myoelectric control. The amount of training is commonly determined by experience. The purpose of this study is to provide an offline validation method that makes the offline performance transferable to online control and find the proper amount of training that achieves good online performance. In the offline experiment, eight able-bodied subjects and three amputees participated in a ten-day training. Repeatability index (RI) and classification error (CE) were used to evaluate user learning and machine learning, respectively. The performance of cross-validation (CV) and time serial related validation (TSV) was compared. Learning curves were established with different training trials by TSV. In the online experiment, sixteen able-bodied subjects were randomly divided into two groups with one- or five-trial training, respectively, followed by participating in the test with and without classifier-output feedback. The correlation between offline and online tests was analyzed. Results indicated that five-trial training was proper to train the user and the classifier. The long-term retention of skills could not shorten the learning process. The correlation between CEs of TSV and the online test was strong (r = 0.87) with five-trial training, while the correlation between CEs of CV and the online test was weak (r = 0.30). Outcomes demonstrate that offline performance evaluated by TSV is transferable to online performance and the learning process can guide the user to achieve good online myoelectric control with minimum training.

Lv Bo, Chai Guohong, Sheng Xinjun, Ding Han, Zhu Xiangyang

2021-Apr-16

General General

Revisiting Light Field Rendering with Deep Anti-Aliasing Neural Network.

In IEEE transactions on pattern analysis and machine intelligence ; h5-index 127.0

The light field (LF) reconstruction is mainly confronted with two challenges, large disparity and non-Lambertian effect. Typical approaches either address the large disparity challenge using depth estimation followed by view synthesis or eschew explicit depth information to enable non-Lambertian rendering, but rarely solve both challenges in a unified framework. In this paper, we revisit the classic LF rendering framework to address both challenges by incorporating it with deep learning techniques. First, we analytically show that the essential issue behind the large disparity and non-Lambertian challenges is the aliasing problem. Classic LF rendering approaches typically mitigate the aliasing with a reconstruction filter in the Fourier domain, which is, however, intractable to implement within a deep learning pipeline. Instead, we introduce an alternative framework to perform anti-aliasing reconstruction in the image domain and analytically show the comparable efficacy on the aliasing issue. To explore the full potential, we then embed the anti-aliasing framework into a deep neural network through the design of an integrated architecture and trainable parameters. The network is trained through end-to-end optimization using a peculiar training set, including regular LFs and unstructured LFs. The proposed pipeline shows superiority on solving both the large disparity and the non-Lambertian challenges.

Wu Gaochang, Liu Yebin, Fang Lu, Chai Tianyou

2021-Apr-16

Radiology Radiology

Interventional Radiology ex-machina: impact of Artificial Intelligence on practice.

In La Radiologia medica

Artificial intelligence (AI) is a branch of Informatics that uses algorithms to tirelessly process data, understand its meaning and provide the desired outcome, continuously redefining its logic. AI was mainly introduced via artificial neural networks, developed in the early 1950s, and with its evolution into "computational learning models." Machine Learning analyzes and extracts features in larger data after exposure to examples; Deep Learning uses neural networks in order to extract meaningful patterns from imaging data, even deciphering that which would otherwise be beyond human perception. Thus, AI has the potential to revolutionize the healthcare systems and clinical practice of doctors all over the world. This is especially true for radiologists, who are integral to diagnostic medicine, helping to customize treatments and triage resources with maximum effectiveness. Related in spirit to Artificial intelligence are Augmented Reality, mixed reality, or Virtual Reality, which are able to enhance accuracy of minimally invasive treatments in image guided therapies by Interventional Radiologists. The potential applications of AI in IR go beyond computer vision and diagnosis, to include screening and modeling of patient selection, predictive tools for treatment planning and navigation, and training tools. Although no new technology is widely embraced, AI may provide opportunities to enhance radiology service and improve patient care, if studied, validated, and applied appropriately.

Gurgitano Martina, Angileri Salvatore Alessio, Rodà Giovanni Maria, Liguori Alessandro, Pandolfi Marco, Ierardi Anna Maria, Wood Bradford J, Carrafiello Gianpaolo

2021-Apr-16

Artificial intelligence (AI), Augmented reality (AR), Deep learning (DL), Interventional radiology (IR), Machine learning (ML), Virtual reality (VR)

Pathology Pathology

A Deep Learning Convolutional Neural Network Can Differentiate Between Helicobacter Pylori Gastritis and Autoimmune Gastritis With Results Comparable to Gastrointestinal Pathologists.

In Archives of pathology & laboratory medicine ; h5-index 49.0

CONTEXT.— : Pathology studies using convolutional neural networks (CNNs) have focused on neoplasms, while studies in inflammatory pathology are rare. We previously demonstrated a CNN differentiates reactive gastropathy, Helicobacter pylori gastritis (HPG), and normal gastric mucosa.

OBJECTIVE.— : To determine whether a CNN can differentiate the following 2 gastric inflammatory patterns: autoimmune gastritis (AG) and HPG.

DESIGN.— : Gold standard diagnoses were blindly established by 2 gastrointestinal (GI) pathologists. One hundred eighty-seven cases were scanned for analysis by HALO-AI. All levels and tissue fragments per slide were included for analysis. The cases were randomized, 112 (60%; 60 HPG, 52 AG) in the training set and 75 (40%; 40 HPG, 35 AG) in the test set. A HALO-AI correct area distribution (AD) cutoff of 50% or more was required to credit the CNN with the correct diagnosis. The test set was blindly reviewed by pathologists with different levels of GI pathology expertise as follows: 2 GI pathologists, 2 general surgical pathologists, and 2 residents. Each pathologist rendered their preferred diagnosis, HPG or AG.

RESULTS.— : At the HALO-AI AD percentage cutoff of 50% or more, the CNN results were 100% concordant with the gold standard diagnoses. On average, autoimmune gastritis cases had 84.7% HALO-AI autoimmune gastritis AD and HP cases had 87.3% HALO-AI HP AD. The GI pathologists, general anatomic pathologists, and residents were on average, 100%, 86%, and 57% concordant with the gold standard diagnoses, respectively.

CONCLUSIONS.— : A CNN can distinguish between cases of HPG and autoimmune gastritis with accuracy equal to GI pathologists.

Franklin Michael M, Schultz Fred A, Tafoya Marissa A, Kerwin Audra A, Broehm Cory J, Fischer Edgar G, Gullapalli Rama R, Clark Douglas P, Hanson Joshua A, Martin David R

2021-Apr-15

Surgery Surgery

Forecasting Future Asthma Hospital Encounters of Patients With Asthma in an Academic Health Care System: Predictive Model Development and Secondary Analysis Study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Asthma affects a large proportion of the population and leads to many hospital encounters involving both hospitalizations and emergency department visits every year. To lower the number of such encounters, many health care systems and health plans deploy predictive models to prospectively identify patients at high risk and offer them care management services for preventive care. However, the previous models do not have sufficient accuracy for serving this purpose well. Embracing the modeling strategy of examining many candidate features, we built a new machine learning model to forecast future asthma hospital encounters of patients with asthma at Intermountain Healthcare, a nonacademic health care system. This model is more accurate than the previously published models. However, it is unclear how well our modeling strategy generalizes to academic health care systems, whose patient composition differs from that of Intermountain Healthcare.

OBJECTIVE : This study aims to evaluate the generalizability of our modeling strategy to the University of Washington Medicine (UWM), an academic health care system.

METHODS : All adult patients with asthma who visited UWM facilities between 2011 and 2018 served as the patient cohort. We considered 234 candidate features. Through a secondary analysis of 82,888 UWM data instances from 2011 to 2018, we built a machine learning model to forecast asthma hospital encounters of patients with asthma in the subsequent 12 months.

RESULTS : Our UWM model yielded an area under the receiver operating characteristic curve (AUC) of 0.902. When placing the cutoff point for making binary classification at the top 10% (1464/14,644) of patients with asthma with the largest forecasted risk, our UWM model yielded an accuracy of 90.6% (13,268/14,644), a sensitivity of 70.2% (153/218), and a specificity of 90.91% (13,115/14,426).

CONCLUSIONS : Our modeling strategy showed excellent generalizability to the UWM, leading to a model with an AUC that is higher than all of the AUCs previously reported in the literature for forecasting asthma hospital encounters. After further optimization, our model could be used to facilitate the efficient and effective allocation of asthma care management resources to improve outcomes.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) : RR2-10.2196/resprot.5039.

Tong Yao, Messinger Amanda I, Wilcox Adam B, Mooney Sean D, Davidson Giana H, Suri Pradeep, Luo Gang

2021-Apr-16

asthma, forecasting, machine learning, patient care management, risk factors

General General

Reliable Deep Learning-Based Detection of Misplaced Chest Electrodes During Electrocardiogram Recording: Algorithm Development and Validation.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : A 12-lead electrocardiogram (ECG) is the most commonly used method to diagnose patients with cardiovascular diseases. However, there are a number of possible misinterpretations of the ECG that can be caused by several different factors, such as the misplacement of chest electrodes.

OBJECTIVE : The aim of this study is to build advanced algorithms to detect precordial (chest) electrode misplacement.

METHODS : In this study, we used traditional machine learning (ML) and deep learning (DL) to autodetect the misplacement of electrodes V1 and V2 using features from the resultant ECG. The algorithms were trained using data extracted from high-resolution body surface potential maps of patients who were diagnosed with myocardial infarction, diagnosed with left ventricular hypertrophy, or a normal ECG.

RESULTS : DL achieved the highest accuracy in this study for detecting V1 and V2 electrode misplacement, with an accuracy of 93.0% (95% CI 91.46-94.53) for misplacement in the second intercostal space. The performance of DL in the second intercostal space was benchmarked with physicians (n=11 and age 47.3 years, SD 15.5) who were experienced in reading ECGs (mean number of ECGs read in the past year 436.54, SD 397.9). Physicians were poor at recognizing chest electrode misplacement on the ECG and achieved a mean accuracy of 60% (95% CI 56.09-63.90), which was significantly poorer than that of DL (P<.001).

CONCLUSIONS : DL provides the best performance for detecting chest electrode misplacement when compared with the ability of experienced physicians. DL and ML could be used to help flag ECGs that have been incorrectly recorded and flag that the data may be flawed, which could reduce the number of erroneous diagnoses.

Rjoob Khaled, Bond Raymond, Finlay Dewar, McGilligan Victoria, J Leslie Stephen, Rababah Ali, Iftikhar Aleeha, Guldenring Daniel, Knoery Charles, McShane Anne, Peace Aaron

2021-Apr-16

ECG, ECG interpretation, cardiovascular disease, deep learning, electrode misplacement, engineering, feature engineering, machine learning, myocardial, myocardial infarction, physicians

Internal Medicine Internal Medicine

Real-Time Clinical Decision Support Based on Recurrent Neural Networks for In-Hospital Acute Kidney Injury: External Validation and Model Interpretation.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Acute kidney injury (AKI) is commonly encountered in clinical practice and is associated with poor patient outcomes and increased health care costs. Despite it posing significant challenges for clinicians, effective measures for AKI prediction and prevention are lacking. Previously published AKI prediction models mostly have a simple design without external validation. Furthermore, little is known about the process of linking model output and clinical decisions due to the black-box nature of neural network models.

OBJECTIVE : We aimed to present an externally validated recurrent neural network (RNN)-based continuous prediction model for in-hospital AKI and show applicable model interpretations in relation to clinical decision support.

METHODS : Study populations were all patients aged 18 years or older who were hospitalized for more than 48 hours between 2013 and 2017 in 2 tertiary hospitals in Korea (Seoul National University Bundang Hospital and Seoul National University Hospital). All demographic data, laboratory values, vital signs, and clinical conditions of patients were obtained from electronic health records of each hospital. We developed 2-stage hierarchical prediction models (model 1 and model 2) using RNN algorithms. The outcome variable for model 1 was the occurrence of AKI within 7 days from the present. Model 2 predicted the future trajectory of creatinine values up to 72 hours. The performance of each developed model was evaluated using the internal and external validation data sets. For the explainability of our models, different model-agnostic interpretation methods were used, including Shapley Additive Explanations, partial dependence plots, individual conditional expectation, and accumulated local effects plots.

RESULTS : We included 69,081 patients in the training, 7675 in the internal validation, and 72,352 in the external validation cohorts for model development after excluding cases with missing data and those with an estimated glomerular filtration rate less than 15 mL/min/1.73 m2 or end-stage kidney disease. Model 1 predicted any AKI development with an area under the receiver operating characteristic curve (AUC) of 0.88 (internal validation) and 0.84 (external validation), and stage 2 or higher AKI development with an AUC of 0.93 (internal validation) and 0.90 (external validation). Model 2 predicted the future creatinine values within 3 days with mean-squared errors of 0.04-0.09 for patients with higher risks of AKI and 0.03-0.08 for those with lower risks. Based on the developed models, we showed AKI probability according to feature values in total patients and each individual with partial dependence, accumulated local effects, and individual conditional expectation plots. We also estimated the effects of feature modifications such as nephrotoxic drug discontinuation on future creatinine levels.

CONCLUSIONS : We developed and externally validated a continuous AKI prediction model using RNN algorithms. Our model could provide real-time assessment of future AKI occurrences and individualized risk factors for AKI in general inpatient cohorts; thus, we suggest approaches to support clinical decisions based on prediction models for in-hospital AKI.

Kim Kipyo, Yang Hyeonsik, Yi Jinyeong, Son Hyung-Eun, Ryu Ji-Young, Kim Yong Chul, Jeong Jong Cheol, Chin Ho Jun, Na Ki Young, Chae Dong-Wan, Han Seung Seok, Kim Sejoong

2021-Apr-16

acute kidney injury, external validation, internal validation, kidney, neural networks, prediction model, recurrent neural network

General General

Design and Delivery of the Clinical Integrative Puzzle as a Collaborative Learning Tool.

In Journal of veterinary medical education

Well-designed collaborative learning tools can provide an opportunity for engaging student experiences that foster deep learning and act as a scaffold for enculturation to the profession through refinement of professional collaborative skills. The clinical integrative puzzle is a paper-and-pencil or computer-based teaching and learning activity that combines disciplinary knowledge with clinical reasoning and problem solving. Effective design and implementation of clinical integrative puzzles requires a multidisciplinary approach to design, a positive classroom climate, and a set of illness scripts (e.g., clinical cases or scenarios) that are similar yet have key differentiating features that provide students with the opportunity to exercise clinical reasoning skills. The tool allows students to co-construct knowledge and develop professional competencies and allows instructors to assess and respond to student learning in a safe and supportive environment, even with large student numbers. The tool can also be used in a summative fashion. This article provides a brief review of the use of this instructional tool and offers tips for design and implementation.

Boller Elise, Courtman Natalie, Chiavaroli Neville, Beck Catherine

2021-Apr

clinical integrative puzzle, clinical reasoning, collaborative learning, extended matching question

General General

Disentangling Rotational Dynamics and Ordering Transitions in a System of Self-Organizing Protein Nanorods via Rotationally Invariant Latent Representations.

In ACS nano ; h5-index 203.0

The dynamics of complex ordering systems with active rotational degrees of freedom exemplified by protein self-assembly is explored using a machine learning workflow that combines deep learning-based semantic segmentation and rotationally invariant variational autoencoder-based analysis of orientation and shape evolution. The latter allows for disentanglement of the particle orientation from other degrees of freedom and compensates for lateral shifts. The disentangled representations in the latent space encode the rich spectrum of local transitions that can now be visualized and explored via continuous variables. The time dependence of ensemble averages allows insight into the time dynamics of the system and, in particular, illustrates the presence of the potential ordering transition. Finally, analysis of the latent variables along the single-particle trajectory allows tracing these parameters on a single-particle level. The proposed approach is expected to be universally applicable for the description of the imaging data in optical, scanning probe, and electron microscopy seeking to understand the dynamics of complex systems where rotations are a significant part of the process.

Kalinin Sergei V, Zhang Shuai, Valleti Mani, Pyles Harley, Baker David, De Yoreo James J, Ziatdinov Maxim

2021-Apr-16

deep learning, latent space models, representation learning, self-assembly, variational autoencoder

Internal Medicine Internal Medicine

Survival time prediction by integrating cox proportional hazards network and distribution function network.

In BMC bioinformatics

BACKGROUND : The Cox proportional hazards model is commonly used to predict hazard ratio, which is the risk or probability of occurrence of an event of interest. However, the Cox proportional hazard model cannot directly generate an individual survival time. To do this, the survival analysis in the Cox model converts the hazard ratio to survival times through distributions such as the exponential, Weibull, Gompertz or log-normal distributions. In other words, to generate the survival time, the Cox model has to select a specific distribution over time.

RESULTS : This study presents a method to predict the survival time by integrating hazard network and a distribution function network. The Cox proportional hazards network is adapted in DeepSurv for the prediction of the hazard ratio and a distribution function network applied to generate the survival time. To evaluate the performance of the proposed method, a new evaluation metric that calculates the intersection over union between the predicted curve and ground truth was proposed. To further understand significant prognostic factors, we use the 1D gradient-weighted class activation mapping method to highlight the network activations as a heat map visualization over an input data. The performance of the proposed method was experimentally verified and the results compared to other existing methods.

CONCLUSIONS : Our results confirmed that the combination of the two networks, Cox proportional hazards network and distribution function network, can effectively generate accurate survival time.

Baek Eu-Tteum, Yang Hyung Jeong, Kim Soo Hyung, Lee Guee Sang, Oh In-Jae, Kang Sae-Ryung, Min Jung-Joon

2021-Apr-15

Cox proportional hazards network, Deep learning, Distribution function network, Prognosis, Survival time prediction

Public Health Public Health

BS-Net: Learning COVID-19 pneumonia severity on a large chest X-ray dataset.

In Medical image analysis

In this work we design an end-to-end deep learning architecture for predicting, on Chest X-rays images (CXR), a multi-regional score conveying the degree of lung compromise in COVID-19 patients. Such semi-quantitative scoring system, namely Brixia score, is applied in serial monitoring of such patients, showing significant prognostic value, in one of the hospitals that experienced one of the highest pandemic peaks in Italy. To solve such a challenging visual task, we adopt a weakly supervised learning strategy structured to handle different tasks (segmentation, spatial alignment, and score estimation) trained with a "from-the-part-to-the-whole" procedure involving different datasets. In particular, we exploit a clinical dataset of almost 5,000 CXR annotated images collected in the same hospital. Our BS-Net demonstrates self-attentive behavior and a high degree of accuracy in all processing stages. Through inter-rater agreement tests and a gold standard comparison, we show that our solution outperforms single human annotators in rating accuracy and consistency, thus supporting the possibility of using this tool in contexts of computer-assisted monitoring. Highly resolved (super-pixel level) explainability maps are also generated, with an original technique, to visually help the understanding of the network activity on the lung areas. We also consider other scores proposed in literature and provide a comparison with a recently proposed non-specific approach. We eventually test the performance robustness of our model on an assorted public COVID-19 dataset, for which we also provide Brixia score annotations, observing good direct generalization and fine-tuning capabilities that highlight the portability of BS-Net in other clinical settings. The CXR dataset along with the source code and the trained model are publicly released for research purposes.

Signoroni Alberto, Savardi Mattia, Benini Sergio, Adami Nicola, Leonardi Riccardo, Gibellini Paolo, Vaccher Filippo, Ravanelli Marco, Borghesi Andrea, Maroldi Roberto, Farina Davide

2021-Mar-31

COVID-19 severity assessment, Chest X-rays, Convolutional neural networks, End-to-end learning, Semi-quantitative rating

General General

Machine Learning to Assess the Risk of Multidrug-Resistant Gram-Negative Bacilli Infections in Febrile Neutropenic Hematological Patients.

In Infectious diseases and therapy

INTRODUCTION : We aimed to assess risk factors for multidrug-resistant Gram-negative bacilli (MDR-GNB) from a large amount of data retrieved from electronic health records (EHRs) and determine whether machine learning (ML) may be useful in assessing the risk of MDR-GNB infection at febrile neutropenia (FN) onset.

METHODS : Retrospective study of almost 7 million pieces of structured data from all consecutive episodes of FN in hematological patients in a tertiary hospital in Barcelona (January 2008-December 2017). Conventional multivariate analysis and ML algorithms (random forest, gradient boosting machine, XGBoost, and GLM) were done.

RESULTS : A total of 3235 episodes of FN in 349 patients were documented; MDR-GNB caused 180 (5.6%) infections in 132 patients. The most frequent MDR-GNBs were MDR-Pseudomonas aeruginosa (53%) and extended-spectrum beta-lactamase-producing Enterobacterales (46%). According to conventional logistic regression analysis, independent factors associated with MDR-GNB infection were age older than 45 years (OR 2.07; 95% CI 1.31-3.24), prior antibiotics (2.62; 1.39-4.92), first-ever FN in this hospitalization (2.94; 1.33-6.52), prior hospitalizations for FN (1.72; 1.02-2.89); at least 15 prior hospital visits (2.65; 1.31-5.33), high-risk hematological diseases (3.62; 1.12-11.67), and hospitalization in a room formerly occupied by patients with MDR-GNB isolation (1.69; 1.20-2.38). ML algorithms achieved the following AUC and F1 score for MDR-GNB prediction: random forest, 0.79-0.9711; GMB, 0.79-0.9705; XGBoost, 0.79-0.9670; and GLM, 0.78-0.9716.

CONCLUSION : Data generated in EHRs proved useful in assessing risk factors for MDR-GNB infections in patients with FN. The great number of analyzed variables allowed us to identify new factors related to MDR infection, as well as to train ML algorithms for infection predictions. This information may be used by clinicians to make better clinical decisions.

Garcia-Vidal Carolina, Puerta-Alcalde Pedro, Cardozo Celia, Orellana Miquel A, Besanson Gaston, Lagunas Jaime, Marco Francesc, Del Rio Ana, Martínez Jose A, Chumbita Mariana, Garcia-Pouton Nicole, Mensa Josep, Rovira Montserrat, Esteve Jordi, Soriano Alex

2021-Apr-16

Electronic health records, Machine learning, Multiresistance, Neutropenia

Radiology Radiology

Systematic review of research design and reporting of imaging studies applying convolutional neural networks for radiological cancer diagnosis.

In European radiology ; h5-index 62.0

OBJECTIVES : To perform a systematic review of design and reporting of imaging studies applying convolutional neural network models for radiological cancer diagnosis.

METHODS : A comprehensive search of PUBMED, EMBASE, MEDLINE and SCOPUS was performed for published studies applying convolutional neural network models to radiological cancer diagnosis from January 1, 2016, to August 1, 2020. Two independent reviewers measured compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Compliance was defined as the proportion of applicable CLAIM items satisfied.

RESULTS : One hundred eighty-six of 655 screened studies were included. Many studies did not meet the criteria for current design and reporting guidelines. Twenty-seven percent of studies documented eligibility criteria for their data (50/186, 95% CI 21-34%), 31% reported demographics for their study population (58/186, 95% CI 25-39%) and 49% of studies assessed model performance on test data partitions (91/186, 95% CI 42-57%). Median CLAIM compliance was 0.40 (IQR 0.33-0.49). Compliance correlated positively with publication year (ρ = 0.15, p = .04) and journal H-index (ρ = 0.27, p < .001). Clinical journals demonstrated higher mean compliance than technical journals (0.44 vs. 0.37, p < .001).

CONCLUSIONS : Our findings highlight opportunities for improved design and reporting of convolutional neural network research for radiological cancer diagnosis.

KEY POINTS : • Imaging studies applying convolutional neural networks (CNNs) for cancer diagnosis frequently omit key clinical information including eligibility criteria and population demographics. • Fewer than half of imaging studies assessed model performance on explicitly unobserved test data partitions. • Design and reporting standards have improved in CNN research for radiological cancer diagnosis, though many opportunities remain for further progress.

O’Shea Robert J, Sharkey Amy Rose, Cook Gary J R, Goh Vicky

2021-Apr-16

Artificial intelligence, Deep learning, Diagnosis, computer-assisted, Neoplasms, Research design

Radiology Radiology

Radiologists in the loop: the roles of radiologists in the development of AI applications.

In European radiology ; h5-index 62.0

OBJECTIVES : To examine the various roles of radiologists in different steps of developing artificial intelligence (AI) applications.

MATERIALS AND METHODS : Through the case study of eight companies active in developing AI applications for radiology, in different regions (Europe, Asia, and North America), we conducted 17 semi-structured interviews and collected data from documents. Based on systematic thematic analysis, we identified various roles of radiologists. We describe how each role happens across the companies and what factors impact how and when these roles emerge.

RESULTS : We identified 9 roles that radiologists play in different steps of developing AI applications: (1) problem finder (in 4 companies); (2) problem shaper (in 3 companies); (3) problem dominator (in 1 company); (4) data researcher (in 2 companies); (5) data labeler (in 3 companies); (6) data quality controller (in 2 companies); (7) algorithm shaper (in 3 companies); (8) algorithm tester (in 6 companies); and (9) AI researcher (in 1 company).

CONCLUSIONS : Radiologists can play a wide range of roles in the development of AI applications. How actively they are engaged and the way they are interacting with the development teams significantly vary across the cases. Radiologists need to become proactive in engaging in the development process and embrace new roles.

KEY POINTS : • Radiologists can play a wide range of roles during the development of AI applications. • Both radiologists and developers need to be open to new roles and ways of interacting during the development process. • The availability of resources, time, expertise, and trust are key factors that impact how actively radiologists play roles in the development process.

Scheek Damian, Rezazade Mehrizi Mohammad H, Ranschaert Erik

2021-Apr-16

AI, Artificial intelligence, Development, Radiologists, Roles

General General

A convolutional neural network for the prediction and forward design of ribozyme-based gene-control elements.

In eLife

Ribozyme switches are a class of RNA-encoded genetic switch that support conditional regulation of gene expression across diverse organisms. An improved elucidation of the relationships between sequence, structure, and activity can improve our capacity for de novo rational design of ribozyme switches. Here, we generated data on the activity of hundreds of thousands of ribozyme sequences. Using automated structural analysis and machine learning, we leveraged these large datasets to develop predictive models that estimate the in vivo gene-regulatory activity of a ribozyme sequence. These models supported the de novo design of ribozyme libraries with low mean basal gene-regulatory activities and new ribozyme switches that exhibit changes in gene-regulatory activity in the presence of a target ligand, producing functional switches for four out of five aptamers. Our work examines how biases in the model and the dataset that affect prediction accuracy can arise and demonstrates that machine learning can be applied to RNA sequences to predict gene-regulatory activity, providing the basis for design tools for functional RNAs.

Schmidt Calvin M, Smolke Christina D

2021-Apr-16

S. cerevisiae, computational biology, systems biology

General General

Harnessing artificial intelligence in cardiac rehabilitation, a systematic review.

In Future cardiology

Aim: This systematic review aims to evaluate the current body of research surrounding the efficacy of artificial intelligence (AI) in cardiac rehabilitation. Presently, AI can be incorporated into personal devices such as smart watches and smartphones, in diagnostic and home monitoring devices, as well as in certain inpatient care settings. Materials & methods: The PRISMA guidelines were followed in this review. Inclusion and exclusion criteria were set using the Population, Intervention, Comparison and Outcomes (PICO) tool. Results: Eight studies meeting the inclusion criteria were found. Conclusion: Incorporation of AI into healthcare, cardiac rehabilitation delivery, and monitoring holds great potential for early detection of cardiac events, allowing for home-based monitoring, and improved clinician decision making.

Sotirakos Sara, Fouda Basem, Mohamed Razif Noor Adeebah, Cribben Niall, Mulhall Cormac, O’Byrne Aisling, Moran Bridget, Connolly Ruairi

2021-Apr-16

arrhythmia, artificial intelligence, cardiac disease, cardiac rehabilitation, heart failure, smartwatch

General General

A deep-learning-based workflow to assess taxonomic affinity of hominid teeth with a test on discriminating Pongo and Homo upper molars.

In American journal of physical anthropology

OBJECTIVES : Convolutional neural network (CNN) is a state-of-art deep learning (DL) method with superior performance in image classification. Here, a CNN-based workflow is proposed to discriminate hominid teeth. Our hope is that this method could help confirm otherwise questionable records of Homo from Pleistocene deposits where there is a standing risk of mis-attributing molars of Pongo to Homo.

METHODS AND MATERIALS : A two-step workflow was designed. The first step is converting the enamel-dentine junction (EDJ) into EDJ card, that is, a two-dimensional image conversion of the three-dimensional EDJ surface. In this step, researchers must carefully orient the teeth according to the cervical plane. The second step is training the CNN learner with labeled EDJ cards. A sample consisting of 53 fossil Pongo and 53 Homo (modern human and Neanderthal) was adopted to generate EDJ cards, which were then separated into training set (n = 84) and validation set (n = 22). To assess the feasibility of this workflow, a Pongo-Homo classifier was trained from the aforementioned EDJ card set, and then the classifier was used to predict the taxonomic affinities of six samples (test set) from von Koenigswald's Chinese Apothecary collection.

RESULTS : Results show that EDJ cards in validation set are classified accurately by the CNN learner. More importantly, taxonomic predictions for six specimens in test set match well with the diagnosis results deduced from multiple lines of evidence, implying the great potential of CNN method.

DISCUSSION : This workflow paves a way for future studies using CNN to address taxonomic complexity (e.g., distinguishing Pongo and Homo teeth from the Pleistocene of Asia). Further improvements include visual interpretation and extending the applicability to moderately worn teeth.

Yi Zhixing, Zanolli Clément, Liao Wei, Wang Wei

2021-Apr-16

convolutional neural network, deep learning, hominid teeth, taxonomy

Surgery Surgery

Machine learning applied to a Cardiac Surgery Recovery Unit and to a Coronary Care Unit for mortality prediction.

In Journal of clinical monitoring and computing

Most established severity-of-illness systems used for prediction of intensive care unit (ICU) mortality were developed targeted at the general ICU population, based on logistic regression (LR). To date, no dynamic predictive tool for ICU mortality has been developed targeted at the Cardiac Surgery Recovery Unit (CSRU) and Coronary Care Unit (CCU) using machine learning (ML). CSRU and CCU adult patients from the MIMIC-III critical care database were studied. The ML methods developed extract ICU data during a 5-h window and demographic features to produce mortality predictions and were compared to six established severity-of-illness systems and LR. In a secondary experiment, additional procedure/surgery and ICU features were added to the models. The ML models developed were the Tree Ensemble (TE), Random Forest, XGBoost Tree Ensemble (XGB), Naive Bayes (NB), and Bayesian network. The discrimination, calibration and accuracy statistics were assessed. The AUROC values were superior for the ML models reaching 0.926 and 0.924 for the XGB, and 0.904 and 0.908 for the TE for ICU mortality prediction in the primary and secondary experiments respectively. Among the conventional systems, the serial SOFA obtained the highest AUROC (0.8405). The Brier score was better for the ML models except the NB over the conventional systems. The accuracy statistics less sensitive to unbalanced cohorts were higher for all the ML models. In conclusion, the predictive power of XGB was excellent, substantially outperforming the conventional systems and LR. The ML models developed in this work offer promising results that could benefit CSRU and CCU.

Nistal-Nuño Beatriz

2021-Apr-15

Cardiac Surgery Recovery Unit, Coronary Care Unit, Decision-support systems, Machine learning, Mortality

Radiology Radiology

A nomogram to predict rupture risk of middle cerebral artery aneurysm.

In Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology

BACKGROUND : Determining the rupture risk of unruptured intracranial aneurysm is crucial for treatment strategy. The purpose of this study was to predict the rupture risk of middle cerebral artery (MCA) aneurysms using a machine learning technique.

METHODS : We retrospectively reviewed 403 MCA aneurysms and randomly partitioned them into the training and testing datasets with a ratio of 8:2. A generalized linear model with logit link was developed using training dataset to predict the aneurysm rupture risk based on the clinical variables and morphological features manually measured from computed tomography angiography. To facilitate the clinical application, we further constructed an easy-to-use nomogram based on the developed model.

RESULTS : Ruptured MCA aneurysm had larger aneurysm size, aneurysm height, perpendicular height, aspect ratio, size ratio, bottleneck factor, and height-width ratio. Presence of a daughter-sac was more common in ruptured than in unruptured MCA aneurysms. Six features, including aneurysm multiplicity, lobulations, size ratio, bottleneck factor, height-width ratio, and aneurysm angle, were adopted in the model after feature selection. The model achieved a relatively good performance with areas under the receiver operating characteristic curves of 0.77 in the training dataset and 0.76 in the testing dataset. The nomogram provided a visual interpretation of our model, and the rupture risk probability of MCA aneurysms can be directly read from it.

CONCLUSION : Our model can be used to predict the rupture risk of MCA aneurysm.

Liu Jinjin, Chen Yongchun, Zhu Dongqin, Li Qiong, Chen Zhonggang, Zhou Jiafeng, Lin Boli, Yang Yunjun, Jia Xiufen

2021-Apr-15

Aneurysm, Computed tomography angiography, Morphology, Prediction, Rupture

General General

Parsing Sage and Rosemary in Time: The Machine Learning Race to Crack Olfactory Perception.

In Chemical senses

Color and pitch perception are largely understandable from characteristics of a physical stimuli: the wavelengths of light and sound waves, respectively. By contrast, understanding olfactory percepts from odorous stimuli (volatile molecules) is much more challenging. No intuitive set of molecular features is up to the task. Here in Chemical Senses, the Ray lab reports using a predictive modeling framework--first breaking molecular structure into thousands of features and then using this to train a predictive statistical model on a wide range of perceptual descriptors--to create a tool for predicting the odor character of hundreds of thousands of available but previously uncharacterized molecules (Kowalewski, Huynh and Ray, 2021). This will allow future investigators to representatively sample the space of odorous molecules as well as identify previously unknown odorants with a target odor character. Here I put this work into the context of other modeling efforts and highlight the urgent need for large new datasets and transparent benchmarks for the field to make and evaluate modeling breakthroughs, respectively.

Gerkin Richard C

2021-Apr-16

Public Health Public Health

Climate action for health and wellbeing in cities: a protocol for the systematic development of a database of peer-reviewed studies using machine learning methods.

In Wellcome open research

Cities produce more than 70% of global greenhouse gas emissions. Action by cities is therefore crucial for climate change mitigation as well as for safeguarding the health and wellbeing of their populations under climate change. Many city governments have made ambitious commitments to climate change mitigation and adaptation and implemented a range of actions to address them. However, a systematic record and synthesis of the findings of evaluations of the effect of such actions on human health and wellbeing is currently lacking. This, in turn, impedes the development of robust knowledge on what constitutes high-impact climate actions of benefit to human health and wellbeing, which can inform future action plans, their implementation and scale-up. The development of a systematic record of studies reporting climate and health actions in cities is made challenging by the broad landscape of relevant literature scattered across many disciplines and sectors, which is challenging to effectively consolidate using traditional literature review methods. This protocol reports an innovative approach for the systematic development of a database of studies of climate change mitigation and adaptation actions implemented in cities, and their benefits (or disbenefits) for human health and wellbeing, derived from peer-reviewed academic literature. Our approach draws on extensive tailored search strategies and machine learning methods for article classification and tagging to generate a database for subsequent systematic reviews addressing questions of importance to urban decision-makers on climate actions in cities for human health and wellbeing.

Belesova Kristine, Callaghan Max, Minx Jan C, Creutzig Felix, Turcu Catalina, Hutchinson Emma, Milner James, Crane Melanie, Haines Andy, Davies Michael, Wilkinson Paul

2021

actions, adaptation, case studies, cities, climate action, climate change, evaluation, implementation, intervention, mitigation, planetary health, public health, solutions, urban health, wellbeing

General General

Inter-regional multimedia fate analysis of PAHs and potential risk assessment by integrating deep learning and climate change scenarios.

In Journal of hazardous materials

Polycyclic aromatic hydrocarbons (PAHs) are hazardous compounds associated with respiratory disease and lung cancer. Increasing fossil fuel consumption, which causes climate change, has accelerated the emissions of PAHs. However, potential risks by PAHs have not been predicted for Korea, and appropriate PAH regulations under climate change have yet to be developed. This study assesses the potential risks posed by PAHs using climate change scenarios based on deep learning, and a multimedia fugacity model was employed to describe the future fate of PAHs. The multimedia fugacity model describes the dynamics of sixteen PAHs by reflecting inter-regional meteorological transportation. A deep neural network predicts future environmental and economic conditions, and the potential risks posed by PAHs, in the year 2050, using a prediction model and climate change scenarios. The assessment indicates that cancer risks would increase by more than 50%, exceeding the lower risk threshold in the southern and western regions. A mix of strategies for developing PAH regulatory policies highlighted the necessity of increasing PAHs monitoring stations and controlling fossil fuel usage based on the domestic and global conditions under climate change scenarios.

Nam Ki Jeon, Li Qian, Heo Sung Ku, Tariq Shahzeb, Loy-Benitez Jorge, Woo Tae Yong, Yoo Chang Kyoo

2021-Jun-05

Climate change, Deep learning, Dynamic fate analysis, Polycyclic aromatic hydrocarbons, Potential risk

Pathology Pathology

Novel application of automated machine learning with MALDI-TOF-MS for rapid high-throughput screening of COVID-19: a proof of concept.

In Scientific reports ; h5-index 158.0

The 2019 novel coronavirus infectious disease (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created an unsustainable need for molecular diagnostic testing. Molecular approaches such as reverse transcription (RT) polymerase chain reaction (PCR) offers highly sensitive and specific means to detect SARS-CoV-2 RNA, however, despite it being the accepted "gold standard", molecular platforms often require a tradeoff between speed versus throughput. Matrix assisted laser desorption ionization (MALDI)-time of flight (TOF)-mass spectrometry (MS) has been proposed as a potential solution for COVID-19 testing and finding a balance between analytical performance, speed, and throughput, without relying on impacted supply chains. Combined with machine learning (ML), this MALDI-TOF-MS approach could overcome logistical barriers encountered by current testing paradigms. We evaluated the analytical performance of an ML-enhanced MALDI-TOF-MS method for screening COVID-19. Residual nasal swab samples from adult volunteers were used for testing and compared against RT-PCR. Two optimized ML models were identified, exhibiting accuracy of 98.3%, positive percent agreement (PPA) of 100%, negative percent agreement (NPA) of 96%, and accuracy of 96.6%, PPA of 98.5%, and NPA of 94% respectively. Machine learning enhanced MALDI-TOF-MS for COVID-19 testing exhibited performance comparable to existing commercial SARS-CoV-2 tests.

Tran Nam K, Howard Taylor, Walsh Ryan, Pepper John, Loegering Julia, Phinney Brett, Salemi Michelle R, Rashidi Hooman H

2021-Apr-15

Public Health Public Health

A Noninvasive Prediction Model for Hepatitis B Virus Disease in Patients with HIV: Based on the Population of Jiangsu, China.

In BioMed research international ; h5-index 102.0

Objective : To establish a machine learning model for identifying patients coinfected with hepatitis B virus (HBV) and human immunodeficiency virus (HIV) through two sexual transmission routes in Jiangsu, China.

Methods : A total of 14197 HIV cases transmitted by homosexual and heterosexual routes were recruited. After data processing, 12469 cases (HIV and HBV, 1033; HIV, 11436) were left for further analysis, including 7849 cases with homosexual transmission and 4620 cases with heterosexual transmission. Univariate logistic regression was used to select variables with significant P value and odds ratio for multivariable analysis. In homosexual transmission and heterosexual transmission groups, 10 and 6 variables were selected, respectively. For identifying HIV individuals coinfected with HBV, a machine learning model was constructed with four algorithms, including Decision Tree, Random Forest, AdaBoost with decision tree (AdaBoost), and extreme gradient boosting decision tree (XGBoost). The detective value of each variable was calculated using the optimal machine learning algorithm.

Results : AdaBoost algorithm showed the highest efficiency in both transmission groups (homosexual transmission group: accuracy = 0.928, precision = 0.915, recall = 0.944, F - 1 = 0.930, and AUC = 0.96; heterosexual transmission group: accuracy = 0.892, precision = 0.881, recall = 0.905, F - 1 = 0.893, and AUC = 0.98). Calculated by AdaBoost algorithm, the detective value of PLA was the highest in homosexual transmission group, followed by CR, AST, HB, ALT, TBIL, leucocyte, age, marital status, and treatment condition; in the heterosexual transmission group, the detective value of PLA was the highest (consistent with the condition in the homosexual group), followed by ALT, AST, TBIL, leucocyte, and symptom severity.

Conclusions : The univariate logistics regression combined with the AdaBoost algorithm could accurately screen the risk factors of HBV in HIV coinfection without invasive testing. Further studies are needed to evaluate the utility and feasibility of this model in various settings.

Yin Yi, Xue Mingyue, Shi Lingen, Qiu Tao, Xia Derun, Fu Gengfeng, Peng Zhihang

2021

General General

Implementing Multilabeling, ADASYN, and ReliefF Techniques for Classification of Breast Cancer Diagnostic through Machine Learning: Efficient Computer-Aided Diagnostic System.

In Journal of healthcare engineering

Multilabel recognition of morphological images and detection of cancerous areas are difficult to locate in the scenario of the image redundancy and less resolution. Cancerous tissues are incredibly tiny in various scenarios. Therefore, for automatic classification, the characteristics of cancer patches in the X-ray image are of critical importance. Due to the slight variation between the textures, using just one feature or using a few features contributes to inaccurate classification outcomes. The present study focuses on five different algorithms for extracting features that can extract further different features. The algorithms are GLCM, LBGLCM, LBP, GLRLM, and SFTA from 8 image groups, and then, the extracted feature spaces are combined. The dataset used for classification is most probably imbalanced. Additionally, another focal point is to eradicate the unbalanced data problem by creating more samples using the ADASYN algorithm so that the error rate is minimized and the accuracy is increased. By using the ReliefF algorithm, it skips less contributing features that relieve the burden on the process. Finally, the feedforward neural network is used for the classification of data. The proposed method showed 99.5% micro, 99.5% macro, 0.5% misclassification, 99.5% recall rats, specificity 99.4%, precision 99.5%, and accuracy 99.5%, showing its robustness in these results. To assess the feasibility of the new system, the INbreast database was used.

Khan Taha Muthar, Xu Shengjun, Khan Zullatun Gull, Uzair Chishti Muhammad

2021

General General

A Protocol for the Diagnosis of Autism Spectrum Disorder Structured in Machine Learning and Verbal Decision Analysis.

In Computational and mathematical methods in medicine

Autism Spectrum Disorder is a mental disorder that afflicts millions of people worldwide. It is estimated that one in 160 children has traces of autism, with five times the higher prevalence in boys. The protocols for detecting symptoms are diverse. However, the following are among the most used: the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5), of the American Psychiatric Association; the Revised Autistic Diagnostic Observation Schedule (ADOS-R); the Autistic Diagnostic Interview (ADI); and the International Classification of Diseases, 10th edition (ICD-10), published by the World Health Organization (WHO) and adopted in Brazil by the Unified Health System (SUS). The application of machine learning models helps make the diagnostic process of Autism Spectrum Disorder more precise, reducing, in many cases, the number of criteria necessary for evaluation, denoting a form of attribute engineering (feature engineering) efficiency. This work proposes a hybrid approach based on machine learning algorithms' composition to discover knowledge and concepts associated with the multicriteria method of decision support based on Verbal Decision Analysis to refine the results. Therefore, the study has the general objective of evaluating how the mentioned hybrid methodology proposal can make the protocol derived from ICD-10 more efficient, providing agility to diagnosing Autism Spectrum Disorder by observing a minor symptom. The study database covers thousands of cases of people who, once diagnosed, obtained government assistance in Brazil.

Andrade Evandro, Portela Samuel, Pinheiro Plácido Rogério, Nunes Luciano Comin, Filho Marum Simão, Costa Wagner Silva, Pinheiro Mirian Caliope Dantas

2021

General General

Guidance for Acupuncture Robot with Potentially Utilizing Medical Robotic Technologies.

In Evidence-based complementary and alternative medicine : eCAM

Acupuncture is gaining increasing attention and recognition all over the world. However, a lot of physical labor is paid by acupuncturists. It is natural to resort to a robot which can improve the accuracy as well as the efficacy of therapy. Several teams have separately developed real acupuncture robots or related technologies and even went to the stage of clinical trial and then achieved success commercially. A completed clinical practical acupuncture robot is not far from reach with the combination of existing mature medical robotic technologies. A hand-eye-brain coordination framework is proposed in this review to integrate the potential utilizing technologies including force feedback, binocular vision, and automatic prescription. We should take acupuncture prescription with artificial intelligence and future development trends into account and make a feasible choice in development of modern acupuncture.

Xu Tiancheng, Xia Youbing

2021

General General

Automatic Detection of Obstructive Sleep Apnea Events Using a Deep CNN-LSTM Model.

In Computational intelligence and neuroscience

Obstructive sleep apnea (OSA) is a common sleep-related respiratory disorder. Around the world, more and more people are suffering from OSA. Because of the limitation of monitor equipment, many people with OSA remain undetected. Therefore, we propose a sleep-monitoring model based on single-channel electrocardiogram using a convolutional neural network (CNN), which can be used in portable OSA monitor devices. To learn different scale features, the first convolution layer comprises three types of filters. The long short-term memory (LSTM) is used to learn the long-term dependencies such as the OSA transition rules. The softmax function is connected to the final fully connected layer to obtain the final decision. To detect a complete OSA event, the raw ECG signals are segmented by a 10 s overlapping sliding window. The proposed model is trained with the segmented raw signals and is subsequently tested to evaluate its event detection performance. According to experiment analysis, the proposed model exhibits Cohen's kappa coefficient of 0.92, a sensitivity of 96.1%, a specificity of 96.2%, and an accuracy of 96.1% with respect to the Apnea-ECG dataset. The proposed model is significantly higher than the results from the baseline method. The results prove that our approach could be a useful tool for detecting OSA on the basis of a single-lead ECG.

Zhang Junming, Tang Zhen, Gao Jinfeng, Lin Li, Liu Zhiliang, Wu Haitao, Liu Fang, Yao Ruxian

2021

General General

Early Detection of Stripe Rust in Winter Wheat Using Deep Residual Neural Networks.

In Frontiers in plant science

Stripe rust (Pst) is a major disease of wheat crops leading untreated to severe yield losses. The use of fungicides is often essential to control Pst when sudden outbreaks are imminent. Sensors capable of detecting Pst in wheat crops could optimize the use of fungicides and improve disease monitoring in high-throughput field phenotyping. Now, deep learning provides new tools for image recognition and may pave the way for new camera based sensors that can identify symptoms in early stages of a disease outbreak within the field. The aim of this study was to teach an image classifier to detect Pst symptoms in winter wheat canopies based on a deep residual neural network (ResNet). For this purpose, a large annotation database was created from images taken by a standard RGB camera that was mounted on a platform at a height of 2 m. Images were acquired while the platform was moved over a randomized field experiment with Pst-inoculated and Pst-free plots of winter wheat. The image classifier was trained with 224 × 224 px patches tiled from the original, unprocessed camera images. The image classifier was tested on different stages of the disease outbreak. At patch level the image classifier reached a total accuracy of 90%. To test the image classifier on image level, the image classifier was evaluated with a sliding window using a large striding length of 224 px allowing for fast test performance. At image level, the image classifier reached a total accuracy of 77%. Even in a stage with very low disease spreading (0.5%) at the very beginning of the Pst outbreak, a detection accuracy of 57% was obtained. Still in the initial phase of the Pst outbreak with 2 to 4% of Pst disease spreading, detection accuracy with 76% could be attained. With further optimizations, the image classifier could be implemented in embedded systems and deployed on drones, vehicles or scanning systems for fast mapping of Pst outbreaks.

Schirrmann Michael, Landwehr Niels, Giebel Antje, Garz Andreas, Dammer Karl-Heinz

2021

ResNet, camera sensor, deep learning, image recognition, monitoring, smart farming, wheat crops, yellow rust

General General

Predicting ASD Diagnosis in Children with Synthetic and Image-based Eye Gaze Data.

In Signal processing. Image communication

As early intervention is highly effective for young children with autism spectrum disorder (ASD), it is imperative to make accurate diagnosis as early as possible. ASD has often been associated with atypical visual attention and eye gaze data can be collected at a very early age. An automatic screening tool based on eye gaze data that could identify ASD risk offers the opportunity for intervention before the full set of symptoms is present. In this paper, we propose two machine learning methods, synthetic saccade approach and image based approach, to automatically classify ASD given children's eye gaze data collected from free-viewing tasks of natural images. The first approach uses a generative model of synthetic saccade patterns to represent the baseline scan-path from a typical non-ASD individual and combines it with the real scan-path as well as other auxiliary data as inputs to a deep learning classifier. The second approach adopts a more holistic image-based approach by feeding the input image and a sequence of fixation maps into a convolutional or recurrent neural network. Using a publicly-accessible collection of children's gaze data, our experiments indicate that the ASD prediction accuracy reaches 67.23% accuracy on the validation dataset and 62.13% accuracy on the test dataset.

Liaqat Sidrah, Wu Chongruo, Duggirala Prashanth Reddy, Cheung Sen-Ching Samson, Chuah Chen-Nee, Ozonoff Sally, Young Gregory

2021-May

Autism Spectrum Disorders, Deep Learning, Eye Gaze Data

General General

ENTRYWISE EIGENVECTOR ANALYSIS OF RANDOM MATRICES WITH LOW EXPECTED RANK.

In Annals of statistics

Recovering low-rank structures via eigenvector perturbation analysis is a common problem in statistical machine learning, such as in factor analysis, community detection, ranking, matrix completion, among others. While a large variety of bounds are available for average errors between empirical and population statistics of eigenvectors, few results are tight for entrywise analyses, which are critical for a number of problems such as community detection. This paper investigates entrywise behaviors of eigenvectors for a large class of random matrices whose expectations are low-rank, which helps settle the conjecture in Abbe et al. (2014b) that the spectral algorithm achieves exact recovery in the stochastic block model without any trimming or cleaning steps. The key is a first-order approximation of eigenvectors under the norm: u k A u k * λ k * , where {u k } and { u k * } are eigenvectors of a random matrix A and its expectation E A , respectively. The fact that the approximation is both tight and linear in A facilitates sharp comparisons between u k and u k * . In particular, it allows for comparing the signs of u k and u k * even if u k - u k * is large. The results are further extended to perturbations of eigenspaces, yielding new -type bounds for synchronization ( 2 -spiked Wigner model) and noisy matrix completion.

Abbe Emmanuel, Fan Jianqing, Wang Kaizheng, Zhong Yiqiao

2020-Jun

62H12, Primary 62H25, community detection, eigenvector perturbation, low-rank structures, matrix completion, random matrices, secondary 60B20, spectral analysis, synchronization

Pathology Pathology

Triage-driven diagnosis of Barrett's esophagus for early detection of esophageal adenocarcinoma using deep learning.

In Nature medicine ; h5-index 170.0

Deep learning methods have been shown to achieve excellent performance on diagnostic tasks, but how to optimally combine them with expert knowledge and existing clinical decision pathways is still an open challenge. This question is particularly important for the early detection of cancer, where high-volume workflows may benefit from (semi-)automated analysis. Here we present a deep learning framework to analyze samples of the Cytosponge-TFF3 test, a minimally invasive alternative to endoscopy, for detecting Barrett's esophagus, which is the main precursor of esophageal adenocarcinoma. We trained and independently validated the framework on data from two clinical trials, analyzing a combined total of 4,662 pathology slides from 2,331 patients. Our approach exploits decision patterns of gastrointestinal pathologists to define eight triage classes of varying priority for manual expert review. By substituting manual review with automated review in low-priority classes, we can reduce pathologist workload by 57% while matching the diagnostic performance of experienced pathologists.

Gehrung Marcel, Crispin-Ortuzar Mireia, Berman Adam G, O’Donovan Maria, Fitzgerald Rebecca C, Markowetz Florian

2021-Apr-15

General General

Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning.

In Nature biomedical engineering

The optimization of therapeutic antibodies is time-intensive and resource-demanding, largely because of the low-throughput screening of full-length antibodies (approximately 1 × 103 variants) expressed in mammalian cells, which typically results in few optimized leads. Here we show that optimized antibody variants can be identified by predicting antigen specificity via deep learning from a massively diverse space of antibody sequences. To produce data for training deep neural networks, we deep-sequenced libraries of the therapeutic antibody trastuzumab (about 1 × 104 variants), expressed in a mammalian cell line through site-directed mutagenesis via CRISPR-Cas9-mediated homology-directed repair, and screened the libraries for specificity to human epidermal growth factor receptor 2 (HER2). We then used the trained neural networks to screen a computational library of approximately 1 × 108 trastuzumab variants and predict the HER2-specific subset (approximately 1 × 106 variants), which can then be filtered for viscosity, clearance, solubility and immunogenicity to generate thousands of highly optimized lead candidates. Recombinant expression and experimental testing of 30 randomly selected variants from the unfiltered library showed that all 30 retained specificity for HER2. Deep learning may facilitate antibody engineering and optimization.

Mason Derek M, Friedensohn Simon, Weber Cédric R, Jordi Christian, Wagner Bastian, Meng Simon M, Ehling Roy A, Bonati Lucia, Dahinden Jan, Gainza Pablo, Correia Bruno E, Reddy Sai T

2021-Apr-15

Public Health Public Health

Predicting the risk of GenX contamination in private well water using a machine-learned Bayesian network model.

In Journal of hazardous materials

Per- and polyfluoroalkyl substances (PFAS) are emerging contaminants that pose significant challenges in mechanistic fate and transport modeling due to their diverse and complex chemical characteristics. Machine learning provides a novel approach for predicting the spatial distribution of PFAS in the environment. We used spatial location information to link PFAS measurements from 1207 private drinking water wells around a fluorochemical manufacturing facility to a mechanistic model of PFAS air deposition and to publicly available data on soil, land use, topography, weather, and proximity to multiple PFAS sources. We used the resulting linked data set to train a Bayesian network model to predict the risk that GenX, a member of the PFAS class, would exceed a state provisional health goal (140 ng/L) in private well water. The model had high accuracy (ROC curve index for five-fold cross-validation of 0.85, 90% CI 0.84-0.87). Among factors significantly associated with GenX risk in private wells, the most important was the historic rate of atmospheric deposition of GenX from the fluorochemical manufacturing facility. The model output was used to generate spatial risk predictions for the study area to aid in risk assessment, environmental investigations, and targeted public health interventions.

Roostaei Javad, Colley Sarah, Mulhern Riley, May Andrew A, Gibson Jacqueline MacDonald

2021-Jun-05

Bayesian network, Drinking water, GenX, Machine-learning, PFAS, Well water

General General

Patients at high risk of suicide before and during a COVID-19 lockdown: ecological momentary assessment study.

In BJPsych open

The coronavirus disease 2019 (COVID-19) outbreak may have affected the mental health of patients at high risk of suicide. In this study we explored the wish to die and other suicide risk factors using smartphone-based ecological momentary assessment (EMA) in patients with a history of suicidal thoughts and behaviour. Contrary to our expectations we found a decrease in the wish to die during lockdown. This is consistent with previous studies showing that suicide rates decrease during periods of social emergency. Smartphone-based EMA can allow us to remotely assess patients and overcome the physical barriers imposed by lockdown.

Cobo Aurora, Porras-Segovia Alejandro, Pérez-Rodríguez María Mercedes, Artés-Rodríguez Antonio, Barrigón Maria Luisa, Courtet Philippe, Baca-García Enrique

2021-Apr-16

COVID-19, Suicide, ecological momentary assessment, machine learning, suicide attempt

General General

U-Sleep: resilient high-frequency sleep staging.

In NPJ digital medicine

Sleep disorders affect a large portion of the global population and are strong predictors of morbidity and all-cause mortality. Sleep staging segments a period of sleep into a sequence of phases providing the basis for most clinical decisions in sleep medicine. Manual sleep staging is difficult and time-consuming as experts must evaluate hours of polysomnography (PSG) recordings with electroencephalography (EEG) and electrooculography (EOG) data for each patient. Here, we present U-Sleep, a publicly available, ready-to-use deep-learning-based system for automated sleep staging ( sleep.ai.ku.dk ). U-Sleep is a fully convolutional neural network, which was trained and evaluated on PSG recordings from 15,660 participants of 16 clinical studies. It provides accurate segmentations across a wide range of patient cohorts and PSG protocols not considered when building the system. U-Sleep works for arbitrary combinations of typical EEG and EOG channels, and its special deep learning architecture can label sleep stages at shorter intervals than the typical 30 s periods used during training. We show that these labels can provide additional diagnostic information and lead to new ways of analyzing sleep. U-Sleep performs on par with state-of-the-art automatic sleep staging systems on multiple clinical datasets, even if the other systems were built specifically for the particular data. A comparison with consensus-scores from a previously unseen clinic shows that U-Sleep performs as accurately as the best of the human experts. U-Sleep can support the sleep staging workflow of medical experts, which decreases healthcare costs, and can provide highly accurate segmentations when human expertize is lacking.

Perslev Mathias, Darkner Sune, Kempfner Lykke, Nikolic Miki, Jennum Poul Jørgen, Igel Christian

2021-Apr-15

General General

The image features of emotional faces that predict the initial eye movement to a face.

In Scientific reports ; h5-index 158.0

Emotional facial expressions are important visual communication signals that indicate a sender's intent and emotional state to an observer. As such, it is not surprising that reactions to different expressions are thought to be automatic and independent of awareness. What is surprising, is that studies show inconsistent results concerning such automatic reactions, particularly when using different face stimuli. We argue that automatic reactions to facial expressions can be better explained, and better understood, in terms of quantitative descriptions of their low-level image features rather than in terms of the emotional content (e.g. angry) of the expressions. Here, we focused on overall spatial frequency (SF) and localized Histograms of Oriented Gradients (HOG) features. We used machine learning classification to reveal the SF and HOG features that are sufficient for classification of the initial eye movement towards one out of two simultaneously presented faces. Interestingly, the identified features serve as better predictors than the emotional content of the expressions. We therefore propose that our modelling approach can further specify which visual features drive these and other behavioural effects related to emotional expressions, which can help solve the inconsistencies found in this line of research.

Stuit S M, Kootstra T M, Terburg D, van den Boomen C, van der Smagt M J, Kenemans J L, Van der Stigchel S

2021-Apr-15

General General

Spatio-temporal feature learning with reservoir computing for T-cell segmentation in live-cell [Formula: see text] fluorescence microscopy.

In Scientific reports ; h5-index 158.0

Advances in high-resolution live-cell [Formula: see text] imaging enabled subcellular localization of early [Formula: see text] signaling events in T-cells and paved the way to investigate the interplay between receptors and potential target channels in [Formula: see text] release events. The huge amount of acquired data requires efficient, ideally automated image processing pipelines, with cell localization/segmentation as central tasks. Automated segmentation in live-cell cytosolic [Formula: see text] imaging data is, however, challenging due to temporal image intensity fluctuations, low signal-to-noise ratio, and photo-bleaching. Here, we propose a reservoir computing (RC) framework for efficient and temporally consistent segmentation. Experiments were conducted with Jurkat T-cells and anti-CD3 coated beads used for T-cell activation. We compared the RC performance with a standard U-Net and a convolutional long short-term memory (LSTM) model. The RC-based models (1) perform on par in terms of segmentation accuracy with the deep learning models for cell-only segmentation, but show improved temporal segmentation consistency compared to the U-Net; (2) outperform the U-Net for two-emission wavelengths image segmentation and differentiation of T-cells and beads; and (3) perform on par with the convolutional LSTM for single-emission wavelength T-cell/bead segmentation and differentiation. In turn, RC models contain only a fraction of the parameters of the baseline models and reduce the training time considerably.

Hadaeghi Fatemeh, Diercks Björn-Philipp, Schetelig Daniel, Damicelli Fabrizio, Wolf Insa M A, Werner René

2021-Apr-15

General General

Crop response to El Niño-Southern Oscillation related weather variation to help farmers manage their crops.

In Scientific reports ; h5-index 158.0

Although weather is a major driver of crop yield, many farmers don't know in advance how the weather will vary nor how their crops will respond. We hypothesized that where El Niño-Southern Oscillation (ENSO) drives weather patterns, and data on crop response to distinct management practices exists, it should be possible to map ENSO Oceanic Index (ENSO OI) patterns to crop management responses without precise weather data. Time series data on cacao farm yields in Sulawesi, Indonesia, with and without fertilizer, were used to provide proof-of-concept. A machine learning approach associated 75% of cacao yield variation with the ENSO patterns up to 8 and 24 months before harvest and predicted when fertilizer applications would be worthwhile. Thus, it's possible to relate average cacao crop performance and management response directly to ENSO patterns without weather data provided: (1) site specific data exist on crop performance over time with distinct management practices; and (2) the weather patterns are driven by ENSO OI. We believe that the principles established here can readily be applied to other crops, particularly when there's little data available on crop responses to management and weather. However, specific models will be required for each crop and every recommendation domain.

Chapman Ross, Cock James, Samson Marianne, Janetski Noel, Janetski Kate, Gusyana Dadang, Dutta Sudarshan, Oberthür Thomas

2021-Apr-15

General General

Novel, non-invasive markers for detecting therapy induced neuroendocrine differentiation in castration-resistant prostate cancer patients.

In Scientific reports ; h5-index 158.0

Neuroendocrine prostate cancer (NEPC), a highly aggressive variant of castration-resistant prostate cancer (CRPC), often emerges upon treatment with androgen pathway inhibitors, via neuroendocrine differentiation. Currently, NEPC diagnosis is challenging as available markers are not sufficiently specific. Our objective was to identify novel, extracellular vesicles (EV)-based biomarkers for diagnosing NEPC. Towards this, we performed small RNA next generation sequencing in serum EVs isolated from a cohort of CRPC patients with adenocarcinoma characteristics (CRPC-Adeno) vs CRPC-NE and identified significant dysregulation of 182 known and 4 novel miRNAs. We employed machine learning algorithms to develop an 'EV-miRNA classifier' that could robustly stratify 'CRPC-NE' from 'CRPC-Adeno'. Examination of protein repertoire of exosomes from NEPC cellular models by mass spectrometry identified thrombospondin 1 (TSP1) as a specific biomarker. In view of our results, we propose that a miRNA panel and TSP1 can be used as novel, non-invasive tools to identify NEPC and guide treatment decisions. In conclusion, our study identifies for the first time, novel non-invasive exosomal/extracellular vesicle based biomarkers for detecting neuroendocrine differentiation in advanced castration resistant prostate cancer patients with important translational implications in clinical management of these patients that is currently extremely challenging.

Bhagirath Divya, Liston Michael, Akoto Theresa, Lui Byron, Bensing Barbara A, Sharma Ashok, Saini Sharanjot

2021-Apr-15

Pathology Pathology

Novel application of automated machine learning with MALDI-TOF-MS for rapid high-throughput screening of COVID-19: a proof of concept.

In Scientific reports ; h5-index 158.0

The 2019 novel coronavirus infectious disease (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created an unsustainable need for molecular diagnostic testing. Molecular approaches such as reverse transcription (RT) polymerase chain reaction (PCR) offers highly sensitive and specific means to detect SARS-CoV-2 RNA, however, despite it being the accepted "gold standard", molecular platforms often require a tradeoff between speed versus throughput. Matrix assisted laser desorption ionization (MALDI)-time of flight (TOF)-mass spectrometry (MS) has been proposed as a potential solution for COVID-19 testing and finding a balance between analytical performance, speed, and throughput, without relying on impacted supply chains. Combined with machine learning (ML), this MALDI-TOF-MS approach could overcome logistical barriers encountered by current testing paradigms. We evaluated the analytical performance of an ML-enhanced MALDI-TOF-MS method for screening COVID-19. Residual nasal swab samples from adult volunteers were used for testing and compared against RT-PCR. Two optimized ML models were identified, exhibiting accuracy of 98.3%, positive percent agreement (PPA) of 100%, negative percent agreement (NPA) of 96%, and accuracy of 96.6%, PPA of 98.5%, and NPA of 94% respectively. Machine learning enhanced MALDI-TOF-MS for COVID-19 testing exhibited performance comparable to existing commercial SARS-CoV-2 tests.

Tran Nam K, Howard Taylor, Walsh Ryan, Pepper John, Loegering Julia, Phinney Brett, Salemi Michelle R, Rashidi Hooman H

2021-Apr-15

oncology Oncology

Nasal versus oronasal masks for home non-invasive ventilation in patients with chronic hypercapnia: a systematic review and individual participant data meta-analysis.

In Thorax ; h5-index 75.0

BACKGROUND : The optimal interface for the delivery of home non-invasive ventilation (NIV) to treat chronic respiratory failure has not yet been determined. The aim of this individual participant data (IPD) meta-analysis was to compare the effect of nasal and oronasal masks on treatment efficacy and adherence in patients with COPD and obesity hypoventilation syndrome (OHS).

METHODS : We searched Medline and Cochrane Central Register of Controlled Trials for prospective randomised controlled trials (RCTs) of at least 1 month's duration, published between January 1994 and April 2019, that assessed NIV efficacy in patients with OHS and COPD. The main outcomes were diurnal PaCO2, PaO2 and NIV adherence (PROSPERO CRD42019132398).

FINDINGS : Of 1576 articles identified, 34 RCTs met the inclusion criteria and IPD were obtained for 18. Ten RCTs were excluded because only one type of mask was used, or mask data were missing. Data from 8 RCTs, including 290 IPD, underwent meta-analysis. Oronasal masks were used in 86% of cases. There were no differences between oronasal and nasal masks for PaCO2 (0.61 mm Hg (95% CI -2.15 to 3.38); p=0.68), PaO2 (-0.00 mm Hg (95% CI -4.59 to 4.58); p=1) or NIV adherence (0·29 hour/day (95% CI -0.74 to 1.32); p=0.58). There was no interaction between the underlying pathology and the effect of mask type on any outcome.

INTERPRETATION : Oronasal masks are the most used interface for the delivery of home NIV in patients with OHS and COPD; however, there is no difference in the efficacy or tolerance of oronasal or nasal masks.

Lebret Marius, Léotard Antoine, Pépin Jean Louis, Windisch Wolfram, Ekkernkamp Emelie, Pallero Mercedes, Sánchez-Quiroga M-Ángeles, Hart Nicholas, Kelly Julia L, Patout Maxime, Funk Georg Chistian, Duiverman Marieke L, Masa Juan F, Simonds Anita, Murphy Patrick Brian, Wijkstra Peter J, Dreher Michael, Storre Jan, Khouri Charles, Borel Jean-Christian

2021-Apr-15

COPD pathology, non invasive ventilation, sleep apnoea

Surgery Surgery

HLA Ligand Atlas: a benign reference of HLA-presented peptides to improve T-cell-based cancer immunotherapy.

In Journal for immunotherapy of cancer

BACKGROUND : The human leucocyte antigen (HLA) complex controls adaptive immunity by presenting defined fractions of the intracellular and extracellular protein content to immune cells. Understanding the benign HLA ligand repertoire is a prerequisite to define safe T-cell-based immunotherapies against cancer. Due to the poor availability of benign tissues, if available, normal tissue adjacent to the tumor has been used as a benign surrogate when defining tumor-associated antigens. However, this comparison has proven to be insufficient and even resulted in lethal outcomes. In order to match the tumor immunopeptidome with an equivalent counterpart, we created the HLA Ligand Atlas, the first extensive collection of paired HLA-I and HLA-II immunopeptidomes from 227 benign human tissue samples. This dataset facilitates a balanced comparison between tumor and benign tissues on HLA ligand level.

METHODS : Human tissue samples were obtained from 16 subjects at autopsy, five thymus samples and two ovary samples originating from living donors. HLA ligands were isolated via immunoaffinity purification and analyzed in over 1200 liquid chromatography mass spectrometry runs. Experimentally and computationally reproducible protocols were employed for data acquisition and processing.

RESULTS : The initial release covers 51 HLA-I and 86 HLA-II allotypes presenting 90,428 HLA-I- and 142,625 HLA-II ligands. The HLA allotypes are representative for the world population. We observe that immunopeptidomes differ considerably between tissues and individuals on source protein and HLA-ligand level. Moreover, we discover 1407 HLA-I ligands from non-canonical genomic regions. Such peptides were previously described in tumors, peripheral blood mononuclear cells (PBMCs), healthy lung tissues and cell lines. In a case study in glioblastoma, we show that potential on-target off-tumor adverse events in immunotherapy can be avoided by comparing tumor immunopeptidomes to the provided multi-tissue reference.

CONCLUSION : Given that T-cell-based immunotherapies, such as CAR-T cells, affinity-enhanced T cell transfer, cancer vaccines and immune checkpoint inhibition, have significant side effects, the HLA Ligand Atlas is the first step toward defining tumor-associated targets with an improved safety profile. The resource provides insights into basic and applied immune-associated questions in the context of cancer immunotherapy, infection, transplantation, allergy and autoimmunity. It is publicly available and can be browsed in an easy-to-use web interface at https://hla-ligand-atlas.org .

Marcu Ana, Bichmann Leon, Kuchenbecker Leon, Kowalewski Daniel Johannes, Freudenmann Lena Katharina, Backert Linus, Mühlenbruch Lena, Szolek András, Lübke Maren, Wagner Philipp, Engler Tobias, Matovina Sabine, Wang Jian, Hauri-Hohl Mathias, Martin Roland, Kapolou Konstantina, Walz Juliane Sarah, Velz Julia, Moch Holger, Regli Luca, Silginer Manuela, Weller Michael, Löffler Markus W, Erhard Florian, Schlosser Andreas, Kohlbacher Oliver, Stevanović Stefan, Rammensee Hans-Georg, Neidert Marian Christoph

2021-Apr

adaptive immunity, antigen presentation, antigens, carbohydrate, immunotherapy, translational medical research, tumor-associated

Surgery Surgery

Application of Federated Learning for Thyroid Ultrasound Image Analysis to Protect Personal Information: Validation Study in Real Healthcare Environment.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : Federated learning (FL) is a decentralized approach to machine learning which is attracting attention as a training strategy that overcomes medical data privacy regulations and generalization of deep learning algorithms. FL mitigates many systemic privacy risks by sharing only the model and parameters for training, without the need to export existing medical datasets. In this study, we performed ultrasound (US) image analysis using FL to predict the benignity or malignancy of thyroid nodules.

OBJECTIVE : The goal of this study was to evaluate whether the performance of FL is comparable with that of conventional deep learning.

METHODS : A total of 8,457 (5,375 malignant, 3,082 benign) US images were collected from six institutions, and used for FL and conventional deep learning. Five deep learning networks (VGG19, ResNet50, ResNext50, SE-ResNet50, and SE-ResNext50) were used. Using stratified random sampling, we selected 20% (1,075 malignant, 616 benign) of the total images for internal validation. For external validation, we used 100 US images (50 malignant, 50 benign) from another institution.

RESULTS : For internal validation, the area under the receiver operating characteristic (AUROC) curve of the FL was between 78.88 and 87.56, and the AUC of the conventional deep learning was between 82.61 and 91.57. For external validation, the AUROC of the FL was between 75.20 and 86.72, and the AUROC curve of the conventional deep learning was between 73.04 and 91.04.

CONCLUSIONS : We demonstrated the performance of FL using decentralized data was comparable to that of conventional deep learning using pooled data. FL might be potentially useful for analyzing medical images while protecting patients' personal information.

Lee Haeyun, Chai Young Jun, Joo Hyunjin, Lee Kyungsu, Hwang Jae Youn, Kim Seok-Mo, Kim Kwangsoon, Nam Inn-Chul, Choi June Young, Yu Hyeong Won, Lee Myung-Chul, Masuoka Hiroo, Miyauchi Akira, Lee Kyu Eun, Kim Sungwan, Kong Hyoun-Joong

2021-Apr-03

General General

Long-term mortality risk stratification of liver transplant recipients: real-time application of deep learning algorithms on longitudinal data.

In The Lancet. Digital health

BACKGROUND : Survival of liver transplant recipients beyond 1 year since transplantation is compromised by an increased risk of cancer, cardiovascular events, infection, and graft failure. Few clinical tools are available to identify patients at risk of these complications, which would flag them for screening tests and potentially life-saving interventions. In this retrospective analysis, we aimed to assess the ability of deep learning algorithms of longitudinal data from two prospective cohorts to predict complications resulting in death after liver transplantation over multiple timeframes, compared with logistic regression models.

METHODS : In this machine learning analysis, model development was done on a set of 42 146 liver transplant recipients (mean age 48·6 years [SD 17·3]; 17 196 [40·8%] women) from the Scientific Registry of Transplant Recipients (SRTR) in the USA. Transferability of the model was further evaluated by fine-tuning on a dataset from the University Health Network (UHN) in Canada (n=3269; mean age 52·5 years [11·1]; 1079 [33·0%] women). The primary outcome was cause of death, as recorded in the databases, due to cardiovascular causes, infection, graft failure, or cancer, within 1 year and 5 years of each follow-up examination after transplantation. We compared the performance of four deep learning models against logistic regression, assessing performance using the area under the receiver operating characteristic curve (AUROC).

FINDINGS : In both datasets, deep learning models outperformed logistic regression, with the Transformer model achieving the highest AUROCs in both datasets (p<0·0001). The AUROC for the Transformer model across all outcomes in the SRTR dataset was 0·804 (99% CI 0·795-0·854) for 1-year predictions and 0·733 (0·729-0·769) for 5-year predictions. In the UHN dataset, the AUROC for the top-performing deep learning model was 0·807 (0·795-0·842) for 1-year predictions and 0·722 (0·705-0·764) for 5-year predictions. AUROCs ranged from 0·695 (0·680-0·713) for prediction of death from infection within 5 years to 0·859 (0·847-0·871) for prediction of death by graft failure within 1 year.

INTERPRETATION : Deep learning algorithms can incorporate longitudinal information to continuously predict long-term outcomes after liver transplantation, outperforming logistic regression models. Physicians could use these algorithms at routine follow-up visits to identify liver transplant recipients at risk for adverse outcomes and prevent these complications by modifying management based on ranked features.

FUNDING : Canadian Donation and Transplant Research Program, CIFAR AI Chairs Program.

Nitski Osvald, Azhie Amirhossein, Qazi-Arisar Fakhar Ali, Wang Xueqi, Ma Shihao, Lilly Leslie, Watt Kymberly D, Levitsky Josh, Asrani Sumeet K, Lee Douglas S, Rubin Barry B, Bhat Mamatha, Wang Bo

2021-Apr-09

Surgery Surgery

A deep learning model for the classification of indeterminate lung carcinoma in biopsy whole slide images.

In Scientific reports ; h5-index 158.0

The differentiation between major histological types of lung cancer, such as adenocarcinoma (ADC), squamous cell carcinoma (SCC), and small-cell lung cancer (SCLC) is of crucial importance for determining optimum cancer treatment. Hematoxylin and Eosin (H&E)-stained slides of small transbronchial lung biopsy (TBLB) are one of the primary sources for making a diagnosis; however, a subset of cases present a challenge for pathologists to diagnose from H&E-stained slides alone, and these either require further immunohistochemistry or are deferred to surgical resection for definitive diagnosis. We trained a deep learning model to classify H&E-stained Whole Slide Images of TBLB specimens into ADC, SCC, SCLC, and non-neoplastic using a training set of 579 WSIs. The trained model was capable of classifying an independent test set of 83 challenging indeterminate cases with a receiver operator curve area under the curve (AUC) of 0.99. We further evaluated the model on four independent test sets-one TBLB and three surgical, with combined total of 2407 WSIs-demonstrating highly promising results with AUCs ranging from 0.94 to 0.99.

Kanavati Fahdi, Toyokawa Gouji, Momosaki Seiya, Takeoka Hiroaki, Okamoto Masaki, Yamazaki Koji, Takeo Sadanori, Iizuka Osamu, Tsuneki Masayuki

2021-Apr-14

General General

Efficient few-shot machine learning for classification of EBSD patterns.

In Scientific reports ; h5-index 158.0

Deep learning is quickly becoming a standard approach to solving a range of materials science objectives, particularly in the field of computer vision. However, labeled datasets large enough to train neural networks from scratch can be challenging to collect. One approach to accelerating the training of deep learning models such as convolutional neural networks is the transfer of weights from models trained on unrelated image classification problems, commonly referred to as transfer learning. The powerful feature extractors learned previously can potentially be fine-tuned for a new classification problem without hindering performance. Transfer learning can also improve the results of training a model using a small amount of data, known as few-shot learning. Herein, we test the effectiveness of a few-shot transfer learning approach for the classification of electron backscatter diffraction (EBSD) pattern images to six space groups within the [Formula: see text] point group. Training history and performance metrics are compared with a model of the same architecture trained from scratch. In an effort to make this approach more explainable, visualization of filters, activation maps, and Shapley values are utilized to provide insight into the model's operations. The applicability to real-world phase identification and differentiation is demonstrated using dual phase materials that are challenging to analyze with traditional methods.

Kaufmann Kevin, Lane Hobson, Liu Xiao, Vecchio Kenneth S

2021-Apr-14

General General

Moving towards accurate and early prediction of language delay with network science and machine learning approaches.

In Scientific reports ; h5-index 158.0

Due to wide variability of typical language development, it has been historically difficult to distinguish typical and delayed trajectories of early language growth. Improving our understanding of factors that signal language disorder and delay has the potential to improve the lives of the millions with developmental language disorder (DLD). We develop predictive models of low language (LL) outcomes by analyzing parental report measures of early language skill using machine learning and network science approaches. We harmonized two longitudinal datasets including demographic and standardized measures of early language skills (the MacArthur-Bates Communicative Developmental Inventories; MBCDI) as well as a later measure of LL. MBCDI data was used to calculate several graph-theoretic measures of lexico-semantic structure in toddlers' expressive vocabularies. We use machine-learning techniques to construct predictive models with these datasets to identify toddlers who will have later LL outcomes at preschool and school-age. This approach yielded robust and reliable predictions of later LL outcome with classification accuracies in single datasets exceeding 90%. Generalization performance between different datasets was modest due to differences in outcome ages and diagnostic measures. Grammatical and lexico-semantic measures ranked highly in predictive classification, highlighting promising avenues for early screening and delineating the roots of language disorders.

Borovsky Arielle, Thal Donna, Leonard Laurence B

2021-Apr-14

General General

How machine learning is embedded to support clinician decision making: an analysis of FDA-approved medical devices.

In BMJ health & care informatics

OBJECTIVE : To examine how and to what extent medical devices using machine learning (ML) support clinician decision making.

METHODS : We searched for medical devices that were (1) approved by the US Food and Drug Administration (FDA) up till February 2020; (2) intended for use by clinicians; (3) in clinical tasks or decisions and (4) used ML. Descriptive information about the clinical task, device task, device input and output, and ML method were extracted. The stage of human information processing automated by ML-based devices and level of autonomy were assessed.

RESULTS : Of 137 candidates, 59 FDA approvals for 49 unique devices were included. Most approvals (n=51) were since 2018. Devices commonly assisted with diagnostic (n=35) and triage (n=10) tasks. Twenty-three devices were assistive, providing decision support but left clinicians to make important decisions including diagnosis. Twelve automated the provision of information (autonomous information), such as quantification of heart ejection fraction, while 14 automatically provided task decisions like triaging the reading of scans according to suspected findings of stroke (autonomous decisions). Stages of human information processing most automated by devices were information analysis, (n=14) providing information as an input into clinician decision making, and decision selection (n=29), where devices provide a decision.

CONCLUSION : Leveraging the benefits of ML algorithms to support clinicians while mitigating risks, requires a solid relationship between clinician and ML-based devices. Such relationships must be carefully designed, considering how algorithms are embedded in devices, the tasks supported, information provided and clinicians' interactions with them.

Lyell David, Coiera Enrico, Chen Jessica, Shah Parina, Magrabi Farah

2021-Apr

medical informatics

Dermatology Dermatology

Impact of DSMES app interventions on medication adherence in type 2 diabetes mellitus: systematic review and meta-analysis.

In BMJ health & care informatics

OBJECTIVES : To conduct systematic review and meta-analysis of interventional studies to investigate the impact of diabetes self-management education and support (DSMES) apps on adherence in patients with type 2 diabetes mellitus (T2D).

METHODS : PubMed, Embase, CENTRAL, Web of Science, Scopus and ProQuest were searched, in addition to references of identified articles and similar reviews. Experimental studies, reported in English, assessing DSMES app intervention's impact on adherence and clinical outcomes of patients with T2D compared with usual care were included. Study bias was assessed using Cochrane Risk of Bias V.2.0 tool. Analysis plan involved narrative synthesis, moderator and meta-analysis.

RESULTS : Six randomised controlled trials were included, involving 696 participants (average age 57.6 years, SD 10.59). Mobile apps were mostly used for imputing clinical data, dietary intake or physical activity, and transmitting information to the provider. At 3 months, DSMES apps proved effective in improving medication adherence (standardized mean difference (SMD)=0.393, 95% CI 0.17 to 0.61), glycated haemoglobin (HbA1c) (mean difference (MD)=-0.314, 95% CI -0.477 to -0.151) and Body Mass Index (BMI) (MD=-0.28, 95% CI -0.545 to -0.015). All pooled estimates had low heterogeneity (I2 0%). Four studies had moderate risk of bias while one each was judged to be low and high risks, respectively.

CONCLUSION : DSMES apps had significant small to moderate effects on medication adherence, HbA1c and BMI of patients with T2D compared with usual care. Apps were described as reliable, easy to use and convenient, though participants were required to be phone literate. Evidence comes from feasibility trials with generally moderate risk of bias. Larger trials with longer follow-up periods using theory-based interventions are required to improve current evidence.

Enricho Nkhoma Dumisani, Jenya Soko Charles, Joseph Banda Kondwani, Greenfield David, Li Yu-Chuan Jack, Iqbal Usman

2021-Apr

patient care, primary health care, public health

General General

Faithful and Plausible Explanations of Medical Code Predictions

ArXiv Preprint

Machine learning models that offer excellent predictive performance often lack the interpretability necessary to support integrated human machine decision-making. In clinical medicine and other high-risk settings, domain experts may be unwilling to trust model predictions without explanations. Work in explainable AI must balance competing objectives along two different axes: 1) Explanations must balance faithfulness to the model's decision-making with their plausibility to a domain expert. 2) Domain experts desire local explanations of individual predictions and global explanations of behavior in aggregate. We propose to train a proxy model that mimics the behavior of the trained model and provides fine-grained control over these trade-offs. We evaluate our approach on the task of assigning ICD codes to clinical notes to demonstrate that explanations from the proxy model are faithful and replicate the trained model behavior.

Zach Wood-Doughty, Isabel Cachola, Mark Dredze

2021-04-16

Radiology Radiology

An Insight of the First Community Infected COVID-19 Patient in Beijing by Imported Case: Role of Deep Learning-Assisted CT Diagnosis.

In Chinese medical sciences journal = Chung-kuo i hsueh k'o hsueh tsa chih

In the era of coronavirus disease 2019 (COVID-19) pandemic, imported COVID-19 cases pose great challenges to many countries. Chest CT examination is considered to be complementary to nucleic acid test for COVID-19 detection and diagnosis. We report the first community infected COVID-19 patient by an imported case in Beijing, which manifested as nodular lesions on chest CT imaging at the early stage. Deep Learning (DL)-based diagnostic systems quantitatively monitored the progress of pulmonary lesions in 6 days and timely made alert for suspected pneumonia, so that prompt medical isolation was taken. The patient was confirmed as COVID-19 case after nucleic acid test, for which the community transmission was prevented timely. The roles of DL-assisted diagnosis in helping radiologists screening suspected COVID cases were discussed.

Li Da Sheng, Wang Da Wei, Wang Na Na, Xu Hai Wang, Huang He, Dong Jian Ping, Xia Chen

2021-Mar-31

Surgery Surgery

Split Learning Meets Koopman Theory for Wireless Remote Monitoring and Prediction

ArXiv Preprint

Remote state monitoring over wireless is envisaged to play a pivotal role in enabling beyond 5G applications ranging from remote drone control to remote surgery. One key challenge is to identify the system dynamics that is non-linear with a large dimensional state. To obviate this issue, in this article we propose to train an autoencoder whose encoder and decoder are split and stored at a state sensor and its remote observer, respectively. This autoencoder not only decreases the remote monitoring payload size by reducing the state representation dimension, but also learns the system dynamics by lifting it via a Koopman operator, thereby allowing the observer to locally predict future states after training convergence. Numerical results under a non-linear cart-pole environment demonstrate that the proposed split learning of a Koopman autoencoder can locally predict future states, and the prediction accuracy increases with the representation dimension and transmission power.

Abanoub M. Girgis, Hyowoon Seo, Jihong Park, Mehdi Bennis, Jinho Choi

2021-04-16

General General

A prediction model using machine-learning algorithm for assessing intrathecal hyperbaric bupivacaine dose during cesarean section.

In BMC anesthesiology ; h5-index 31.0

BACKGROUND : The intrathecal hyperbaric bupivacaine dosage for cesarean section is difficult to predetermine. This study aimed to develop a decision-support model using a machine-learning algorithm for assessing intrathecal hyperbaric bupivacaine dose based on physical variables during cesarean section.

METHODS : Term parturients presenting for elective cesarean section under spinal anaesthesia were enrolled. Spinal anesthesia was performed at the L3/4 interspace with 0.5% hyperbaric bupivacaine at dosages determined by the anesthesiologist. A spinal spread level between T4-T6 was considered the appropriate block level. We used a machine-learning algorithm to identify relevant parameters. The dataset was split into derivation (80%) and validation (20%) cohorts. A decision-support model was developed for obtaining the regression equation between optimized intrathecal 0.5% hyperbaric bupivacaine volume and physical variables.

RESULTS : A total of 684 parturients were included, of whom 516 (75.44%) and 168 (24.56%) had block levels between T4 and T6, and less than T6 or higher than T4, respectively. The appropriate block level rate was 75.44%, with the mean bupivacaine volume [1.965, 95%CI (1.945,1.984)]ml. In lasso regression, based on the principle of predicting a reasonable dose of intrathecal bupivacaine with fewer physical variables, the model is "Y=0.5922+ 0.055117* X1-0.017599*X2" (Y: bupivacaine volume; X1: vertebral column length; X2: abdominal girth), with λ 0.055, MSE 0.0087, and R2 0.807.

CONCLUSIONS : After applying a machine-learning algorithm, we developed a decision model with R2 0.8070 and MSE due to error 0.0087 using abdominal girth and vertebral column length for predicting the optimized intrathecal 0.5% hyperbaric bupivacaine dosage during term cesarean sections.

Wei Chang-Na, Wang Li-Ying, Chang Xiang-Yang, Zhou Qing-He

2021-Apr-14

Bupivacaine dosage, Cesarean section, Machine learning algorithm, Physical variables, Spinal anesthesia

General General

MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networks

ArXiv Preprint

Explainable AI (XAI) is a research area whose objective is to increase trustworthiness and to enlighten the hidden mechanism of opaque machine learning techniques. This becomes increasingly important in case such models are applied to the chemistry domain, for its potential impact on humans' health, e.g, toxicity analysis in pharmacology. In this paper, we present a novel approach to tackle explainability of deep graph networks in the context of molecule property prediction t asks, named MEG (Molecular Explanation Generator). We generate informative counterfactual explanations for a specific prediction under the form of (valid) compounds with high structural similarity and different predicted properties. Given a trained DGN, we train a reinforcement learning based generator to output counterfactual explanations. At each step, MEG feeds the current candidate counterfactual into the DGN, collects the prediction and uses it to reward the RL agent to guide the exploration. Furthermore, we restrict the action space of the agent in order to only keep actions that maintain the molecule in a valid state. We discuss the results showing how the model can convey non-ML experts with key insights into the learning model focus in the neighbourhood of a molecule.

Danilo Numeroso, Davide Bacciu

2021-04-16

General General

The shape of low-concentration dose-response functions for benzene: implications for human health risk assessment.

In Critical reviews in toxicology

Are dose-response relationships for benzene and health effects such as myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) supra-linear, with disproportionately high risks at low concentrations, e.g. below 1 ppm? To investigate this hypothesis, we apply recent mode of action (MoA) and mechanistic information and modern data science techniques to quantify air benzene-urinary metabolite relationships in a previously studied data set for Tianjin, China factory workers. We find that physiologically based pharmacokinetics (PBPK) models and data for Tianjin workers show approximately linear production of benzene metabolites for air benzene (AB) concentrations below about 15 ppm, with modest sublinearity at low concentrations (e.g. below 5 ppm). Analysis of the Tianjin worker data using partial dependence plots reveals that production of metabolites increases disproportionately with increases in air benzene (AB) concentrations above 10 ppm, exhibiting steep sublinearity (J shape) before becoming saturated. As a consequence, estimated cumulative exposure is not an adequate basis for predicting risk. Risk assessments must consider the variability of exposure concentrations around estimated exposure concentrations to avoid over-estimating risks at low concentrations. The same average concentration for a specified duration is disproportionately risky if it has higher variance. Conversely, if chronic inflammation via activation of inflammasomes is a critical event for induction of MDS and other health effects, then sufficiently low concentrations of benzene are predicted not to cause increased risks of inflammasome-mediated diseases, no matter how long the duration of exposure. Thus, we find no evidence that the dose-response relationship is supra-linear at low doses; instead sublinear or zero excess risk at low concentrations is more consistent with the data. A combination of physiologically based pharmacokinetic (PBPK) modeling, Bayesian network (BN) analysis and inference, and partial dependence plots appears a promising and practical approach for applying current data science methods to advance benzene risk assessment.

Cox Louis A, Ketelslegers Hans B, Lewis R Jeffrey

2021-Apr-15

Bayesian networks, Benzene metabolism, Chinese workers, PBPK model, machine learning, supralinearity

General General

T-LEAP: occlusion-robust pose estimation of walking cows using temporal information

ArXiv Preprint

As herd size on dairy farms continue to increase, automatic health monitoring of cows has gained in interest. Lameness, a prevalent health disorder in dairy cows, is commonly detected by analyzing the gait of cows. A cow's gait can be tracked in videos using pose estimation models because models learn to automatically localize anatomical landmarks in images and videos. Most animal pose estimation models are static, that is, videos are processed frame by frame and do not use any temporal information. In this work, a static deep-learning model for animal-pose-estimation was extended to a temporal model that includes information from past frames. We compared the performance of the static and temporal pose estimation models. The data consisted of 1059 samples of 4 consecutive frames extracted from videos (30 fps) of 30 different dairy cows walking through an outdoor passageway. As farm environments are prone to occlusions, we tested the robustness of the static and temporal models by adding artificial occlusions to the videos. The experiments showed that, on non-occluded data, both static and temporal approaches achieved a Percentage of Correct Keypoints (PCKh@0.2) of 99%. On occluded data, our temporal approach outperformed the static one by up to 32.9%, suggesting that using temporal data is beneficial for pose estimation in environments prone to occlusions, such as dairy farms. The generalization capabilities of the temporal model was evaluated by testing it on data containing unknown cows (cows not present in the training set). The results showed that the average detection rate (PCKh@0.2) was of 93.8% on known cows and 87.6% on unknown cows, indicating that the model is capable of generalizing well to new cows and that they could be easily fine-tuned to new herds. Finally, we showed that with harder tasks, such as occlusions and unknown cows, a deeper architecture was more beneficial.

Helena Russello, Rik van der Tol, Gert Kootstra

2021-04-16

Radiology Radiology

e-ASPECTS software improves interobserver agreement and accuracy of interpretation of aspects score.

In Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences

INTRODUCTION : There is increased interest in the use of artificial intelligence-based (AI) software packages in the evaluation of neuroimaging studies for acute ischemic stroke. We studied whether, compared to standard image interpretation without AI, Brainomix e-ASPECTS software improved interobserver agreement and accuracy in detecting ASPECTS regions affected in anterior circulation LVO.

METHODS : We included 60 consecutive patients with anterior circulation LVO who had TICI 3 revascularization within 60 minutes of their baseline CT. A total of 16 readers, including senior neuroradiologists, junior neuroradiologists and vascular neurologists participated. Readers interpreted CT scans on independent workstations and assessed final ASPECTS and evaluated whether each individual ASPECTS region was affected. Two months later, readers again evaluated the CT scans, but with assistance of e-ASPECTS software. We assessed interclass correlation coefficient for total ASPECTS and interobserver agreement with Fleiss' Kappa for each ASPECTS region with and without assistance of the e-ASPECTS. We also assessed accuracy for the readers with and without e-ASPECTS assistance. In our assessment of accuracy, ground truth was the 24 hour CT in this cohort of patients who had prompt and complete revascularization.

RESULTS : Interclass correlation coefficient for total ASPECTS without e-ASPECTS assistance was 0.395, indicating fair agreement compared, to 0.574 with e-ASPECTS assistance, indicating good agreement (P < 0.01). There was significant improvement in inter-rater agreement with e-ASPECTS assistance for each individual region with the exception of M6 and caudate. The e-ASPECTS software had higher accuracy than the overall cohort of readers (with and without e-ASPECTS assistance) for every region except the caudate.

CONCLUSIONS : Use of Brainomix e-ASPECTS software resulted in significant improvements in inter-rater agreement and accuracy of ASPECTS score evaluation in a large group of neuroradiologists and neurologists. e-ASPECTS software was more predictive of final infarct/ASPECTS than the overall group interpreting the CT scans with and without e-ASPECTS assistance.

Brinjikji Waleed, Abbasi Mehdi, Arnold Catherine, Benson John C, Braksick Sherry A, Campeau Norbert, Carr Carrie M, Cogswell Petrice M, Klaas James P, Liebo Greta B, Little Jason T, Luetmer Patrick H, Messina Steven A, Nagelschneider Alex A, Schwartz Kara M, Wood Christopher P, Nasr Deena M, Kallmes David F

2021-Apr-14

ASPECTS, CT, Stroke, artificial intelligence, large vessel occlusion

General General

Identifying Water Stress in Chickpea Plant by Analyzing Progressive Changes in Shoot Images using Deep Learning

ArXiv Preprint

To meet the needs of a growing world population, we need to increase the global agricultural yields by employing modern, precision, and automated farming methods. In the recent decade, high-throughput plant phenotyping techniques, which combine non-invasive image analysis and machine learning, have been successfully applied to identify and quantify plant health and diseases. However, these image-based machine learning usually do not consider plant stress's progressive or temporal nature. This time-invariant approach also requires images showing severe signs of stress to ensure high confidence detections, thereby reducing this approach's feasibility for early detection and recovery of plants under stress. In order to overcome the problem mentioned above, we propose a temporal analysis of the visual changes induced in the plant due to stress and apply it for the specific case of water stress identification in Chickpea plant shoot images. For this, we have considered an image dataset of two chickpea varieties JG-62 and Pusa-372, under three water stress conditions; control, young seedling, and before flowering, captured over five months. We then develop an LSTM-CNN architecture to learn visual-temporal patterns from this dataset and predict the water stress category with high confidence. To establish a baseline context, we also conduct a comparative analysis of the CNN architecture used in the proposed model with the other CNN techniques used for the time-invariant classification of water stress. The results reveal that our proposed LSTM-CNN model has resulted in the ceiling level classification performance of \textbf{98.52\%} on JG-62 and \textbf{97.78\%} on Pusa-372 and the chickpea plant data. Lastly, we perform an ablation study to determine the LSTM-CNN model's performance on decreasing the amount of temporal session data used for training.

Shiva Azimi, Rohan Wadhawan, Tapan K. Gandhi

2021-04-16

General General

Identifying Water Stress in Chickpea Plant by Analyzing Progressive Changes in Shoot Images using Deep Learning

ArXiv Preprint

To meet the needs of a growing world population, we need to increase the global agricultural yields by employing modern, precision, and automated farming methods. In the recent decade, high-throughput plant phenotyping techniques, which combine non-invasive image analysis and machine learning, have been successfully applied to identify and quantify plant health and diseases. However, these image-based machine learning usually do not consider plant stress's progressive or temporal nature. This time-invariant approach also requires images showing severe signs of stress to ensure high confidence detections, thereby reducing this approach's feasibility for early detection and recovery of plants under stress. In order to overcome the problem mentioned above, we propose a temporal analysis of the visual changes induced in the plant due to stress and apply it for the specific case of water stress identification in Chickpea plant shoot images. For this, we have considered an image dataset of two chickpea varieties JG-62 and Pusa-372, under three water stress conditions; control, young seedling, and before flowering, captured over five months. We then develop an LSTM-CNN architecture to learn visual-temporal patterns from this dataset and predict the water stress category with high confidence. To establish a baseline context, we also conduct a comparative analysis of the CNN architecture used in the proposed model with the other CNN techniques used for the time-invariant classification of water stress. The results reveal that our proposed LSTM-CNN model has resulted in the ceiling level classification performance of \textbf{98.52\%} on JG-62 and \textbf{97.78\%} on Pusa-372 and the chickpea plant data. Lastly, we perform an ablation study to determine the LSTM-CNN model's performance on decreasing the amount of temporal session data used for training.

Shiva Azimi, Rohan Wadhawan, Tapan K. Gandhi

2021-04-16

General General

Tactile Avatar: Tactile Sensing System Mimicking Human Tactile Cognition.

In Advanced science (Weinheim, Baden-Wurttemberg, Germany)

As a surrogate for human tactile cognition, an artificial tactile perception and cognition system are proposed to produce smooth/soft and rough tactile sensations by its user's tactile feeling; and named this system as "tactile avatar". A piezoelectric tactile sensor is developed to record dynamically various physical information such as pressure, temperature, hardness, sliding velocity, and surface topography. For artificial tactile cognition, the tactile feeling of humans to various tactile materials ranging from smooth/soft to rough are assessed and found variation among participants. Because tactile responses vary among humans, a deep learning structure is designed to allow personalization through training based on individualized histograms of human tactile cognition and recording physical tactile information. The decision error in each avatar system is less than 2% when 42 materials are used to measure the tactile data with 100 trials for each material under 1.2N of contact force with 4cm s-1 of sliding velocity. As a tactile avatar, the machine categorizes newly experienced materials based on the tactile knowledge obtained from training data. The tactile sensation showed a high correlation with the specific user's tendency. This approach can be applied to electronic devices with tactile emotional exchange capabilities, as well as advanced digital experiences.

Kim Kyungsoo, Sim Minkyung, Lim Sung-Ho, Kim Dongsu, Lee Doyoung, Shin Kwonsik, Moon Cheil, Choi Ji-Woong, Jang Jae Eun

2021-Apr

P(VDF‐TrFE), machine learning, piezoelectric effect, tactile avatars

General General

Novel deep learning-based survival prediction for oral cancer by analyzing tumor-infiltrating lymphocyte profiles through CIBERSORT.

In Oncoimmunology

The tumor microenvironment (TME) within mucosal neoplastic tissue in oral cancer (ORCA) is greatly influenced by tumor-infiltrating lymphocytes (TILs). Here, a clustering method was performed using CIBERSORT profiles of ORCA data that were filtered from the publicly accessible data of patients with head and neck cancer in The Cancer Genome Atlas (TCGA) using hierarchical clustering where patients were regrouped into binary risk groups based on the clustering-measuring scores and survival patterns associated with individual groups. Based on this analysis, clinically reasonable differences were identified in 16 out of 22 TIL fractions between groups. A deep neural network classifier was trained using the TIL fraction patterns. This internally validated classifier was used on another individual ORCA dataset from the International Cancer Genome Consortium data portal, and patient survival patterns were precisely predicted. Seven common differentially expressed genes between the two risk groups were obtained. This new approach confirms the importance of TILs in the TME and provides a direction for the use of a novel deep-learning approach for cancer prognosis.

Kim Yeongjoo, Kang Ji Wan, Kang Junho, Kwon Eun Jung, Ha Mihyang, Kim Yoon Kyeong, Lee Hansong, Rhee Je-Keun, Kim Yun Hak

2021-Mar-29

Head and neck cancer, cibersort, deep learning, international cancer genome consortium, oral cancer, the cancer genome atlas, tumor microenvironment, tumor-infiltrating lymphocytes

Cardiology Cardiology

Clinical Influencing Factors of Acute Myocardial Infarction Based on Improved Machine Learning.

In Journal of healthcare engineering

At present, there is no method to predict or monitor patients with AMI, and there is no specific treatment method. In order to improve the analysis of clinical influencing factors of acute myocardial infarction, based on the machine learning algorithm, this paper uses the K-means algorithm to carry out multifactor analysis and constructs a hybrid model combined with the ART2 network. Moreover, this paper simulates and analyzes the model training process and builds a system structure model based on the KNN algorithm. After constructing the model system, this paper studies the clinical influencing factors of acute myocardial infarction and combines mathematical statistics and factor analysis to carry out statistical analysis of test results. The research results show that the system model constructed in this paper has a certain effect in the clinical analysis of acute myocardial infarction.

Du Hongwei, Feng Linxing, Xu Yan, Zhan Enbo, Xu Wei

2021

Pathology Pathology

Application of machine learning in prediction of Chemotherapy resistant of Ovarian Cancer based on Gut Microbiota.

In Journal of Cancer

Background: Ovarian cancer (OC) has the highest mortality among gynecological malignancies, and resistance to chemotherapy drugs is common. We aim to develop a machine learning approach based on gut microbiota to predict the chemotherapy resistance of OC. Methods: The study included patients diagnosed with OC by pathology and treated with platinum and paclitaxel in Shengjing Hospital of China Medical University between 2017 and 2018. Fecal samples were collected from patients, and 16S rRNA sequencing was used to analyze the differences in gut microbiota between OC patients with and without chemotherapy resistance. Nine machine learning classifiers were used to derive the chemotherapy resistance of OC from gut microbiota. Results: A total of 77 chemoresistant OC patients and 97 chemosensitive OC patients were enrolled. The gut microbiota diversity was higher in OC patients with chemotherapy resistance. There were statistically significant differences between the two groups in Shannon indexes (P <0.05) and Simpson indexes (P <0.05). Machine learning techniques can predict the chemoresistance of OC, and the random forest showed the best performance among all models. The area under the ROC curve for RF model was 0.909. Conclusions: The diversity of gut microbiota was higher in OC patients with chemotherapy resistance. Further studies are warranted to validate our findings based on machine learning techniques.

Gong Ting-Ting, He Xin-Hui, Gao Song, Wu Qi-Jun

2021

chemoresistant, gut microbiota, machine learning, ovarian cancer, random forest

General General

Recycled Clothing Classification System Using Intelligent IoT and Deep Learning with AlexNet.

In Computational intelligence and neuroscience

Recently, Internet of Things (IoT) and artificial intelligence (AI), led by machine learning and deep learning, have emerged as key technologies of the Fourth Industrial Revolution (4IR). In particular, object recognition technology using deep learning is currently being used in various fields, and thanks to the strong performance and potential of deep learning, many research groups and Information Technology (IT) companies are currently investing heavily in deep learning. The textile industry involves a lot of human resources in all processes, such as raw material collection, dyeing, processing, and sewing, and the wastage of resources and energy and increase in environmental pollution are caused by the short-term waste of clothing produced during these processes. Environmental pollution can be reduced to a great extent through the use of recycled clothing. In Korea, the utilization rate of recycled clothing is increasing, the amount of used clothing is high with the annual consumption being at $56.2 billion, but it is not properly utilized because of the manual recycling clothing collection system. It has several problems such as a closed workplace environment, workers' health, rising labor costs, and low processing speed that make it difficult to apply the existing clothing recognition technology, classified by deformation and overlapping of clothing shapes, when transporting recycled clothing to the conveyor belt. In this study, I propose a recycled clothing classification system with IoT and AI using object recognition technology to the problems. The IoT device consists of Raspberry pi and a camera, and AI uses the transfer-learned AlexNet to classify different types of clothing. As a result of this study, it was confirmed that the types of recycled clothing using artificial intelligence could be predicted and accurate classification work could be performed instead of the experience and know-how of working workers in the clothing classification worksite, which is a closed space. This will lead to the innovative direction of the recycling clothing classification work that was performed by people in the existing working worker. In other words, it is expected that standardization of necessary processes, utilization of artificial intelligence, application of automation system, various cost reduction, and work efficiency improvement will be achieved.

Noh Sun-Kuk

2021

General General

Machine Learning Can Predict Total Death After Radiofrequency Ablation in Liver Cancer Patients.

In Clinical Medicine Insights. Oncology

Objective : Over 1 million new cases of hepatocellular carcinoma (HCC) are diagnosed worldwide every year. Its prognosis remains poor, and the 5-year survival rate in all disease stages is estimated to be between 10% and 20%. Radiofrequency ablation (RFA) has become an important local treatment for liver cancer, and machine learning (ML) can provide many shortcuts for liver cancer medical research. Therefore, we explore the role of ML in predicting the total mortality of liver cancer patients undergoing RFA.

Methods : This study is a secondary analysis of public database data from 578 liver cancer patients. We used Python for ML to establish the prognosis model.

Results : The results showed that the 5 most important factors were platelet count (PLT), Alpha-fetoprotein (AFP), age, tumor size, and total bilirubin, respectively. Results of the total death model for liver cancer patients in test group: among the 5 algorithm models, the highest accuracy rate was that of gbm (0.681), followed by the Logistic algorithm (0.672); among the 5 algorithms, area under the curve (AUC) values, from high to low, were Logistic (0.738), DecisionTree (0.723), gbm (0.717), GradientBoosting (0.714), and Forest (0.693); Among the 5 algorithms, gbm had the highest precision rate (0.721), followed by the Logistic algorithm (0.714). Among the 5 algorithms, DecisionTree had the highest recall rate (0.642), followed by the GradientBoosting algorithm (0.571).

Conclusion : Machine learning can predict total death after RFA in liver cancer patients. Therefore, ML research has great potential for both personalized treatment and prognosis of liver cancer.

Tong Jianhua, Liu Panmiao, Ji Muhuo, Wang Ying, Xue Qiong, Yang Jian-Jun, Zhou Cheng-Mao

2021

Treatment algorithms, hepatocellular carcinoma, individual outcomes, patient stratification, radiofrequency ablation

Ophthalmology Ophthalmology

Corneal Biomechanical Assessment with Ultra-High-Speed Scheimpflug Imaging During Non-Contact Tonometry: A Prospective Review.

In Clinical ophthalmology (Auckland, N.Z.)

Background : In recent years, increasing interest has arisen in the application of data from corneal biomechanics in many areas of ophthalmology, particularly to assist in the detection of early corneal ectasia or ectasia susceptibility, to predict corneal response to surgical or therapeutic interventions and in glaucoma management. Technology has evolved and, recently, the Scheimpflug principle was associated with a non-contact air-puff tonometer, allowing a thorough analysis of corneal biomechanics and a biomechanically corrected intraocular pressure assessment, opening up new perspectives both in ophthalmology and in other medical areas. Data from corneal biomechanics assessment are being integrated in artificial intelligence models in order to increase its value in clinical practice.

Objective : To review the state of the art in the field of corneal biomechanics assessment with special emphasis to the technology based on ultra-high-speed Scheimpflug imaging during non-contact tonometry.

Summary : A meticulous literature review was performed until the present day. We used 136 published manuscripts as our references. Both information from healthy individuals and descriptions of possible associations with systemic diseases are described. Additionally, it exposed information regarding several fields of ocular pathology, from cornea and ocular surface through areas of refractive surgery and glaucoma until vascular and structural diseases of the chorioretinal unit.

Baptista Pedro Manuel, Ambrosio Renato, Oliveira Luis, Meneres Pedro, Beirao Joao Melo

2021

Corvis, cornea, corneal biomechanics, ultra-high speed Szcheimpflug camera

General General

The efficacy of machine learning algorithm for raw drug authentication in Coscinium fenestratum (Gaertn.) Colebr. employing a DNA barcode database.

In Physiology and molecular biology of plants : an international journal of functional plant biology

Medicinal plants are a valuable resource for traditional as well as modern medicine. Consequently huge demand has exerted a heavy strain on the existing natural resources. Due to over exploitation and unscientific collection most of the commercially traded ayurvedic plants are in the phase of depletion. Adulteration of expensive raw drugs with inferior taxa has become a common practice to meet the annual demand of the ayurvedic industry. Although there are several recommended methods for proper identification varying from the traditional taxonomic to organoleptic and physiochemical, it is difficult to authenticate ayurvedic raw drugs available in extremely dried, powdered or shredded forms. In this regard, the study addresses proper authentication and illicit trade in Coscinium fenestratum (Gaertn.) Colebr. using CBOL recommended standard barcode regions viz. nuclear ribosomal-Internally Transcribed Spacer (nrDNA- ITS), maturase K (matK), ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit (rbcL), and psbA-trnH spacer regions. Further, an integrated analytical approach employing Maximum Likelihood phylogenetic tree and Machine Learning Approach, Waikato Environment for Knowledge Analysis was employed to prove efficacy of the method. The automated species identification technique, Artificial Intelligence uses the ability of computers to build models that can receive the input data and then conduct statistical analyses which significantly reduces the human labour. Concurrently, scientific management, restoration, cultivation and conservation measures should be given utmost priority to reduce the depletion of wild resources as well as to meet the rapidly increasing demand of the herbal industries.

Unnikrishnan Remya, Sumod M, Jayaraj R, Sujanapal P, Dev Suma Arun

2021-Mar

Artificial intelligence, DNA barcoding, Machine learning algorithm, Medicinal plants, Threatened species

General General

Climate change and specialty coffee potential in Ethiopia.

In Scientific reports ; h5-index 158.0

Current climate change impact studies on coffee have not considered impact on coffee typicities that depend on local microclimatic, topographic and soil characteristics. Thus, this study aims to provide a quantitative risk assessment of the impact of climate change on suitability of five premium specialty coffees in Ethiopia. We implement an ensemble model of three machine learning algorithms to predict current and future (2030s, 2050s, 2070s, and 2090s) suitability for each specialty coffee under four Shared Socio-economic Pathways (SSPs). Results show that the importance of variables determining coffee suitability in the combined model is different from those for specialty coffees despite the climatic factors remaining more important in determining suitability than topographic and soil variables. Our model predicts that 27% of the country is generally suitable for coffee, and of this area, only up to 30% is suitable for specialty coffees. The impact modelling showed that the combined model projects a net gain in coffee production suitability under climate change in general but losses in five out of the six modelled specialty coffee growing areas. We conclude that depending on drivers of suitability and projected impacts, climate change will significantly affect the Ethiopian speciality coffee sector and area-specific adaptation measures are required to build resilience.

Chemura Abel, Mudereri Bester Tawona, Yalew Amsalu Woldie, Gornott Christoph

2021-Apr-14

General General

Mushroom data creation, curation, and simulation to support classification tasks.

In Scientific reports ; h5-index 158.0

Predicting if a set of mushrooms is edible or not corresponds to the task of classifying them into two groups-edible or poisonous-on the basis of a classification rule. To support this binary task, we have collected the largest and most comprehensive attribute based data available. In this work, we detail the creation, curation and simulation of a data set for binary classification. Thanks to natural language processing, the primary data are based on a text book for mushroom identification and contain 173 species from 23 families. While the secondary data comprise simulated or hypothetical entries that are structurally comparable to the 1987 data, it serves as pilot data for classification tasks. We evaluated different machine learning algorithms, namely, naive Bayes, logistic regression, and linear discriminant analysis (LDA), and random forests (RF). We found that the RF provided the best results with a five-fold Cross-Validation accuracy and F2-score of 1.0 ([Formula: see text], [Formula: see text]), respectively. The results of our pilot are conclusive and indicate that our data were not linearly separable. Unlike the 1987 data which showed good results using a linear decision boundary with the LDA. Our data set contains 23 families and is the largest available. We further provide a fully reproducible workflow and provide the data under the FAIR principles.

Wagner Dennis, Heider Dominik, Hattab Georges

2021-Apr-14

General General

Differences in subcortico-cortical interactions identified from connectome and microcircuit models in autism.

In Nature communications ; h5-index 260.0

The pathophysiology of autism has been suggested to involve a combination of both macroscale connectome miswiring and microcircuit anomalies. Here, we combine connectome-wide manifold learning with biophysical simulation models to understand associations between global network perturbations and microcircuit dysfunctions in autism. We studied neuroimaging and phenotypic data in 47 individuals with autism and 37 typically developing controls obtained from the Autism Brain Imaging Data Exchange initiative. Our analysis establishes significant differences in structural connectome organization in individuals with autism relative to controls, with strong between-group effects in low-level somatosensory regions and moderate effects in high-level association cortices. Computational models reveal that the degree of macroscale anomalies is related to atypical increases of recurrent excitation/inhibition, as well as subcortical inputs into cortical microcircuits, especially in sensory and motor areas. Transcriptomic association analysis based on postmortem datasets identifies genes expressed in cortical and thalamic areas from childhood to young adulthood. Finally, supervised machine learning finds that the macroscale perturbations are associated with symptom severity scores on the Autism Diagnostic Observation Schedule. Together, our analyses suggest that atypical subcortico-cortical interactions are associated with both microcircuit and macroscale connectome differences in autism.

Park Bo-Yong, Hong Seok-Jun, Valk Sofie L, Paquola Casey, Benkarim Oualid, Bethlehem Richard A I, Di Martino Adriana, Milham Michael P, Gozzi Alessandro, Yeo B T Thomas, Smallwood Jonathan, Bernhardt Boris C

2021-04-13

General General

Leveraging Genomic Associations in Precision Digital Care for Weight Loss: Cohort Study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : In this age of global COVID-19 pandemic, the urgency of addressing an epidemic of obesity and associated inflammatory illnesses has come to the fore. Studies have demonstrated that interactions between single nucleotide polymorphisms (SNPs) and lifestyle interventions like food and exercise may vary metabolic outcomes, contributing to obesity and therapeutic response. However, there is a paucity of research relating outcomes from digital therapeutics to inclusion of genetic data in care interventions.

OBJECTIVE : This study aims to describe and model weight loss of subjects enrolled in a precision digital weight loss program informed by machine learning analysis of subject data, including genomic. It was hypothesized that weight loss models would exhibit better fit when incorporating genomic data than utilizing demographic and engagement variables alone.

METHODS : A cohort of 393 participants enrolled in Digbi's personalized digital care program for 120 days was analyzed retrospectively. Care protocol included the use of subject data informing precision coaching by mobile app and personal coach. Two linear regression models of weight loss in this cohort (pounds lost, percentage lost) as a function of demographic and behavioral engagement variables were fit. Genomic-enhanced models were built by adding 197 SNPs from subject genomic data as predictors, then refitting, employing Lasso regression on SNPs for variable selection. Success/failure logistic regression models were also fit, with and without genomic data.

RESULTS : 72% of subjects in this cohort lost weight, while 17% maintained stable weight. 142 subjects lost 5% within 120 days. Models describe the impact of demographic and clinical factors, behavioral engagement, and genomic risk on weight loss. The addition of genomic predictors improved the mean squared error of weight loss models (pounds lost and percent) from 70 to 60 and 16 to 13 respectively. The logistic model improved pseudo R2 from 0.193 to 0.285. Gender, engagement and specific SNPs were significantly associated with weight loss. SNPs within genes involved in metabolic pathways that process food and regulate storage of fat were associated with weight loss in this cohort. This included rs17300539_G (insulin resistance, monounsaturated fat metabolism), rs2016520_C (BMI, waist circumference, cholesterol metabolism), and rs4074995_A (calcium-potassium transport, serum calcium levels). Models described greater average weight loss for subjects having more of these risk alleles. Notably, coaching for dietary modification was personalized to these genetic risks.

CONCLUSIONS : Adding genomic information in modeling outcomes of a digital precision weight loss program greatly enhanced model accuracy. Interpretable weight loss models pointed to efficacy of coaching informed by subjects' genomic risk, accompanied by active engagement of subjects in their own success. While large-scale validation is needed, our study preliminarily supports precision dietary interventions for weight loss utilizing genetic risk, with digitally delivered recommendations alongside health-coaching to improve intervention efficacy.

CLINICALTRIAL :

Sinha Ranjan, Kachru Dashyanng, Ricchetti Roshni Ray, Singh-Rambiritch Simitha, Muthukumar Karthik Marimuthu, Singaravel Vidhya, Irudayanathan Carmel, Reddy-Sinha Chandana, Junaid Imran, Sharma Garima, Airey Catherine, Francis-Lyon Patricia Alice

2021-Apr-11

Radiology Radiology

Three-Dimensional Deep Noninvasive Radiomics for the Prediction of Disease Control in Patients With Metastatic Urothelial Carcinoma treated With Immunotherapy.

In Clinical genitourinary cancer

INTRODUCTION : Immunotherapy is effective in a small percentage of patients with cancer and no reliable predictive biomarkers are currently available. Artificial Intelligence algorithms may automatically quantify radiologic characteristics associated with disease response to medical treatments.

METHODS : We investigated an innovative approach based on a 3-dimensional (3D) deep radiomics pipeline to classify visual features of chest-abdomen computed tomography (CT) scans with the aim of distinguishing disease control from progressive disease to immune checkpoint inhibitors (ICIs). Forty-two consecutive patients with metastatic urothelial cancer had progressed on first-line platinum-based chemotherapy and had baseline CT scans at immunotherapy initiation. The 3D-pipeline included self-learned visual features and a deep self-attention mechanism. According to the outcome to the ICIs, a 3D deep classifier semiautomatically categorized the most discriminative region of interest on the CT scans.

RESULTS : With a median follow-up of 13.3 months (95% CI, 11.1-15.6), the median overall survival was 8.5 months (95% CI, 3.1-13.8). According to disease response to immunotherapy, the median overall survival was 3.6 months (95% CI, 2.0-5.2) for patients with progressive disease; it was not yet reached for those with disease control. The predictive accuracy of the 3D-pipeline was 82.5% (sensitivity 96%; specificity, 60%). The addition of baseline clinical factors increased the accuracy to 92.5% by improving specificity to 87%; the accuracy of other architectures ranged from 72.5% to 90%.

CONCLUSION : Artificial Intelligence by 3D deep radiomics is a potential noninvasive biomarker for the prediction of disease control to ICIs in metastatic urothelial cancer and deserves validation in larger series.

Rundo Francesco, Bersanelli Melissa, Urzia Valeria, Friedlaender Alex, Cantale Ornella, Calcara Giacomo, Addeo Alfredo, Banna Giuseppe Luigi

2021-Mar-19

3D-CNN, Artificial intelligence, Deep learning, Immune-checkpoint inhibitors, Machine learning

Surgery Surgery

Voice Feature Selection to Improve Performance of Machine Learning Models for Voice Production Inversion.

In Journal of voice : official journal of the Voice Foundation

OBJECTIVE : Estimation of physiological control parameters of the vocal system from the produced voice outcome has important applications in clinical management of voice disorders . Previously we developed a simulation-based neural network for estimation of vocal fold geometry, mechanical properties, and subglottal pressure from voice outcome features that characterize the acoustics of the produced voice. The goals of this study are to (1) explore the possibility of improving the estimation accuracy of physiological control parameters by including voice outcome features characterizing vocal fold vibration; and (2) identify voice feature sets that optimize both estimation accuracy and robustness to measurement noise.

METHODS : Feedforward neural networks are trained to solve the inversion problem of estimating the physiological control parameters of a three-dimensional body-cover vocal fold model from different sets of voice outcome features that characterize the simulated voice acoustics, glottal flow, and vocal fold vibration. A sensitivity analysis is then performed to evaluate the contribution of individual voice features to the overall performance of the neural networks in estimating the physiologic control parameters.

RESULTS AND CONCLUSIONS : While including voice outcome features characterizing vocal fold vibration increases estimation accuracy, it also reduces the network's robustness to measurement noise, due to high sensitivity of network performance to voice outcome features measuring the absolute amplitudes of the glottal flow and area waveforms, which are also difficult to measure accurately in practical applications. By excluding such glottal flow-based features and replacing glottal area-based features by their normalized counterparts, we are able to significantly improve both estimation accuracy and robustness to noise. We further show that similar estimation accuracy and robustness can be achieved with an even smaller set of voice outcome features by excluding features of small sensitivity.

Zhang Zhaoyan

2021-Apr-10

Voice inversion—Vocal fold geometry—Vocal fold stiffness—Machine learning

Radiology Radiology

Automated detection and segmentation of thoracic lymph nodes from CT using 3D foveal fully convolutional neural networks.

In BMC medical imaging

BACKGROUND : In oncology, the correct determination of nodal metastatic disease is essential for patient management, as patient treatment and prognosis are closely linked to the stage of the disease. The aim of the study was to develop a tool for automatic 3D detection and segmentation of lymph nodes (LNs) in computed tomography (CT) scans of the thorax using a fully convolutional neural network based on 3D foveal patches.

METHODS : The training dataset was collected from the Computed Tomography Lymph Nodes Collection of the Cancer Imaging Archive, containing 89 contrast-enhanced CT scans of the thorax. A total number of 4275 LNs was segmented semi-automatically by a radiologist, assessing the entire 3D volume of the LNs. Using this data, a fully convolutional neuronal network based on 3D foveal patches was trained with fourfold cross-validation. Testing was performed on an unseen dataset containing 15 contrast-enhanced CT scans of patients who were referred upon suspicion or for staging of bronchial carcinoma.

RESULTS : The algorithm achieved a good overall performance with a total detection rate of 76.9% for enlarged LNs during fourfold cross-validation in the training dataset with 10.3 false-positives per volume and of 69.9% in the unseen testing dataset. In the training dataset a better detection rate was observed for enlarged LNs compared to smaller LNs, the detection rate for LNs with a short-axis diameter (SAD) ≥ 20 mm and SAD 5-10 mm being 91.6% and 62.2% (p < 0.001), respectively. Best detection rates were obtained for LNs located in Level 4R (83.6%) and Level 7 (80.4%).

CONCLUSIONS : The proposed 3D deep learning approach achieves an overall good performance in the automatic detection and segmentation of thoracic LNs and shows reasonable generalizability, yielding the potential to facilitate detection during routine clinical work and to enable radiomics research without observer-bias.

Iuga Andra-Iza, Carolus Heike, Höink Anna J, Brosch Tom, Klinder Tobias, Maintz David, Persigehl Thorsten, Baeßler Bettina, Püsken Michael

2021-Apr-13

Artificial intelligence, Computed tomography, Deep learning, Lymph nodes, Staging

General General

SINA-BERT: A pre-trained Language Model for Analysis of Medical Texts in Persian

ArXiv Preprint

We have released Sina-BERT, a language model pre-trained on BERT (Devlin et al., 2018) to address the lack of a high-quality Persian language model in the medical domain. SINA-BERT utilizes pre-training on a large-scale corpus of medical contents including formal and informal texts collected from a variety of online resources in order to improve the performance on health-care related tasks. We employ SINA-BERT to complete following representative tasks: categorization of medical questions, medical sentiment analysis, and medical question retrieval. For each task, we have developed Persian annotated data sets for training and evaluation and learnt a representation for the data of each task especially complex and long medical questions. With the same architecture being used across tasks, SINA-BERT outperforms BERT-based models that were previously made available in the Persian language.

Nasrin Taghizadeh, Ehsan Doostmohammadi, Elham Seifossadat, Hamid R. Rabiee, Maedeh S. Tahaei

2021-04-15

General General

Analytical validation of the Percepta genomic sequencing classifier; an RNA next generation sequencing assay for the assessment of Lung Cancer risk of suspicious pulmonary nodules.

In BMC cancer

BACKGROUND : Bronchoscopy is a common procedure used for evaluation of suspicious lung nodules, but the low diagnostic sensitivity of bronchoscopy often results in inconclusive results and delays in treatment. Percepta Genomic Sequencing Classifier (GSC) was developed to assist with patient management in cases where bronchoscopy is inconclusive. Studies have shown that exposure to tobacco smoke alters gene expression in airway epithelial cells in a way that indicates an increased risk of developing lung cancer. Percepta GSC leverages this idea of a molecular "field of injury" from smoking and was developed using RNA sequencing data generated from lung bronchial brushings of the upper airway. A Percepta GSC score is calculated from an ensemble of machine learning algorithms utilizing clinical and genomic features and is used to refine a patient's risk stratification.

METHODS : The objective of the analysis described and reported here is to validate the analytical performance of Percepta GSC. Analytical performance studies characterized the sensitivity of Percepta GSC test results to input RNA quantity, the potentially interfering agents of blood and genomic DNA, and the reproducibility of test results within and between processing runs and between laboratories.

RESULTS : Varying the amount of input RNA into the assay across a nominal range had no significant impact on Percepta GSC classifier results. Bronchial brushing RNA contaminated with up to 10% genomic DNA by nucleic acid mass also showed no significant difference on classifier results. The addition of blood RNA, a potential contaminant in the bronchial brushing sample, caused no change to classifier results at up to 11% contamination by RNA proportion. Percepta GSC scores were reproducible between runs, within runs, and between laboratories, varying within less than 4% of the total score range (standard deviation of 0.169 for scores on 4.57 scale).

CONCLUSIONS : The analytical sensitivity, analytical specificity, and reproducibility of Percepta GSC laboratory results were successfully demonstrated under conditions of expected day to day variation in testing. Percepta GSC test results are analytically robust and suitable for routine clinical use.

Johnson Marla K, Wu Shuyang, Pankratz Daniel G, Fedorowicz Grazyna, Anderson Jessica, Ding Jie, Wong Mei, Cao Manqiu, Babiarz Joshua, Lofaro Lori, Walsh P Sean, Kennedy Giulia C, Huang Jing

2021-Apr-13

Analytical validation, Bronchial brushing specimen, Genomic sequencing classifier, Lung lesion, Molecular diagnostic, Percepta

Public Health Public Health

Modeling the impact of public response on the COVID-19 pandemic in Ontario.

In PloS one ; h5-index 176.0

The outbreak of SARS-CoV-2 is thought to have originated in Wuhan, China in late 2019 and has since spread quickly around the world. To date, the virus has infected tens of millions of people worldwide, compelling governments to implement strict policies to counteract community spread. Federal, provincial, and municipal governments have employed various public health policies, including social distancing, mandatory mask wearing, and the closure of schools and businesses. However, the implementation of these policies can be difficult and costly, making it imperative that both policy makers and the citizenry understand their potential benefits and the risks of non-compliance. In this work, a mathematical model is developed to study the impact of social behaviour on the course of the pandemic in the province of Ontario. The approach is based upon a standard SEIRD model with a variable transmission rate that depends on the behaviour of the population. The model parameters, which characterize the disease dynamics, are estimated from Ontario COVID-19 epidemiological data using machine learning techniques. A key result of the model, following from the variable transmission rate, is the prediction of the occurrence of a second wave using the most current infection data and disease-specific traits. The qualitative behaviour of different future transmission-reduction strategies is examined, and the time-varying reproduction number is analyzed using existing epidemiological data and future projections. Importantly, the effective reproduction number, and thus the course of the pandemic, is found to be sensitive to the adherence to public health policies, illustrating the need for vigilance as the economy continues to reopen.

Eastman Brydon, Meaney Cameron, Przedborski Michelle, Kohandel Mohammad

2021

General General

Applying Personal Knowledge Graphs to Health

ArXiv Preprint

Knowledge graphs that encapsulate personal health information, or personal health knowledge graphs (PHKG), can help enable personalized health care in knowledge-driven systems. In this paper we provide a short survey of existing work surrounding the emerging paradigm of PHKGs and highlight the major challenges that remain. We find that while some preliminary exploration exists on the topic of personal knowledge graphs, development of PHKGs remains under-explored. A range of challenges surrounding the collection, linkage, and maintenance of personal health knowledge remains to be addressed to fully realize PHKGs.

Sola Shirai, Oshani Seneviratne, Deborah L. McGuinness

2021-04-15

Cardiology Cardiology

A dilated inception CNN-LSTM network for fetal heart rate estimation.

In Physiological measurement ; h5-index 36.0

OBJECTIVE : Fetal heart rate monitoring is routinely used during pregnancy and labor to assess fetal well-being. The non-invasive fetal electrocardiogram (ECG), obtained by electrodes on the maternal abdomen, is a promising alternative to standard fetal monitoring. Subtraction of the maternal ECG from the abdominal measurements results in fetal ECG signals, in which the fetal heart rate can be determined typically through R-peak detection. However, the low signal-to-noise ratio and the nonstationary nature of the fetal ECG make R-peak detection a challenging task.

APPROACH : We propose an alternative approach that instead of performing R-peak detection employs deep learning to directly determine the fetal heart rate from the extracted fetal ECG signals. We introduce a combination of dilated inception convolutional neural networks (CNN) with long short-term memory networks to capture both short-term and long-term temporal dynamics of the fetal heart rate. The robustness of the method is reinforced by a separate CNN-based classifier that estimates the reliability of the outcome.

MAIN RESULTS : Our method achieved a positive percent agreement (within 10% of the actual fetal heart rate value) of 97.3% on a dataset recorded during labor and 99.6% on set-A of the 2013 Physionet/Computing in Cardiology Challenge exceeding top-performing state-of-the-art algorithms from the literature.

SIGNIFICANCE : The proposed method can potentially improve the accuracy and robustness of fetal heart rate extraction in clinical practice.

Fotiadou Eleni, van Sloun Ruud J G, van Laar Judith O E H, Vullings Rik

2021-Apr-14

convolutional neural networks, dilated convolution, fetal electrocardiogram, fetal heart rate, long short-term memory networks, noninvasive fetal ECG

General General

Deep convolutional autoencoder for the simultaneous removal of baseline noise and baseline drift in chromatograms.

In Journal of chromatography. A

Enhancement of chromatograms, such as the reduction of baseline noise and baseline drift, is often essential to accurately detect and quantify analytes in a mixture. Current methods have been well studied and adopted for decades and have assisted researchers in obtaining reliable results. However, these methods rely on relatively simple statistics of the data (chromatograms) which in some cases result in significant information loss and inaccuracies. In this study, a deep one-dimensional convolutional autoencoder was developed that simultaneously removes baseline noise and baseline drift with minimal information loss, for a large number and great variety of chromatograms. To enable the autoencoder to denoise a chromatogram to be almost, or completely, noise-free, it was trained on data obtained from an implemented chromatogram simulator that generated 190.000 representative simulated chromatograms. The trained autoencoder was then tested and compared to some of the most widely used and well-established denoising methods on testing datasets of tens of thousands of simulated chromatograms; and then further tested and verified on real chromatograms. The results show that the developed autoencoder can successfully remove baseline noise and baseline drift simultaneously with minimal information loss; outperforming methods like Savitzky-Golay smoothing, Gaussian smoothing and wavelet smoothing for baseline noise reduction (root mean squared error of 1.094 mAU compared to 2.074 mAU, 2.394 mAU and 2.199 mAU) and Savitkzy-Golay smoothing combined with asymmetric least-squares or polynomial fitting for baseline noise and baseline drift reduction (root mean absolute error of 1.171 mAU compared to 3.397 mAU and 4.923 mAU). Evidence is presented that autoencoders can be utilized to enhance and correct chromatograms and consequently improve and alleviate downstream data analysis, with the drawback of needing a carefully implemented simulator, that generates realistic chromatograms, to train the autoencoder.

Kensert Alexander, Collaerts Gilles, Efthymiadis Kyriakos, Van Broeck Peter, Desmet Gert, Cabooter Deirdre

2021-Mar-23

Autoencoder, Baseline drift, Baseline noise, Deep learning, Denoising, Machine learning, Noise reduction

General General

Artificial intelligence-enhanced white-light colonoscopy with attention guidance predicts colorectal cancer invasion depth.

In Gastrointestinal endoscopy ; h5-index 72.0

BACKGROUND AND AIMS : Endoscopic submucosal dissection (ESD) and endoscopic mucosal resection (EMR) are applied in treating superficial colorectal neoplasms but contraindicated by deeply invasive colorectal cancer (CRC). The invasion depth of neoplasms can be examined by an automated artificial intelligence (AI) system to determine the applicability of ESD and EMR.

METHODS : A deep convolutional neural network (CNN) with tumor localization branch to guide invasion depth classification was constructed on the GoogLeNet architecture. The model was trained using 7,734 nonmagnified white-light colonoscopy (WLC) images supplemented by image augmentation, from 657 lesions labeled with histopathological analysis of invasion depth. An independent testing dataset consisting of 1,631 WLC images from 156 lesions was used to validate the model.

RESULTS : For predicting noninvasive and superficially invasive neoplasms, the model achieved an overall accuracy of 91.1% (95% confidence interval [CI], 89.6% - 92.4%), with 91.2% sensitivity (95% CI, 88.8% - 93.3%) and 91.0% specificity (95% CI, 89.0% - 92.7%) at an optimal cutoff of 0.41 and the area under the receiver operating characteristic (AUROC) of 0.970 (95% CI, 0.962 - 0.978). Inclusion of the advanced CRC data significantly increased the sensitivity in differentiating superficial neoplasms from deeply invasive early CRC to 65.3% (95% CI, 61.9% - 68.8%) with AUROC of 0.729 (95% CI, 0.699 - 0.759) similar to experienced endoscopists (0.691; 95% CI, 0.624 - 0.758).

CONCLUSION : We have developed an AI-enhanced attention-guided WLC system that differentiates noninvasive or superficially submucosal invasive neoplasms from deeply invasive CRC with high accuracy, sensitivity, and specificity.

Xiaobei Luo, Jiahao Wang, Zelong Han, Yang Yu, Zhenyu Chen, Feiyang Huang, Yumeng Xu, Jianqun Cai, Qiang Zhang, Weiguang Qiao, Chuan Ng Inn, Robby Tan, Side Liu, Hanry Yu

2021-Apr-11

artificial intelligence, colorectal cancer, invasion depth, white light colonoscopy

Public Health Public Health

High-dimensional profiling clusters asthma severity by lymphoid and non-lymphoid status.

In Cell reports ; h5-index 119.0

Clinical definitions of asthma fail to capture the heterogeneity of immune dysfunction in severe, treatment-refractory disease. Applying mass cytometry and machine learning to bronchoalveolar lavage (BAL) cells, we find that corticosteroid-resistant asthma patients cluster largely into two groups: one enriched in interleukin (IL)-4+ innate immune cells and another dominated by interferon (IFN)-γ+ T cells, including tissue-resident memory cells. In contrast, BAL cells of a healthier population are enriched in IL-10+ macrophages. To better understand cellular mediators of severe asthma, we developed the Immune Cell Linkage through Exploratory Matrices (ICLite) algorithm to perform deconvolution of bulk RNA sequencing of mixed-cell populations. Signatures of mitosis and IL-7 signaling in CD206-FcεRI+CD127+IL-4+ innate cells in one patient group, contrasting with adaptive immune response in T cells in the other, are preserved across technologies. Transcriptional signatures uncovered by ICLite identify T-cell-high and T-cell-poor severe asthma patients in an independent cohort, suggesting broad applicability of our findings.

Camiolo Matthew J, Zhou Xiaoying, Oriss Timothy B, Yan Qi, Gorry Michael, Horne William, Trudeau John B, Scholl Kathryn, Chen Wei, Kolls Jay K, Ray Prabir, Weisel Florian J, Weisel Nadine M, Aghaeepour Nima, Nadeau Kari, Wenzel Sally E, Ray Anuradha

2021-Apr-13

BAL, CyTOF, FceRI+, ICLite, IFN-g, RNA-seq, clusters, immune, multi-omics, severe asthma

General General

Intranasal vasopressin modulates resting state brain activity across multiple neural systems: evidence from a brain imaging machine learning study.

In Neuropharmacology ; h5-index 72.0

Arginine vasopressin (AVP), a neuropeptide with widespread receptors in brain regions important for socioemotional processing, is critical in regulating various mammalian social behavior and emotion. Although a growing body of task-based brain imaging studies have revealed the effects of AVP on brain activity associated with emotion processing, social cognition and behaviors, the potential modulations of AVP on resting-state brain activity remain largely unknown. Here, the current study addressed this issue by adopting a machine learning approach to distinguish administration of AVP and placebo, employing the amplitude of low-frequency fluctuation (ALFF) as a measure of resting-state brain activity. The brain regions contributing to the classification were then subjected to functional connectivity and decoding analyses, allowing for a data-driven quantitative inference on psychophysiological functions. Our results indicated that ALFF across multiple neural systems were sufficient to distinguish between AVP and placebo at individual level, with the contributing regions distributed across the social cognition network, sensorimotor regions and emotional processing network. These findings suggest that the role of AVP in socioemotional functioning recruits multiple brain networks distributed across the whole brain rather than specific localized neural pathways. Beyond these findings, the current data-driven approach also opens a novel avenue to delineate neural underpinnings of various neuropeptides or hormones.

Chen Xinling, Xu Yongbo, Li Bingjie, Wu Xiaoyan, Li Ting, Wang Li, Zhang Yijie, Lin Wanghuan, Qu Chen, Feng Chunliang

2021-Apr-11

amplitude of low-frequency fluctuation, arginine vasopressin, functional decoding, large-scale network, machine learning, resting-state fMRI

Public Health Public Health

Modeling the impact of public response on the COVID-19 pandemic in Ontario.

In PloS one ; h5-index 176.0

The outbreak of SARS-CoV-2 is thought to have originated in Wuhan, China in late 2019 and has since spread quickly around the world. To date, the virus has infected tens of millions of people worldwide, compelling governments to implement strict policies to counteract community spread. Federal, provincial, and municipal governments have employed various public health policies, including social distancing, mandatory mask wearing, and the closure of schools and businesses. However, the implementation of these policies can be difficult and costly, making it imperative that both policy makers and the citizenry understand their potential benefits and the risks of non-compliance. In this work, a mathematical model is developed to study the impact of social behaviour on the course of the pandemic in the province of Ontario. The approach is based upon a standard SEIRD model with a variable transmission rate that depends on the behaviour of the population. The model parameters, which characterize the disease dynamics, are estimated from Ontario COVID-19 epidemiological data using machine learning techniques. A key result of the model, following from the variable transmission rate, is the prediction of the occurrence of a second wave using the most current infection data and disease-specific traits. The qualitative behaviour of different future transmission-reduction strategies is examined, and the time-varying reproduction number is analyzed using existing epidemiological data and future projections. Importantly, the effective reproduction number, and thus the course of the pandemic, is found to be sensitive to the adherence to public health policies, illustrating the need for vigilance as the economy continues to reopen.

Eastman Brydon, Meaney Cameron, Przedborski Michelle, Kohandel Mohammad

2021

General General

SA-Net: A scale-attention network for medical image segmentation.

In PloS one ; h5-index 176.0

Semantic segmentation of medical images provides an important cornerstone for subsequent tasks of image analysis and understanding. With rapid advancements in deep learning methods, conventional U-Net segmentation networks have been applied in many fields. Based on exploratory experiments, features at multiple scales have been found to be of great importance for the segmentation of medical images. In this paper, we propose a scale-attention deep learning network (SA-Net), which extracts features of different scales in a residual module and uses an attention module to enforce the scale-attention capability. SA-Net can better learn the multi-scale features and achieve more accurate segmentation for different medical image. In addition, this work validates the proposed method across multiple datasets. The experiment results show SA-Net achieves excellent performances in the applications of vessel detection in retinal images, lung segmentation, artery/vein(A/V) classification in retinal images and blastocyst segmentation. To facilitate SA-Net utilization by the scientific community, the code implementation will be made publicly available.

Hu Jingfei, Wang Hua, Wang Jie, Wang Yunqi, He Fang, Zhang Jicong

2021

General General

Degradation Alignment in Remaining Useful Life Prediction Using Deep Cycle-Consistent Learning.

In IEEE transactions on neural networks and learning systems

Due to the benefits of reduced maintenance cost and increased operational safety, effective prognostic methods have always been highly demanded in real industries. In the recent years, intelligent data-driven remaining useful life (RUL) prediction approaches have been successfully developed and achieved promising performance. However, the existing methods mostly set hard RUL labels on the training data and pay less attention to the degradation pattern variations of different entities. This article proposes a deep learning-based RUL prediction method. The cycle-consistent learning scheme is proposed to achieve a new representation space, where the data of different entities in similar degradation levels can be well aligned. A first predicting time determination approach is further proposed, which facilitates the following degradation percentage estimation and RUL prediction tasks. The experimental results on a popular degradation data set suggest that the proposed method offers a novel perspective on data-driven prognostic studies and a promising tool for RUL estimations.

Li Xiang, Zhang Wei, Ma Hui, Luo Zhong, Li Xu

2021-Apr-14

General General

Deep Learning-Based 2-D Frequency Estimation of Multiple Sinusoidals.

In IEEE transactions on neural networks and learning systems

Frequency estimation of 2-D multicomponent sinusoidal signals is a fundamental issue in the statistical signal processing community that arises in various disciplines. In this article, we extend the DeepFreq model by modifying its network architecture and apply it to 2-D signals. We name the proposed framework 2-D ResFreq. Compared with the original DeepFreq framework, the 2-D convolutional implementation of the matched filtering module facilitates the transformation from time-domain signals to frequency-domain signals and reduces the number of network parameters. The additional upsampling layer and stacked residual blocks are designed to perform superresolution. Moreover, we introduce frequency amplitude information into the optimization function to improve the amplitude accuracy. After training, the signals in the test set are forward-mapped to 2-D accurate and high-resolution frequency representations. Frequency and amplitude estimation are achieved by measuring the locations and strengths of the spectral peaks. We conduct numerical experiments to demonstrate the superior performance of the proposed architecture in terms of its superresolution capability and estimation accuracy.

Pan Pingping, Zhang Yunjian, Deng Zhenmiao, Qi Wei

2021-Apr-14

General General

The virtual physiological human gets nerves! How to account for the action of the nervous system in multiphysics simulations of human organs.

In Journal of the Royal Society, Interface

This article shows how to couple multiphysics and artificial neural networks to design computer models of human organs that autonomously adapt their behaviour to environmental stimuli. The model simulates motility in the intestine and adjusts its contraction patterns to the physical properties of the luminal content. Multiphysics reproduces the solid mechanics of the intestinal membrane and the fluid mechanics of the luminal content; the artificial neural network replicates the activity of the enteric nervous system. Previous studies recommended training the network with reinforcement learning. Here, we show that reinforcement learning alone is not enough; the input-output structure of the network should also mimic the basic circuit of the enteric nervous system. Simulations are validated against in vivo measurements of high-amplitude propagating contractions in the human intestine. When the network has the same input-output structure of the nervous system, the model performs well even when faced with conditions outside its training range. The model is trained to optimize transport, but it also keeps stress in the membrane low, which is exactly what occurs in the real intestine. Moreover, the model responds to atypical variations of its functioning with 'symptoms' that reflect those arising in diseases. If the healthy intestine model is made artificially ill by adding digital inflammation, motility patterns are disrupted in a way consistent with inflammatory pathologies such as inflammatory bowel disease.

Alexiadis A, Simmons M J H, Stamatopoulos K, Batchelor H K, Moulitsas I

2021-Apr

coupling multiphysics with artificial intelligence, mathematical modelling of the intestine, multiphysics, reinforcement learning, virtual human

General General

Signatures of COVID-19 severity and immune response in the respiratory tract microbiome.

In medRxiv : the preprint server for health sciences

Rationale : Viral infection of the respiratory tract can be associated with propagating effects on the airway microbiome, and microbiome dysbiosis may influence viral disease.

Objective : To define the respiratory tract microbiome in COVID-19 and relationship disease severity, systemic immunologic features, and outcomes.

Methods and Measurements : We examined 507 oropharyngeal, nasopharyngeal and endotracheal samples from 83 hospitalized COVID-19 patients, along with non-COVID patients and healthy controls. Bacterial communities were interrogated using 16S rRNA gene sequencing, commensal DNA viruses Anelloviridae and Redondoviridae were quantified by qPCR, and immune features were characterized by lymphocyte/neutrophil (L/N) ratios and deep immune profiling of peripheral blood mononuclear cells (PBMC).

Main Results : COVID-19 patients had upper respiratory microbiome dysbiosis, and greater change over time than critically ill patients without COVID-19. Diversity at the first time point correlated inversely with disease severity during hospitalization, and microbiome composition was associated with L/N ratios and PBMC profiles in blood. Intubated patients showed patient-specific and dynamic lung microbiome communities, with prominence of Staphylococcus . Anelloviridae and Redondoviridae showed more frequent colonization and higher titers in severe disease. Machine learning analysis demonstrated that integrated features of the microbiome at early sampling points had high power to discriminate ultimate level of COVID-19 severity.

Conclusions : The respiratory tract microbiome and commensal virome are disturbed in COVID-19, correlate with systemic immune parameters, and early microbiome features discriminate disease severity. Future studies should address clinical consequences of airway dysbiosis in COVID-19, possible use as biomarkers, and role of bacterial and viral taxa identified here in COVID-19 pathogenesis.

Merenstein Carter, Liang Guanxiang, Whiteside Samantha A, Cobián-Güemes Ana G, Merlino Madeline S, Taylor Louis J, Glascock Abigail, Bittinger Kyle, Tanes Ceylan, Graham-Wooten Jevon, Khatib Layla A, Fitzgerald Ayannah S, Reddy Shantan, Baxter Amy E, Giles Josephine R, Oldridge Derek A, Meyer Nuala J, Wherry E John, McGinniss John E, Bushman Frederic D, Collman Ronald G

2021-Apr-05

General General

Piecewise-linear modelling with feature selection for Li-ion battery end of life prognosis

ArXiv Preprint

The complex nature of lithium-ion battery degradation has led to many machine learning based approaches to health forecasting being proposed in literature. However, machine learning can be computationally intensive. Linear approaches are faster but have previously been too inflexible for successful prognosis. For both techniques, the choice and quality of the inputs is a limiting factor of performance. Piecewise-linear models, combined with automated feature selection, offer a fast and flexible alternative without being as computationally intensive as machine learning. Here, a piecewise-linear approach to battery health forecasting was compared to a Gaussian process regression tool and found to perform equally well. The input feature selection process demonstrated the benefit of limiting the correlation between inputs. Further trials found that the piecewise-linear approach was robust to changing input size and availability of training data.

Samuel Greenbank, David A. Howey

2021-04-15

General General

Nonblind Image Deblurring via Deep Learning in Complex Field.

In IEEE transactions on neural networks and learning systems

Nonblind image deblurring is about recovering the latent clear image from a blurry one generated by a known blur kernel, which is an often-seen yet challenging inverse problem in imaging. Its key is how to robustly suppress noise magnification during the inversion process. Recent approaches made a breakthrough by exploiting convolutional neural network (CNN)-based denoising priors in the image domain or the gradient domain, which allows using a CNN for noise suppression. The performance of these approaches is highly dependent on the effectiveness of the denoising CNN in removing magnified noise whose distribution is unknown and varies at different iterations of the deblurring process for different images. In this article, we introduce a CNN-based image prior defined in the Gabor domain. The prior not only utilizes the optimal space-frequency resolution and strong orientation selectivity of the Gabor transform but also enables using complex-valued (CV) representations in intermediate processing for better denoising. A CV CNN is developed to exploit the benefits of the CV representations, with better generalization to handle unknown noises over the real-valued ones. Combining our Gabor-domain CV CNN-based prior with an unrolling scheme, we propose a deep-learning-based approach to nonblind image deblurring. Extensive experiments have demonstrated the superior performance of the proposed approach over the state-of-the-art ones.

Quan Yuhui, Lin Peikang, Xu Yong, Nan Yuesong, Ji Hui

2021-Apr-14

General General

Effective Collaborative Representation Learning for Multilabel Text Categorization.

In IEEE transactions on neural networks and learning systems

With the booming of deep learning, massive attention has been paid to developing neural models for multilabel text categorization (MLTC). Most of the works concentrate on disclosing word-label relationship, while less attention is taken in exploiting global clues, particularly with the relationship of document-label. To address this limitation, we propose an effective collaborative representation learning (CRL) model in this article. CRL consists of a factorization component for generating shallow representations of documents and a neural component for deep text-encoding and classification. We have developed strategies for jointly training those two components, including an alternating-least-squares-based approach for factorizing the pointwise mutual information (PMI) matrix of label-document and multitask learning (MTL) strategy for the neural component. According to the experimental results on six data sets, CRL can explicitly take advantage of the relationship of document-label and achieve competitive classification performance in comparison with some state-of-the-art deep methods.

Wu Hao, Qin Shaowei, Nie Rencan, Cao Jinde, Gorbachev Sergey

2021-Apr-14

General General

Rapid Estimation of Entire Brain Strain Using Deep Learning Models.

In IEEE transactions on bio-medical engineering

OBJECTIVE : Many recent studies have suggested that brain deformation resulting from a head impact is linked to the corresponding clinical outcome, such as mild traumatic brain injury (mTBI). Even though several finite element (FE) head models have been developed and validated to calculate brain deformation based on impact kinematics, the clinical application of these FE head models is limited due to the time-consuming nature of FE simulations. This work aims to accelerate the process of brain deformation calculation and thus improve the potential for clinical applications.

METHODS : We propose a deep learning head model with a five-layer deep neural network and feature engineering, and trained and tested the model on 2511 total head impacts from a combination of head model simulations and on-field college football and mixed martial arts impacts.

RESULTS : The proposed deep learning head model can calculate the maximum principal strain (Green Lagrange) for every element in the entire brain in less than 0.001s with an average root mean squared error of 0.022, and with a standard deviation of 0.001 over twenty repeats with random data partition and model initialization.

CONCLUSION : Trained and tested using the dataset of 2511 head impacts, this model can be applied to various sports in the calculation of brain strain with accuracy, and its applicability can even further be extended by incorporating data from other types of head impacts.

SIGNIFICANCE : In addition to the potential clinical application in real-time brain deformation monitoring, this model will help researchers estimate the brain strain from a large number of head impacts more efficiently than using FE models.

Zhan Xianghao, Liu Yuzhe, Raymond Samuel J, Vahid Alizadeh Hossein, Domel August, Gevaert Olivier, Zeineh Michael, Grant Gerald, Camarillo David Benjamin

2021-Apr-14

Pathology Pathology

Applications of Machine and Deep Learning in Adaptive Immunity.

In Annual review of chemical and biomolecular engineering

Adaptive immunity is mediated by lymphocyte B and T cells, which respectively express a vast and diverse repertoire of B cell and T cell receptors and, in conjunction with peptide antigen presentation through major histocompatibility complexes (MHCs), can recognize and respond to pathogens and diseased cells. In recent years, advances in deep sequencing have led to a massive increase in the amount of adaptive immune receptor repertoire data; additionally, proteomics techniques have led to a wealth of data on peptide-MHC presentation. These large-scale data sets are now making it possible to train machine and deep learning models, which can be used to identify complex and high-dimensional patterns in immune repertoires. This article introduces adaptive immune repertoires and machine and deep learning related to biological sequence data and then summarizes the many applications in this field, which span from predicting the immunological status of a host to the antigen specificity of individual receptors and the engineering of immunotherapeutics. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 12 is June 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Pertseva Margarita, Gao Beichen, Neumeier Daniel, Yermanos Alexander, Reddy Sai T

2021-Apr-14

Radiology Radiology

Computer-Assisted Reporting and Decision Support in Standardized Radiology Reporting for Cancer Imaging.

In JCO clinical cancer informatics

PURPOSE : Recent advances in structured reporting are providing an opportunity to enhance cancer imaging assessment to drive value-based care and improve patient safety.

METHODS : The computer-assisted reporting and decision support (CAR/DS) framework has been developed to enable systematic ingestion of guidelines as clinical decision structured reporting tools embedded within the radiologist's workflow.

RESULTS : CAR/DS tools can reduce the radiology reporting variability and increase compliance with clinical guidelines. The lung cancer use-case is used to describe various scenarios of a cancer imaging structured reporting pathway, including incidental findings, screening, staging, and restaging or continued care. Various aspects of these tools are also described using cancer-related examples for different imaging modalities and applications such as calculators. Such systems can leverage artificial intelligence (AI) algorithms to assist with the generation of structured reports and there are opportunities for new AI applications to be created using the structured data associated with CAR/DS tools.

CONCLUSION : These AI-enabled systems are starting to allow information from multiple sources to be integrated and inserted into structured reports to drive improvements in clinical decision support and patient care.

Bizzo Bernardo C, Almeida Renata R, Alkasab Tarik K

2021-Apr

General General

A new approach: information gain algorithm-based k-nearest neighbors hybrid diagnostic system for Parkinson's disease.

In Physical and engineering sciences in medicine

Parkinson's disease (PD) is a slow and insidiously progressive neurological brain disorder. The development of expert systems capable of automatically and highly accurately diagnosing early stages of PD based on speech signals would provide an important contribution to the health sector. For this purpose, the Information Gain Algorithm-based K-Nearest Neighbors (IGKNN) model was developed. This approach was applied to the feature data sets formed using the Tunable Q-factor Wavelet Transform (TQWT) method. First, 12 sub-feature data sets forming the TQWT feature group were analyzed separately after which the one with the best performance was selected, and the IGKNN model was applied to this sub-feature data set. Finally, it was observed that the performance results provided with the IGKNN system for this sub-feature data set were better than those for the complete set of data. According to the results, values of receiver operating characteristic and precision-recall curves exceeded 0.95, and a classification accuracy of almost 98% was obtained with the 22 features selected from this sub-group. In addition, the kappa coefficient was 0.933 and showed a perfect agreement between actual and predicted values. The performance of the IGKNN system was also compared with results from other studies in the literature in which the same data were used, and the approach proposed in this study far outperformed any approaches reported in the literature. Also, as in this IGKNN approach, an expert system that can diagnose PD and achieve maximum performance with fewer features from the audio signals has not been previously encountered.

Yücelbaş Cüneyt

2021-Apr-14

Artificial intelligence systems, Information gain approach, KNN, Parkinson’s disease, Speech signals

Radiology Radiology

Deep learning model for predicting gestational age after the first trimester using fetal MRI.

In European radiology ; h5-index 62.0

OBJECTIVES : To evaluate a deep learning model for predicting gestational age from fetal brain MRI acquired after the first trimester in comparison to biparietal diameter (BPD).

MATERIALS AND METHODS : Our Institutional Review Board approved this retrospective study, and a total of 184 T2-weighted MRI acquisitions from 184 fetuses (mean gestational age: 29.4 weeks) who underwent MRI between January 2014 and June 2019 were included. The reference standard gestational age was based on the last menstruation and ultrasonography measurements in the first trimester. The deep learning model was trained with T2-weighted images from 126 training cases and 29 validation cases. The remaining 29 cases were used as test data, with fetal age estimated by both the model and BPD measurement. The relationship between the estimated gestational age and the reference standard was evaluated with Lin's concordance correlation coefficient (ρc) and a Bland-Altman plot. The ρc was assessed with McBride's definition.

RESULTS : The ρc of the model prediction was substantial (ρc = 0.964), but the ρc of the BPD prediction was moderate (ρc = 0.920). Both the model and BPD predictions had greater differences from the reference standard at increasing gestational age. However, the upper limit of the model's prediction (2.45 weeks) was significantly shorter than that of BPD (5.62 weeks).

CONCLUSIONS : Deep learning can accurately predict gestational age from fetal brain MR acquired after the first trimester.

KEY POINTS : • The prediction of gestational age using ultrasound is accurate in the first trimester but becomes inaccurate as gestational age increases. • Deep learning can accurately predict gestational age from fetal brain MRI acquired in the second and third trimester. • Prediction of gestational age by deep learning may have benefits for prenatal care in pregnancies that are underserved during the first trimester.

Kojita Yasuyuki, Matsuo Hidetoshi, Kanda Tomonori, Nishio Mizuho, Sofue Keitaro, Nogami Munenobu, Kono Atsushi K, Hori Masatoshi, Murakami Takamichi

2021-Apr-14

Brain, Deep learning, Fetus, Gestational age, Pregnancy

General General

Uncertainty measurement of radiomics features against inherent quantum noise in computed tomography imaging.

In European radiology ; h5-index 62.0

OBJECTIVES : Quantum noise is a random process in X-ray-based imaging systems. We addressed and measured the uncertainty of radiomics features against this quantum noise in computed tomography (CT) images.

METHODS : A clinical multi-detector CT scanner, two homogeneous phantom sets, and four heterogeneous samples were used. A solid tumor tissue removed from a male BALB/c mouse was included. We the placed phantom sets on the CT scanning table and repeated 20 acquisitions with identical imaging settings. Regions of interest were delineated for feature extraction. Statistical quantities-average, standard deviation, and percentage uncertainty-were calculated from these 20 repeated scans. Percentage uncertainty was used to measure and quantify feature stability against quantum noise. Twelve radiomics features were measured. Random noise was added to study the robustness of machine learning classifiers against feature uncertainty.

RESULTS : We found the ranges of percentage uncertainties from homogeneous soft tissue phantoms, homogeneous bone phantoms, and solid tumor tissue to be 0.01-2138%, 0.02-15%, and 0.18-16%, respectively. Overall, it was found that the CT features ShortRunHighGrayLevelEmpha (SRHGE) (0.01-0.18%), ShortRunLowGrayLevelEmpha (SRLGE) (0.01-0.41%), LowGrayLevelRunEmpha (LGRE) (0.01-0.39%), and LongRunLowGrayLevelEmpha (LRLGE) (0.02-0.66%) were the most stable features against the inherent quantum noise. The most unstable features were cluster shade (1-2138%) and max probability (1-16%). The impact of random noise to the prediction accuracy by different machine learning classifiers was found to be between 0 and 12%.

CONCLUSIONS : Twelve features were used for uncertainty measurements. The upper and lower bounds of percentage uncertainties were determined. The quantum noise effect on machine learning classifiers is model dependent.

KEY POINTS : • Quantum noise is a random process and is intrinsic to X-ray-based imaging systems. This inherent quantum noise creates unpredictable fluctuations in the gray-level intensities of image pixels. Extra cautions and further validations are strongly recommended when unstable radiomics features are selected by a predictive model for disease classification or treatment outcome prognosis. • We addressed and used the statistical quantity of percentage uncertainty to measure the uncertainty of radiomics features against the inherent quantum noise in computed tomography (CT) images. • A clinical multi-detector CT scanner, two homogeneous phantom sets, and four heterogeneous samples were used in the stability measurement. A solid tumor tissue removed from a male BALB/c mouse was included in the heterogeneous sample.

Tu Shu-Ju, Chen Wei-Yuan, Wu Chen-Te

2021-Apr-14

Health care quality assurance, Medical informatics computing, Radiomics, Uncertainty, X-ray computed tomography

Public Health Public Health

Model and variable selection using machine learning methods with applications to childhood stunting in Bangladesh.

In Informatics for health & social care

Childhood stunting is a serious public health concern in Bangladesh. Earlier research used conventional statistical methods to identify the risk factors of stunting, and very little is known about the applications and usefulness of machine learning (ML) methods that can identify the risk factors of various health conditions based on complex data. This research evaluates the performance of ML methods in predicting stunting among under-5 aged children using 2014 Bangladesh Demographic and Health Survey data. Besides, this paper identifies variables which are important to predict stunting in Bangladesh. Among the selected ML methods, gradient boosting provides the smallest misclassification error in predicting stunting, followed by random forests, support vector machines, classification tree and logistic regression with forward-stepwise selection. The top 10 important variables (in order of importance) that better predict childhood stunting in Bangladesh are child age, wealth index, maternal education, preceding birth interval, paternal education, division, household size, maternal age at first birth, maternal nutritional status, and parental age. Our study shows that ML can support the building of prediction models and emphasizes on the demographic, socioeconomic, nutritional and environmental factors to understand stunting in Bangladesh.

Khan Jahidur Rahman, Tomal Jabed H, Raheem Enayetur

2021-Apr-14

Bangladesh, Machine learning, prediction, stunting, variable importance

General General

How to draw the line - Raman spectroscopy as a tool for the assessment of biomedicines.

In Biological chemistry

Biomedicines are complex biochemical formulations with multiple components that require extensive quality control during manufacturing and in subsequent batch testing. A proof-of-concept study has shown that an application of Raman spectroscopy can be beneficial for a classification of vaccines. However, the complexity of biomedicines introduces new challenges to spectroscopic methodology that require advanced experimental protocols. We further show the impact of analytical protocols on vaccine classification using R as an Open Source data analysis platform. In conclusion, we advocate for standardized and transparent experimental and analytical procedures and discuss current findings and open challenges.

Kamp Christel, Becker Björn, Matheis Walter, Öppling Volker, Bekeredjian-Ding Isabelle

2021-Apr-13

Raman spectroscopy, machine learning, pre-processing, quality control, standardisation, vaccine

General General

Severity analysis of road transport accidents of hazardous materials with machine learning.

In Traffic injury prevention

OBJECTIVE : The aim of this study was to explore a suitable method for analyzing road transport accidents that involve hazardous materials and to explore the main factors that influence the occurrence of accidents of varying severity.

METHODS : The 2015-2019 reported crash data from the Ministry of Transport of the People's Republic of China were obtained, and road transport crashes involving hazardous materials were extracted as the analysis data. The dataset was classified into three injury severity categories: property damage only (PDO), injured (INJ), and fatal (FAT). A statistical model and three machine learning-based models were developed: a random parameters logit model (RPLM), multilayer perceptron (MLP), decision tree C5.0 (C5.0) and support vector machine (SVM). The four models were trained/estimated using the training/estimation dataset, and the best model was used to model accidents of the three different severity levels. The main factors that influence the occurrence of accidents at each crash severity level were obtained.

RESULTS : C5.0 had the best modeling performance. The direct accident form (DAF), indirect accident form (IAF) and road segment (RS) were determined to be the critical determinants of PDO accidents. The DAF, IAF, road type, RS and time had a substantial effect on INJ accidents. The DAF, IAF, hazardous material type (HMT) and road surface condition were important factors in the occurrence of FAT accidents.

CONCLUSIONS : Different data have unique characteristics, and the best modeling and analysis method should be chosen accordingly. The safety of road transport of hazardous materials in China is poor, and the losses caused by accidents are substantial. Strengthening the monitoring of travel speed and travel time; improving driver safety awareness, driving skills and the ability to mitigate emergencies; improving the configuration of vehicle safety equipment and the linkage with the control center and rescue center; improving the environmental differences between inside a tunnel and outside a tunnel; reducing the design of long downhill and steep slope sections; reducing the transport plan in unsafe environments; and improving the ability of road management to mitigate bad environments can be effective measures to reduce the severity of road transport accidents involving hazardous materials.

Shen Xiaoyan, Wei Shanshan

2021-Apr-13

Hazardous materials, accident severities, influencing factors, machine learning, road transportation

General General

Early stage risk communication and community engagement (RCCE) strategies and measures against the coronavirus disease 2019 (COVID-19) pandemic crisis.

In Global health journal (Amsterdam, Netherlands)

Coronavirus disease 2019 (COVID-19) pandemic has proven to be tenacious and shows that the global community is still poorly prepared to handling such emerging pandemics. Enhancing global solidarity in emergency preparedness and response, and the mobilization of conscience and cooperation, can serve as an excellent source of ideas and measures in a timely manner. The article provides an overview of the key components of risk communication and community engagement (RCCE) strategies at the early stages in vulnerable nations and populations, and highlight contextual recommendations for strengthening coordinated and sustainable RCCE preventive and emergency response strategies against COVID-19 pandemic. Global solidarity calls for firming governance, abundant community participation and enough trust to boost early pandemic preparedness and response. Promoting public RCCE response interventions needs crucially improving government health systems and security proactiveness, community to individual confinement, trust and resilience solutions. To better understand population risk and vulnerability, as well as COVID-19 transmission dynamics, it is important to build intelligent systems for monitoring isolation/quarantine and tracking by use of artificial intelligence and machine learning systems algorithms. Experiences and lessons learned from the international community is crucial for emerging pandemics prevention and control programs, especially in promoting evidence-based decision-making, integrating data and models to inform effective and sustainable RCCE strategies, such as local and global safe and effective COVID-19 vaccines and mass immunization programs.

Zhang Yanjie, Tambo Ernest, Djuikoue Ingrid C, Tazemda Gildas K, Fotsing Michael F, Zhou Xiao-Nong

2021-Mar

Coronavirus disease 2019 (COVID-19), Governance, Pandemic, Response, Risk communication and community engagement (RCCE), Trust, Vaccination

General General

Do Deep Neural Networks Forget Facial Action Units? -- Exploring the Effects of Transfer Learning in Health Related Facial Expression Recognition

ArXiv Preprint

In this paper, we present a process to investigate the effects of transfer learning for automatic facial expression recognition from emotions to pain. To this end, we first train a VGG16 convolutional neural network to automatically discern between eight categorical emotions. We then fine-tune successively larger parts of this network to learn suitable representations for the task of automatic pain recognition. Subsequently, we apply those fine-tuned representations again to the original task of emotion recognition to further investigate the differences in performance between the models. In the second step, we use Layer-wise Relevance Propagation to analyze predictions of the model that have been predicted correctly previously but are now wrongly classified. Based on this analysis, we rely on the visual inspection of a human observer to generate hypotheses about what has been forgotten by the model. Finally, we test those hypotheses quantitatively utilizing concept embedding analysis methods. Our results show that the network, which was fully fine-tuned for pain recognition, indeed payed less attention to two action units that are relevant for expression recognition but not for pain recognition.

Pooja Prajod, Dominik Schiller, Tobias Huber, Elisabeth André

2021-04-15

General General

Anomaly Detection Algorithm for Real-World Data and Evidence in Clinical Research.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : Statistical analysis, which has become an integral part of evidence-based medicine, relies heavily on data quality that is of critical importance in modern clinical research. Input data are not only at risk of being falsified or fabricated, but also of being mishandled by investigators.

OBJECTIVE : The urgent need to assure the highest data quality possible has led to the implementation of various auditing strategies designed to monitor clinical trials and detect errors of different origin that frequently occur in the field.

METHODS : A particular electronic data capture (EDC) system, which is used for data management in clinical registries, is presented including its architecture and data structure. This EDC system features an algorithm based on machine learning designed to detect anomalous patterns in quantitative data. The detection algorithm combines clustering with a series of seven distance metrics that serve to determine the strength of an anomaly. For the detection process, the thresholds and combinations of the metrics were used and the detection performance was evaluated and validated in the experiments involving simulated anomalous data and real-world data.

RESULTS : Five different clinical registries related to neuroscience were presented - all of them running in the given EDC system. Two of the registries were selected for the evaluation experiments and served also to validate the detection performance on an independent dataset. The best performing combination of the distance metrics was that of Canberra, Manhattan and Mahalanobis, whereas Cosine and Chebyshev metrics had been excluded from further analysis due to the lowest performance when used as single distance metric-based classifiers.

CONCLUSIONS : The experimental results demonstrate that the algorithm, which is universal in nature, and as such may be implemented in other EDC systems, is capable of anomalous data detection with a sensitivity exceeding 85 %.

CLINICALTRIAL :

Churová Vendula, Vyškovský Roman, Maršálová Kateřina, Kudláček David, Schwarz Daniel

2021-Apr-12

General General

Demographic Predictors of Dropping Out of Treatment (DOT) in Substance Use Disorder Treatment.

In Substance use & misuse ; h5-index 30.0

BACKGROUND : Researchers have not studied or used novel methods for identifying potential disparities for sexual minorities, those with criminal pasts, and veterans in (DOT).

METHODS : We used Bayesian logistic regression to identify factors associated with DOT, tested interaction effects, and used machine learning to classify qualitative responses.

FINDINGS : With 2,772 clients from two inpatient clinics in the Southwest United States, we found sexual minorities and females had 52% and 61%, increases and African Americans had 54% decreases in the odds of DOT. Additionally, those with a criminal past and 34.5 and older were less likely to DOT by 5% relative to clients with no prior involvement in the criminal justice system.

CONCLUSIONS : This study illustrated the disparities for women and sexual minorities in DOT as well as demonstrated novel methodological approaches to addressing previously unanswered questions.

Hanauer Matthew, Sielbeck-Mathes Kathryn, Banks Bre, Mitori Jessica, Reuveny Adi

2021-Apr-14

Bayesian analysis, against medical advice, machine learning, substance misuse

Pathology Pathology

Feasibility of deep learning-based fully automated classification of microsatellite instability in tissue slides of colorectal cancer.

In International journal of cancer ; h5-index 82.0

High levels of microsatellite instability (MSI-H) occurs in about 15% of sporadic colorectal cancer (CRC) and is an important predictive marker for response to immune checkpoint inhibitors. To test the feasibility of a deep learning (DL)-based classifier as a screening tool for MSI status, we built a fully automated DL-based MSI classifier using pathology whole-slide images (WSIs) of CRCs. On small image patches of The Cancer Genome Atlas (TCGA) CRC WSI dataset, tissue/non-tissue, normal/tumor, and MSS/MSI-H classifiers were applied sequentially for the fully automated prediction of the MSI status. The classifiers were also tested on an independent cohort. Furthermore, to test how the expansion of the training data affects the performance of the DL-based classifier, additional classifier trained on both TCGA and external datasets was tested. The areas under the receiver operating characteristic curves were 0.892 and 0.972 for the TCGA and external datasets, respectively, by a classifier trained on both datasets. The performance of the DL-based classifier was much better than that of previously reported histomorphology-based methods. We speculated that about 40% of CRC slides could be screened for MSI status without molecular testing by the DL-based classifier. These results demonstrated that the DL-based method has potential as a screening tool to discriminate molecular alteration in tissue slides. This article is protected by copyright. All rights reserved.

Lee Sung Hak, Song In Hye, Jang Hyun-Jong

2021-Apr-13

Computational pathology, computer-aided diagnosis, convolutional neural network, digital pathology

Radiology Radiology

Artificial intelligence development in pediatric body magnetic resonance imaging: best ideas to adapt from adults.

In Pediatric radiology

Emerging manifestations of artificial intelligence (AI) have featured prominently in virtually all industries and facets of our lives. Within the radiology literature, AI has shown great promise in improving and augmenting radiologist workflow. In pediatric imaging, while greatest AI inroads have been made in musculoskeletal radiographs, there are certainly opportunities within thoracoabdominal MRI for AI to add significant value. In this paper, we briefly review non-interpretive and interpretive data science, with emphasis on potential avenues for advancement in pediatric body MRI based on similar work in adults. The discussion focuses on MRI image optimization, abdominal organ segmentation, and osseous lesion detection encountered during body MRI in children.

Moore Michael M, Iyer Ramesh S, Sarwani Nabeel I, Sze Raymond W

2021-Apr-13

Artificial intelligence, Children, Convolutional neural network, Magnetic resonance imaging, Optimization, Segmentation

Radiology Radiology

Unconventional non-amino acidic PET radiotracers for molecular imaging in gliomas.

In European journal of nuclear medicine and molecular imaging ; h5-index 66.0

PURPOSE : The objective of this review was to explore the potential clinical application of unconventional non-amino acid PET radiopharmaceuticals in patients with gliomas.

METHODS : A comprehensive search strategy was used based on SCOPUS and PubMed databases using the following string: ("perfusion" OR "angiogenesis" OR "hypoxia" OR "neuroinflammation" OR proliferation OR invasiveness) AND ("brain tumor" OR "glioma") AND ("Positron Emission Tomography" OR PET). From all studies published in English, the most relevant articles were selected for this review, evaluating the mostly used PET radiopharmaceuticals in research centers, beyond amino acid radiotracers and 2-[18F]fluoro-2-deoxy-D-glucose ([18F]FDG), for the assessment of different biological features, such as perfusion, angiogenesis, hypoxia, neuroinflammation, cell proliferation, tumor invasiveness, and other biological characteristics in patients with glioma.

RESULTS : At present, the use of non-amino acid PET radiopharmaceuticals specifically designed to assess perfusion, angiogenesis, hypoxia, neuroinflammation, cell proliferation, tumor invasiveness, and other biological features in glioma is still limited.

CONCLUSION : The use of investigational PET radiopharmaceuticals should be further explored considering their promising potential and studies specifically designed to validate these preliminary findings are needed. In the clinical scenario, advancements in the development of new PET radiopharmaceuticals and new imaging technologies (e.g., PET/MR and the application of the artificial intelligence to medical images) might contribute to improve the clinical translation of these novel radiotracers in the assessment of gliomas.

Laudicella R, Quartuccio N, Argiroffi G, Alongi P, Baratto L, Califaretti E, Frantellizzi V, De Vincentis G, Del Sole A, Evangelista L, Baldari S, Bisdas S, Ceci Francesco, Iagaru Andrei

2021-Apr-13

Angiogenesis, Glioma, Neuroinflammation, PET, Perfusion, Proliferation

General General

Comprehensive review and evaluation of computational methods for identifying FLT3-internal tandem duplication in acute myeloid leukaemia.

In Briefings in bioinformatics

Internal tandem duplication (ITD) of FMS-like tyrosine kinase 3 (FLT3-ITD) constitutes an independent indicator of poor prognosis in acute myeloid leukaemia (AML). AML with FLT3-ITD usually presents with poor treatment outcomes, high recurrence rate and short overall survival. Currently, polymerase chain reaction and capillary electrophoresis are widely adopted for the clinical detection of FLT3-ITD, whereas the length and mutation frequency of ITD are evaluated using fragment analysis. With the development of sequencing technology and the high incidence of FLT3-ITD mutations, a multitude of bioinformatics tools and pipelines have been developed to detect FLT3-ITD using next-generation sequencing data. However, systematic comparison and evaluation of the methods or software have not been performed. In this study, we provided a comprehensive review of the principles, functionality and limitations of the existing methods for detecting FLT3-ITD. We further compared the qualitative and quantitative detection capabilities of six representative tools using simulated and biological data. Our results will provide practical guidance for researchers and clinicians to select the appropriate FLT3-ITD detection tools and highlight the direction of future developments in this field. Availability: A Docker image with several programs pre-installed is available at https://github.com/niu-lab/docker-flt3-itd to facilitate the application of FLT3-ITD detection tools.

Yuan Danyang, He Xiaoyu, Han Xinyin, Yang Chunyan, Liu Fei, Zhang Shuying, Luan Haijing, Li Ruilin, He Jiayin, Duan Xiaohong, Wang Dongliang, Zhou Qiming, Gao Sujun, Niu Beifang

2021-Apr-13

\n FLT3-ITD, acute myeloid leukaemia, bioinformatics, next-generation sequencing

General General

Signatures of COVID-19 severity and immune response in the respiratory tract microbiome.

In medRxiv : the preprint server for health sciences

Rationale : Viral infection of the respiratory tract can be associated with propagating effects on the airway microbiome, and microbiome dysbiosis may influence viral disease.

Objective : To define the respiratory tract microbiome in COVID-19 and relationship disease severity, systemic immunologic features, and outcomes.

Methods and Measurements : We examined 507 oropharyngeal, nasopharyngeal and endotracheal samples from 83 hospitalized COVID-19 patients, along with non-COVID patients and healthy controls. Bacterial communities were interrogated using 16S rRNA gene sequencing, commensal DNA viruses Anelloviridae and Redondoviridae were quantified by qPCR, and immune features were characterized by lymphocyte/neutrophil (L/N) ratios and deep immune profiling of peripheral blood mononuclear cells (PBMC).

Main Results : COVID-19 patients had upper respiratory microbiome dysbiosis, and greater change over time than critically ill patients without COVID-19. Diversity at the first time point correlated inversely with disease severity during hospitalization, and microbiome composition was associated with L/N ratios and PBMC profiles in blood. Intubated patients showed patient-specific and dynamic lung microbiome communities, with prominence of Staphylococcus . Anelloviridae and Redondoviridae showed more frequent colonization and higher titers in severe disease. Machine learning analysis demonstrated that integrated features of the microbiome at early sampling points had high power to discriminate ultimate level of COVID-19 severity.

Conclusions : The respiratory tract microbiome and commensal virome are disturbed in COVID-19, correlate with systemic immune parameters, and early microbiome features discriminate disease severity. Future studies should address clinical consequences of airway dysbiosis in COVID-19, possible use as biomarkers, and role of bacterial and viral taxa identified here in COVID-19 pathogenesis.

Merenstein Carter, Liang Guanxiang, Whiteside Samantha A, Cobián-Güemes Ana G, Merlino Madeline S, Taylor Louis J, Glascock Abigail, Bittinger Kyle, Tanes Ceylan, Graham-Wooten Jevon, Khatib Layla A, Fitzgerald Ayannah S, Reddy Shantan, Baxter Amy E, Giles Josephine R, Oldridge Derek A, Meyer Nuala J, Wherry E John, McGinniss John E, Bushman Frederic D, Collman Ronald G

2021-Apr-05

General General

Classification of Rice Yield Using UAV-Based Hyperspectral Imagery and Lodging Feature.

In Plant phenomics (Washington, D.C.)

High-yield rice cultivation is an effective way to address the increasing food demand worldwide. Correct classification of high-yield rice is a key step of breeding. However, manual measurements within breeding programs are time consuming and have high cost and low throughput, which limit the application in large-scale field phenotyping. In this study, we developed an accurate large-scale approach and presented the potential usage of hyperspectral data for rice yield measurement using the XGBoost algorithm to speed up the rice breeding process for many breeders. In total, 13 japonica rice lines in regional trials in northern China were divided into different categories according to the manual measurement of yield. Using an Unmanned Aerial Vehicle (UAV) platform equipped with a hyperspectral camera to capture images over multiple time series, a rice yield classification model based on the XGBoost algorithm was proposed. Four comparison experiments were carried out through the intraline test and the interline test considering lodging characteristics at the midmature stage or not. The result revealed that the degree of lodging in the midmature stage was an important feature affecting the classification accuracy of rice. Thus, we developed a low-cost, high-throughput phenotyping and nondestructive method by combining UAV-based hyperspectral measurements and machine learning for estimation of rice yield to improve rice breeding efficiency.

Wang Jian, Wu Bizhi, Kohnen Markus V, Lin Daqi, Yang Changcai, Wang Xiaowei, Qiang Ailing, Liu Wei, Kang Jianbin, Li Hua, Shen Jing, Yao Tianhao, Su Jun, Li Bangyu, Gu Lianfeng

2021

General General

Electronic structure data at ground and excited state of the structural and opto-electronic properties of organic photovoltaic materials.

In Data in brief

This work presents data coming from electronic structure calculations at the Density Functional Theory level, performed in a series of organic photovoltaic materials. The data represents the Cartesian coordinates of such molecular systems at the lowest energy geometry and at the first excited state. Data evidencing the nature of the photo-isomerization in the OPV systems was also obtained. Additionally, the highest probabilities of the molecular electronic transitions giving rise to the absorption spectra observed in excited state were also computed. These data may aid to estimate photovoltaic parameters, and to tailor materials intended to be implemented in solar cell devices. They may also be used as input to design a training set for machine learning analysis and artificial intelligence.

Delesma Cornelio, Amador-Bedolla Carlos, Robles Miguel, Muñiz Jesús

2021-Apr

Density functional theory, Excited state, Organic photovoltaic, Photo-isomerization

Radiology Radiology

Connectome-based prediction of brain age in Rolandic epilepsy: a protocol for a multicenter cross-sectional study.

In Annals of translational medicine

Background : Rolandic epilepsy (RE) is a common pediatric idiopathic partial epilepsy syndrome. Children with RE display varying degrees of cognitive impairment. In epilepsy, age-related neuroanatomic and cognitive changes differ greatly from those observed in the healthy brain, and may be defined as accelerated brain aging. Connectome-based predictive modeling (CPM) is a recently developed machine learning approach that uses whole-brain connectivity measured with neuroimaging data ("neural fingerprints") to predict brain-behavior relationships. The aim of the study will be to develop and validate a CPM for predicting brain age in patients with RE.

Methods : A multicenter, cross-sectional study will be conducted in 5 Chinese hospitals. A total of 100 RE patients (including 50 patients receiving anti-epileptic drugs and 50 drug-naïve patients) and 100 healthy children will be recruited to undergo a neuropsychological test using the Wechsler Intelligence Scale. Magnetic resonance images will also be collected. CPM will be applied to predict the brain age of children with RE based on brain functional connectivity.

Discussion : The findings of the study will facilitate our understanding of developmental changes in the brain in children with RE and could also be an important milestone in the journey toward developing effective early interventions for this disorder.

Trial registration : The study has been registered with Chinese Clinical Trial Registry (ChiCTR2000032984).

Wang Fuqin, Yin Yu, Yang Yang, Liang Ting, Huang Tingting, He Cheng, Hu Jie, Zhang Jingjing, Yang Yanli, Xing Qianlu, Zhang Tijiang, Liu Heng

2021-Mar

MRI, Rolandic epilepsy (RE), brain age, machine learning

Public Health Public Health

Artificial neural network to predict the effect of obesity on the risk of tuberculosis infection.

In Journal of public health research

BACKGROUND : Body weight has been implicated as a risk factor for latent tuberculosis infection (LTBI) and the active disease.

DESIGN AND METHODS : This study aimed to develop artificial neural network (ANN) models for predicting LTBI from body weight and other host-related disease risk factors. We used datasets from participants of the US-National Health and Nutrition Examination Survey (NHANES; 2012; n=5,156; 514 with LTBI and 4,642 controls) to develop three ANNs employing body mass index (BMI, Network I), BMI and HbA1C (as a proxy for diabetes; Network II) and BMI, HbA1C and education (as a proxy for socioeconomic status; Network III). The models were trained on n=1018 age- and sex-matched subjects equally distributed between the control and LTBI groups. The endpoint was the prediction of LTBI.

RESULTS : When data was adjusted for age, sex, diabetes and level of education, odds ratio (OR) and 95% confidence intervals (CI) for risk of LTBI with increased BMI was 0.85 (95%CI: 0.77 - 0.96, p=0.01). The three ANNs had a predictive accuracy varied from 75 to 80% with sensitivities ranged from 85% to 94% and specificities of approximately 70%. Areas under the receiver operating characteristic curve (AUC) were between 0.82 and 0.87. Optimal ANN performance was noted using BMI as a risk indicator.

CONCLUSION : Body weight can be employed in developing artificial intelligence-based tool to predict LTBI. This can be useful in precise decision making in clinical and public health practices aiming to curb the burden of tuberculosis, e.g., in the management and monitoring of the tuberculosis prevention programs and to evaluate the impact of healthy weight on tuberculosis risk and burden.

Badawi Alaa, Liu Christina J, Rehim Anas A, Gupta Alind

2021-Mar-15

General General

Leveraging Genomic Associations in Precision Digital Care for Weight Loss: Cohort Study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : In this age of global COVID-19 pandemic, the urgency of addressing an epidemic of obesity and associated inflammatory illnesses has come to the fore. Studies have demonstrated that interactions between single nucleotide polymorphisms (SNPs) and lifestyle interventions like food and exercise may vary metabolic outcomes, contributing to obesity and therapeutic response. However, there is a paucity of research relating outcomes from digital therapeutics to inclusion of genetic data in care interventions.

OBJECTIVE : This study aims to describe and model weight loss of subjects enrolled in a precision digital weight loss program informed by machine learning analysis of subject data, including genomic. It was hypothesized that weight loss models would exhibit better fit when incorporating genomic data than utilizing demographic and engagement variables alone.

METHODS : A cohort of 393 participants enrolled in Digbi's personalized digital care program for 120 days was analyzed retrospectively. Care protocol included the use of subject data informing precision coaching by mobile app and personal coach. Two linear regression models of weight loss in this cohort (pounds lost, percentage lost) as a function of demographic and behavioral engagement variables were fit. Genomic-enhanced models were built by adding 197 SNPs from subject genomic data as predictors, then refitting, employing Lasso regression on SNPs for variable selection. Success/failure logistic regression models were also fit, with and without genomic data.

RESULTS : 72% of subjects in this cohort lost weight, while 17% maintained stable weight. 142 subjects lost 5% within 120 days. Models describe the impact of demographic and clinical factors, behavioral engagement, and genomic risk on weight loss. The addition of genomic predictors improved the mean squared error of weight loss models (pounds lost and percent) from 70 to 60 and 16 to 13 respectively. The logistic model improved pseudo R2 from 0.193 to 0.285. Gender, engagement and specific SNPs were significantly associated with weight loss. SNPs within genes involved in metabolic pathways that process food and regulate storage of fat were associated with weight loss in this cohort. This included rs17300539_G (insulin resistance, monounsaturated fat metabolism), rs2016520_C (BMI, waist circumference, cholesterol metabolism), and rs4074995_A (calcium-potassium transport, serum calcium levels). Models described greater average weight loss for subjects having more of these risk alleles. Notably, coaching for dietary modification was personalized to these genetic risks.

CONCLUSIONS : Adding genomic information in modeling outcomes of a digital precision weight loss program greatly enhanced model accuracy. Interpretable weight loss models pointed to efficacy of coaching informed by subjects' genomic risk, accompanied by active engagement of subjects in their own success. While large-scale validation is needed, our study preliminarily supports precision dietary interventions for weight loss utilizing genetic risk, with digitally delivered recommendations alongside health-coaching to improve intervention efficacy.

CLINICALTRIAL :

Sinha Ranjan, Kachru Dashyanng, Ricchetti Roshni Ray, Singh-Rambiritch Simitha, Muthukumar Karthik Marimuthu, Singaravel Vidhya, Irudayanathan Carmel, Reddy-Sinha Chandana, Junaid Imran, Sharma Garima, Airey Catherine, Francis-Lyon Patricia Alice

2021-Apr-11

General General

BERT based Transformers lead the way in Extraction of Health Information from Social Media

ArXiv Preprint

This paper describes our submissions for the Social Media Mining for Health (SMM4H)2021 shared tasks. We participated in 2 tasks:(1) Classification, extraction and normalization of adverse drug effect (ADE) mentions in English tweets (Task-1) and (2) Classification of COVID-19 tweets containing symptoms(Task-6). Our approach for the first task uses the language representation model RoBERTa with a binary classification head. For the second task, we use BERTweet, based on RoBERTa. Fine-tuning is performed on the pre-trained models for both tasks. The models are placed on top of a custom domain-specific processing pipeline. Our system ranked first among all the submissions for subtask-1(a) with an F1-score of 61%. For subtask-1(b), our system obtained an F1-score of 50% with improvements up to +8% F1 over the score averaged across all submissions. The BERTweet model achieved an F1 score of 94% on SMM4H 2021 Task-6.

Sidharth R, Abhiraj Tiwari, Parthivi Choubey, Saisha Kashyap, Sahil Khose, Kumud Lakara, Nishesh Singh, Ujjwal Verma

2021-04-15

General General

BERT based Transformers lead the way in Extraction of Health Information from Social Media

ArXiv Preprint

This paper describes our submissions for the Social Media Mining for Health (SMM4H)2021 shared tasks. We participated in 2 tasks:(1) Classification, extraction and normalization of adverse drug effect (ADE) mentions in English tweets (Task-1) and (2) Classification of COVID-19 tweets containing symptoms(Task-6). Our approach for the first task uses the language representation model RoBERTa with a binary classification head. For the second task, we use BERTweet, based on RoBERTa. Fine-tuning is performed on the pre-trained models for both tasks. The models are placed on top of a custom domain-specific processing pipeline. Our system ranked first among all the submissions for subtask-1(a) with an F1-score of 61%. For subtask-1(b), our system obtained an F1-score of 50% with improvements up to +8% F1 over the score averaged across all submissions. The BERTweet model achieved an F1 score of 94% on SMM4H 2021 Task-6.

Sidharth R, Abhiraj Tiwari, Parthivi Choubey, Saisha Kashyap, Sahil Khose, Kumud Lakara, Nishesh Singh, Ujjwal Verma

2021-04-15

General General

A retrospective study on the effectiveness of Artificial Intelligence-based Clinical Decision Support System (AI-CDSS) to improve the incidence of hospital-related venous thromboembolism (VTE).

In Annals of translational medicine

Background : Artificial intelligence technology is widely used in the medical industry. Our retrospective study evaluated the effectiveness of an AI-CDSS in improving the incidence of hospital-related VTE and the impact of anticoagulant drug use.

Methods : This study collected relevant data on adult patients over 18 years of age who are not discharged 24 hours, from January to July 2019 and from January to July 2020, the VTE high-risk department of Ruijin Hospital. Before and after using AI-CDSS, the incidence of hospital-related VTE and using anticoagulants were analyzed.

Results : Between January to July 2019 and January to July 2020, 3,565 and 4,423 adult patients over 18 years old were hospitalized in our hospital and were designed as a control group and intervention group, respectively (7,988 in total). Both groups had similar baseline characteristics. There were 4,716 (59.03%) male patients, the mean age was 60.43±13.09 years, and the mean stay was 7.56±7.76 days. More than half of the patients (4,605, 57.58%) came from the respiratory. VTE events during hospitalization occurred in 41 patients; overall, 5.13/1,000 (41 episodes in 7,988 patients). Compared with the control group, before implementing AI-CDSS, the rate of VTE during hospitalization was reduced from 5.89/1,000 (21 episodes in 3,565 patients) to 4.75/1,000 patients (20 episodes in 4,423 patients) (relative reduction of 19.35%) in the intervention group. The use rate of anticoagulant drugs was increased from 19.97% (712/3,565) in the control group to 22.88% (1,012/4,423) in intervention group [P<0.01, odds ratio (OR): 1.19, 95 percent confidence interval (95% CI) (1.07-1.32)], (relative 14.57% increase). Poisson's regression results showed that department, age ≥75 years [OR: 3.09, 95% Cl (1.45-6.33)], duration of hospitalization [OR: 1.04, 95% CI (1.03-1.05)], heart failure [OR: 5.13, 95% CI (1.74-13.54)] and renal failure [OR: 3.60, 95% CI (0.90-11.34)] were high-risk factors for VTE events.

Conclusions : Implementing AI-CDSS can help clinicians identify hospitalized patients at increased VTE risk, take effective preventive measures, and improve clinicians' compliance with the American College of Chest Physicians (ACCP) guidelines.

Zhou Shuai, Ma Xudong, Jiang Songyi, Huang Xiaoyan, You Yi, Shang Hanbing, Lu Yong

2021-Mar

China, a retrospective study, artificial intelligence-based clinical decision support system (AI-CDSS), hospitalization rate, venous thromboembolism (VTE)

General General

Tracking-based deep learning method for temporomandibular joint segmentation.

In Annals of translational medicine

Background : The shape, size, and surface information relating to the glenoid fossae and condyles in temporomandibular joints (TMJ) are essential for diagnosing and treating. Patients with TMJ disease often have surface abrasion which may cause fuzzy edges in computed tomography (CT) imaging, especially for low-dose CT, making TMJ segmentation more difficult.

Methods : In this paper, an automatic segmentation algorithm based on deep learning and post-processing was introduced. First, U-Net was used to divide images into 3 categories: glenoid fossae, condyles, and background. For structural fractures in these divided images, the internal force constraint of a snake model was used to replenish the integrity of the fracture boundary in a post-processing operation, and the initial boundary of the snake was obtained based on the basis of the tracking concept. A total of 206 cases of low-dose CT were used to verify the effectiveness of the algorithm, and such indicators as the Dice coefficient (DC) and mean surface distance (MSD) were used to evaluate the agreement between experimental results and the gold standard.

Results : The proposed method is tested on a self-collected dataset. The results demonstrate that proposed method achieves state-of-the-art performance in terms of DCs = 0.92±0.03 (condyles) and 0.90±0.04 (glenoid fossae), and MSDs =0.20±0.19 mm (condyles) and 0.19±0.08 mm (glenoid fossae).

Conclusions : This study is the first to focus on the simultaneous segmentation of TMJ glenoid fossae and condyles. The proposed U-Net + tracking-based algorithm showed a relatively high segmentation efficiency, enabling it to achieve sought-after segmentation accuracy.

Liu Yi, Lu Yao, Fan Yubo, Mao Longxia

2021-Mar

Biomedical imaging, computer-aided diagnosis, deep learning, image segmentation, low-dose computed tomography (low-dose CT), tracking

oncology Oncology

Narrative review of prostate cancer grading systems: will the Gleason scores be replaced by the Grade Groups?

In Translational andrology and urology

The Gleason grading system, proposed by Dr. Donald F. Gleason in 1966, is one of the most important prognostic factors in men with prostate cancer (PCa). At consensus conferences held in 2005 and 2014, organized by the International Society of Urological Pathology (ISUP), the system was modified to reflect the current diagnostic and therapeutic approaches. In particular, in the 2014 Conference, it was recognized that there were weaknesses with the original and the 2005 ISUP modified Gleason systems. Based on the results of a research conducted by Prof. JI Epstein and his group, a new grading system was proposed by the ISUP in order to address some of such deficiencies: i.e., the five distinct Grade Groups (GGs). Since 2014, results of studies have been published by different groups and societies, including the Genitourinary Pathology Society (GUPS), giving additional support to the prognostic role of the architectural Gleason patterns and, in particular, of the GGs. A revised GG system, taking into account the percentage of Gleason pattern (GP) 4, cribriform and intraductal carcinoma, tertiary GP 5, and reactive stroma grade, has shown to have some advantages, however not ready for adoption in the current practice. The aim of this contribution was to review the major updates and recommendations regarding the GPs and GSs, as well as the GGs, trying to give an answer to the following questions: "How has the grade group system been used in the routine?" and "will the Gleason scoring system be replace by the grade groups?" We also discussed the potential implementation in the future of molecular pathology and artificial intelligence in grading to further define risk groups in patients with PCa.

Montironi Rodolfo, Cheng Liang, Cimadamore Alessia, Mazzucchelli Roberta, Scarpelli Marina, Santoni Matteo, Massari Francesco, Lopez-Beltran Antonio

2021-Mar

2005 ISUP Gleason modified system, 2014 ISUP Gleason modified system, Genitourinary Pathology Society (GUPS), Gleason grading, International Society of Urological Pathology (ISUP), Prostate cancer (PCa), prognostic grade grouping

General General

Early stage risk communication and community engagement (RCCE) strategies and measures against the coronavirus disease 2019 (COVID-19) pandemic crisis.

In Global health journal (Amsterdam, Netherlands)

Coronavirus disease 2019 (COVID-19) pandemic has proven to be tenacious and shows that the global community is still poorly prepared to handling such emerging pandemics. Enhancing global solidarity in emergency preparedness and response, and the mobilization of conscience and cooperation, can serve as an excellent source of ideas and measures in a timely manner. The article provides an overview of the key components of risk communication and community engagement (RCCE) strategies at the early stages in vulnerable nations and populations, and highlight contextual recommendations for strengthening coordinated and sustainable RCCE preventive and emergency response strategies against COVID-19 pandemic. Global solidarity calls for firming governance, abundant community participation and enough trust to boost early pandemic preparedness and response. Promoting public RCCE response interventions needs crucially improving government health systems and security proactiveness, community to individual confinement, trust and resilience solutions. To better understand population risk and vulnerability, as well as COVID-19 transmission dynamics, it is important to build intelligent systems for monitoring isolation/quarantine and tracking by use of artificial intelligence and machine learning systems algorithms. Experiences and lessons learned from the international community is crucial for emerging pandemics prevention and control programs, especially in promoting evidence-based decision-making, integrating data and models to inform effective and sustainable RCCE strategies, such as local and global safe and effective COVID-19 vaccines and mass immunization programs.

Zhang Yanjie, Tambo Ernest, Djuikoue Ingrid C, Tazemda Gildas K, Fotsing Michael F, Zhou Xiao-Nong

2021-Mar

Coronavirus disease 2019 (COVID-19), Governance, Pandemic, Response, Risk communication and community engagement (RCCE), Trust, Vaccination

General General

Prediction of hematocrit through imbalanced dataset of blood spectra.

In Healthcare technology letters

In spite of machine learning has been successfully used in a wide range of healthcare applications, there are several parameters that could influence the performance of a machine learning system. One of the big issues for a machine learning algorithm is related to imbalanced dataset. An imbalanced dataset occurs when the distribution of data is not uniform. This makes harder the implementation of accurate models. In this paper, intelligent models are implemented to predict the hematocrit level of blood starting from visible spectral data. The aim of this work is to show the effects of two balancing techniques (SMOTE and SMOTE+ENN) on the imbalanced dataset of blood spectra. Four different machine learning systems are fitted with imbalanced and balanced datasets and their performances are compared showing an improvement, in terms of accuracy, due to the use of balancing.

Decaro Cristoforo, Montanari Giovanni Battista, Bianconi Marco, Bellanca Gaetano

2021-Apr

Radiology Radiology

Computer-Aided Diagnosis of Congenital Abnormalities of the Kidney and Urinary Tract in Children Using a Multi-Instance Deep Learning Method Based on Ultrasound Imaging Data.

In Proceedings. IEEE International Symposium on Biomedical Imaging

Ultrasound images are widely used for diagnosis of congenital abnormalities of the kidney and urinary tract (CAKUT). Since a typical clinical ultrasound image captures 2D information of a specific view plan of the kidney and images of the same kidney on different planes have varied appearances, it is challenging to develop a computer aided diagnosis tool robust to ultrasound images in different views. To overcome this problem, we develop a multi-instance deep learning method for distinguishing children with CAKUT from controls based on their clinical ultrasound images, aiming to automatic diagnose the CAKUT in children based on ultrasound imaging data. Particularly, a multi-instance deep learning method was developed to build a robust pattern classifier to distinguish children with CAKUT from controls based on their ultrasound images in sagittal and transverse views obtained during routine clinical care. The classifier was built on imaging features derived using transfer learning from a pre-trained deep learning model with a mean pooling operator for fusing instance-level classification results. Experimental results have demonstrated that the multi-instance deep learning classifier performed better than classifiers built on either individual sagittal slices or individual transverse slices.

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

2020-Apr

Multi-instance deep learning, congenital abnormalities of the kidney and urinary tract, sagittal view, transverse view, ultrasound images

Internal Medicine Internal Medicine

Accessory pathway analysis using a multimodal deep learning model.

In Scientific reports ; h5-index 158.0

Cardiac accessory pathways (APs) in Wolff-Parkinson-White (WPW) syndrome are conventionally diagnosed with decision tree algorithms; however, there are problems with clinical usage. We assessed the efficacy of the artificial intelligence model using electrocardiography (ECG) and chest X-rays to identify the location of APs. We retrospectively used ECG and chest X-rays to analyse 206 patients with WPW syndrome. Each AP location was defined by an electrophysiological study and divided into four classifications. We developed a deep learning model to classify AP locations and compared the accuracy with that of conventional algorithms. Moreover, 1519 chest X-ray samples from other datasets were used for prior learning, and the combined chest X-ray image and ECG data were put into the previous model to evaluate whether the accuracy improved. The convolutional neural network (CNN) model using ECG data was significantly more accurate than the conventional tree algorithm. In the multimodal model, which implemented input from the combined ECG and chest X-ray data, the accuracy was significantly improved. Deep learning with a combination of ECG and chest X-ray data could effectively identify the AP location, which may be a novel deep learning model for a multimodal model.

Nishimori Makoto, Kiuchi Kunihiko, Nishimura Kunihiro, Kusano Kengo, Yoshida Akihiro, Adachi Kazumasa, Hirayama Yasutaka, Miyazaki Yuichiro, Fujiwara Ryudo, Sommer Philipp, El Hamriti Mustapha, Imada Hiroshi, Takemoto Makoto, Takami Mitsuru, Shinohara Masakazu, Toh Ryuji, Fukuzawa Koji, Hirata Ken-Ichi

2021-Apr-13

Public Health Public Health

Medical records-based chronic kidney disease phenotype for clinical care and "big data" observational and genetic studies.

In NPJ digital medicine

Chronic Kidney Disease (CKD) represents a slowly progressive disorder that is typically silent until late stages, but early intervention can significantly delay its progression. We designed a portable and scalable electronic CKD phenotype to facilitate early disease recognition and empower large-scale observational and genetic studies of kidney traits. The algorithm uses a combination of rule-based and machine-learning methods to automatically place patients on the staging grid of albuminuria by glomerular filtration rate ("A-by-G" grid). We manually validated the algorithm by 451 chart reviews across three medical systems, demonstrating overall positive predictive value of 95% for CKD cases and 97% for healthy controls. Independent case-control validation using 2350 patient records demonstrated diagnostic specificity of 97% and sensitivity of 87%. Application of the phenotype to 1.3 million patients demonstrated that over 80% of CKD cases are undetected using ICD codes alone. We also demonstrated several large-scale applications of the phenotype, including identifying stage-specific kidney disease comorbidities, in silico estimation of kidney trait heritability in thousands of pedigrees reconstructed from medical records, and biobank-based multicenter genome-wide and phenome-wide association studies.

Shang Ning, Khan Atlas, Polubriaginof Fernanda, Zanoni Francesca, Mehl Karla, Fasel David, Drawz Paul E, Carrol Robert J, Denny Joshua C, Hathcock Matthew A, Arruda-Olson Adelaide M, Peissig Peggy L, Dart Richard A, Brilliant Murray H, Larson Eric B, Carrell David S, Pendergrass Sarah, Verma Shefali Setia, Ritchie Marylyn D, Benoit Barbara, Gainer Vivian S, Karlson Elizabeth W, Gordon Adam S, Jarvik Gail P, Stanaway Ian B, Crosslin David R, Mohan Sumit, Ionita-Laza Iuliana, Tatonetti Nicholas P, Gharavi Ali G, Hripcsak George, Weng Chunhua, Kiryluk Krzysztof

2021-Apr-13

General General

Machine learning, transcriptome, and genotyping chip analyses provide insights into SNP markers identifying flower color in Platycodon grandiflorus.

In Scientific reports ; h5-index 158.0

Bellflower is an edible ornamental gardening plant in Asia. For predicting the flower color in bellflower plants, a transcriptome-wide approach based on machine learning, transcriptome, and genotyping chip analyses was used to identify SNP markers. Six machine learning methods were deployed to explore the classification potential of the selected SNPs as features in two datasets, namely training (60 RNA-Seq samples) and validation (480 Fluidigm chip samples). SNP selection was performed in sequential order. Firstly, 96 SNPs were selected from the transcriptome-wide SNPs using the principal compound analysis (PCA). Then, 9 among 96 SNPs were later identified using the Random forest based feature selection method from the Fluidigm chip dataset. Among six machines, the random forest (RF) model produced higher classification performance than the other models. The 9 SNP marker candidates selected for classifying the flower color classification were verified using the genomic DNA PCR with Sanger sequencing. Our results suggest that this methodology could be used for future selection of breeding traits even though the plant accessions are highly heterogeneous.

Yu Go-Eun, Shin Younhee, Subramaniyam Sathiyamoorthy, Kang Sang-Ho, Lee Si-Myung, Cho Chuloh, Lee Seung-Sik, Kim Chang-Kug

2021-Apr-13

oncology Oncology

Radiotherapy for brain metastasis and long-term survival.

In Scientific reports ; h5-index 158.0

Patients with brain metastases (BM) can benefit from radiotherapy (RT), although the long-term benefits of RT remain unclear. We searched a Korean national health insurance claims database and identified 135,740 patients with newly diagnosed BM during 2002-2017. Propensity score matching (PSM) was used to evaluate survival according to RT modality, which included whole-brain radiotherapy (WBRT) and/or stereotactic radiosurgery (SRS). The 84,986 eligible patients were followed for a median interval of 6.6 months, and 37,046 patients underwent RT (43.6%). After the PSM, patients who underwent RT had significantly better overall survival after 1 year (42.4% vs. 35.3%, P < 0.001), although there was no significant difference at 2.6 years, and patients who did not undergo RT had better survival after 5 years. Among patients with BM from lung cancer, RT was also associated with a survival difference after 1 year (57.3% vs. 32.8%, P < 0.001) and a median survival increase of 3.7 months. The 1-year overall survival rate was significantly better for SRS than for WBRT (46.4% vs. 38.8%, P < 0.001). Among Korean patients with BM, especially patients with primary lung cancer, RT improved the short-term survival rate, and SRS appears to be more useful than WBRT in this setting.

Park Kawngwoo, Bae Gi Hwan, Kim Woo Kyung, Yoo Chan-Jong, Park Cheol Wan, Kim Soo-Ki, Cha Jihye, Kim Jin Wook, Jung Jaehun

2021-Apr-13

General General

A Hybrid MPC for Constrained Deep Reinforcement Learning applied for Planar Robotic Arm.

In ISA transactions

Recently, deep reinforcement learning techniques have achieved tangible results for learning high dimensional control tasks. Due to the trial and error interaction, between the autonomous agent and the environment, the learning phase is unconstrained and limited to the simulator. Such exploration has an additional drawback of consuming unnecessary samples at the beginning of the learning process. Model-based algorithms, on the other hand, handle this issue by learning the dynamics of the environment. However, model-free algorithms have a higher asymptotic performance than model-based ones. The main contribution of this paper is to construct a hybrid structured algorithm from model predictive control (MPC) and deep reinforcement learning (DRL) (MPC-DRL), that makes use of the benefits of both methods, to satisfy constraint conditions throughout the learning process. The validity of the proposed approach is demonstrated by learning a reachability task. The results show complete satisfaction for the constraint condition, represented by a static obstacle, with a smaller number of samples and higher performance compared to state-of-the-art model-free algorithms.

Al-Gabalawy Mostafa

2021-Apr-01

Artificial intelligence, Deep learning, Deep reinforcement learning, Hybrid controller, MPC, Machine learning, Planner robot, Reinforcement learning

oncology Oncology

Gene coexpression network approach to develop an immune prognostic model for pancreatic adenocarcinoma.

In World journal of surgical oncology

BACKGROUND : Pancreatic adenocarcinoma (PAAD) is a nonimmunogenic tumor, and very little is known about the relationship between the host immune response and patient survival. We aimed to develop an immune prognostic model (IPM) and analyze its relevance to the tumor immune profiles of patients with PAAD.

METHODS : We investigated differentially expressed genes between tumor and normal tissues in the TCGA PAAD cohort. Immune-related genes were screened from highly variably expressed genes with weighted gene correlation network analysis (WGCNA) to construct an IPM. Then, the influence of IPM on the PAAD immune profile was comprehensively analyzed.

RESULTS : A total of 4902 genes highly variably expressed among primary tumors were used to construct a weighted gene coexpression network. One hundred seventy-five hub genes in the immune-related module were used for machine learning. Then, we established an IPM with four core genes (FCGR2B, IL10RA, and HLA-DRA) to evaluate the prognosis. The risk score predicted by IPM was an independent prognostic factor and had a high predictive value for the prognosis of patients with PAAD. Moreover, we found that the patients in the low-risk group had higher cytolytic activity and lower innate anti-PD-1 resistance (IPRES) signatures than patients in the high-risk group.

CONCLUSIONS : Unlike the traditional methods that use immune-related genes listed in public databases to screen prognostic genes, we constructed an IPM through WGCNA to predict the prognosis of PAAD patients. In addition, an IPM prediction of low risk indicated enhanced immune activity and a decreased anti-PD-1 therapeutic response.

Gu Xiaoqiang, Zhang Qiqi, Wu Xueying, Fan Yue, Qian Jianxin

2021-Apr-12

Immune profile, Immune prognostic model, Immunotherapy, Pancreatic cancer, WGCNA

Radiology Radiology

Deep CNN-Based CAD System for COVID-19 Detection Using Multiple Lung CT Scans.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Due to the COVID-19 pandemic and the imminent collapse of healthcare systems following the excessive consumption of financial, hospital, and medicinal resources, the WHO changed the alert level on the COVID-19 pandemic from high to very high. Meanwhile, the world began to favor less expensive and more precise COVID-19 detection methods.

OBJECTIVE : Machine vision-based COVID-19 detection methods especially Deep learning as a diagnostic technique in the early stages of the disease have found great importance during the pandemic. This study aimed to design a highly efficient CAD system for COVID-19 by using a NASNet-based algorithm.

METHODS : A state-of-the-art pre-trained CNN network for image feature extraction, called NASNet, was adopted to identify patients with COVID-19 in the first stages of the disease. A local dataset, comprising 10153 CT-scan images of 190 patients with COVID-19 and 59 with Non Covid-19, was used.

RESULTS : After fitting on the training dataset, hyper-parameter tuning and finally topological alterations of the classifier block, the proposed NASNet-based model was evaluated on the test dataset and yielded remarkable results. The proposed model's performance achieved a detection sensitivity, specificity, and accuracy of 0.999, 0.986, and 0.996, respectively.

CONCLUSIONS : The proposed model achieved acceptable results in the categorization of two data classes. Therefore, a CAD system was designed based on this model for COVID-19 detection using multiple lung CT scans. The system managed to differentiate all the COVID-19 cases from non-COVID-19 ones without any error in the application phase. Overall, the proposed deep learning-based CAD system can greatly aid radiologists in the detection of COVID-19 in its early stages. During the COVID-19 pandemic, the use of CAD system as a screening tool accelerates the process of disease detection and prevents the loss of healthcare resources.

CLINICALTRIAL :

Ghaderzadeh Mustafa, Asadi Farkhondeh, Jafari Ramezan, Bashash Davood, Abolghasemi Hassan, Aria Mehrad

2021-Apr-03

General General

A cell-to-patient machine learning transfer approach uncovers novel basal-like breast cancer prognostic markers amongst alternative splice variants.

In BMC biology

BACKGROUND : Breast cancer is amongst the 10 first causes of death in women worldwide. Around 20% of patients are misdiagnosed leading to early metastasis, resistance to treatment and relapse. Many clinical and gene expression profiles have been successfully used to classify breast tumours into 5 major types with different prognosis and sensitivity to specific treatments. Unfortunately, these profiles have failed to subclassify breast tumours into more subtypes to improve diagnostics and survival rate. Alternative splicing is emerging as a new source of highly specific biomarkers to classify tumours in different grades. Taking advantage of extensive public transcriptomics datasets in breast cancer cell lines (CCLE) and breast cancer tumours (TCGA), we have addressed the capacity of alternative splice variants to subclassify highly aggressive breast cancers.

RESULTS : Transcriptomics analysis of alternative splicing events between luminal, basal A and basal B breast cancer cell lines identified a unique splicing signature for a subtype of tumours, the basal B, whose classification is not in use in the clinic yet. Basal B cell lines, in contrast with luminal and basal A, are highly metastatic and express epithelial-to-mesenchymal (EMT) markers, which are hallmarks of cell invasion and resistance to drugs. By developing a semi-supervised machine learning approach, we transferred the molecular knowledge gained from these cell lines into patients to subclassify basal-like triple negative tumours into basal A- and basal B-like categories. Changes in splicing of 25 alternative exons, intimately related to EMT and cell invasion such as ENAH, CD44 and CTNND1, were sufficient to identify the basal-like patients with the worst prognosis. Moreover, patients expressing this basal B-specific splicing signature also expressed newly identified biomarkers of metastasis-initiating cells, like CD36, supporting a more invasive phenotype for this basal B-like breast cancer subtype.

CONCLUSIONS : Using a novel machine learning approach, we have identified an EMT-related splicing signature capable of subclassifying the most aggressive type of breast cancer, which are basal-like triple negative tumours. This proof-of-concept demonstrates that the biological knowledge acquired from cell lines can be transferred to patients data for further clinical investigation. More studies, particularly in 3D culture and organoids, will increase the accuracy of this transfer of knowledge, which will open new perspectives into the development of novel therapeutic strategies and the further identification of specific biomarkers for drug resistance and cancer relapse.

Villemin Jean-Philippe, Lorenzi Claudio, Cabrillac Marie-Sarah, Oldfield Andrew, Ritchie William, Luco Reini F

2021-Apr-12

Alternative splicing, Basal-like, Breast Cancer, Epithelial-to-mesenchymal transition, Machine learning classification, Survival

Public Health Public Health

Impact of Big Data Analytics on People's Health: Overview of Systematic Reviews and Recommendations for Future Studies.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Although the potential of big data analytics for health care is well recognized, evidence is lacking on its effects on public health.

OBJECTIVE : The aim of this study was to assess the impact of the use of big data analytics on people's health based on the health indicators and core priorities in the World Health Organization (WHO) General Programme of Work 2019/2023 and the European Programme of Work (EPW), approved and adopted by its Member States, in addition to SARS-CoV-2-related studies. Furthermore, we sought to identify the most relevant challenges and opportunities of these tools with respect to people's health.

METHODS : Six databases (MEDLINE, Embase, Cochrane Database of Systematic Reviews via Cochrane Library, Web of Science, Scopus, and Epistemonikos) were searched from the inception date to September 21, 2020. Systematic reviews assessing the effects of big data analytics on health indicators were included. Two authors independently performed screening, selection, data extraction, and quality assessment using the AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews 2) checklist.

RESULTS : The literature search initially yielded 185 records, 35 of which met the inclusion criteria, involving more than 5,000,000 patients. Most of the included studies used patient data collected from electronic health records, hospital information systems, private patient databases, and imaging datasets, and involved the use of big data analytics for noncommunicable diseases. "Probability of dying from any of cardiovascular, cancer, diabetes or chronic renal disease" and "suicide mortality rate" were the most commonly assessed health indicators and core priorities within the WHO General Programme of Work 2019/2023 and the EPW 2020/2025. Big data analytics have shown moderate to high accuracy for the diagnosis and prediction of complications of diabetes mellitus as well as for the diagnosis and classification of mental disorders; prediction of suicide attempts and behaviors; and the diagnosis, treatment, and prediction of important clinical outcomes of several chronic diseases. Confidence in the results was rated as "critically low" for 25 reviews, as "low" for 7 reviews, and as "moderate" for 3 reviews. The most frequently identified challenges were establishment of a well-designed and structured data source, and a secure, transparent, and standardized database for patient data.

CONCLUSIONS : Although the overall quality of included studies was limited, big data analytics has shown moderate to high accuracy for the diagnosis of certain diseases, improvement in managing chronic diseases, and support for prompt and real-time analyses of large sets of varied input data to diagnose and predict disease outcomes.

TRIAL REGISTRATION : International Prospective Register of Systematic Reviews (PROSPERO) CRD42020214048; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=214048.

Borges do Nascimento Israel Júnior, Marcolino Milena Soriano, Abdulazeem Hebatullah Mohamed, Weerasekara Ishanka, Azzopardi-Muscat Natasha, Gonçalves Marcos André, Novillo-Ortiz David

2021-Apr-13

World Health Organization, big data, big data analytics, evidence-based medicine, health status, machine learning, overview, public health, secondary data analysis, systematic review

Pathology Pathology

MSIFinder: a python package for detecting MSI status using random forest classifier.

In BMC bioinformatics

BACKGROUND : Microsatellite instability (MSI) is a common genomic alteration in colorectal cancer, endometrial carcinoma, and other solid tumors. MSI is characterized by a high degree of polymorphism in microsatellite lengths owing to the deficiency in the mismatch repair system. Based on the degree, MSI can be classified as microsatellite instability-high (MSI-H) and microsatellite stable (MSS). MSI is a predictive biomarker for immunotherapy efficacy in advanced/metastatic solid tumors, especially in colorectal cancer patients. Several computational approaches based on target panel sequencing data have been used to detect MSI; however, they are considerably affected by the sequencing depth and panel size.

RESULTS : We developed MSIFinder, a python package for automatic MSI classification, using random forest classifier (RFC)-based genome sequencing, which is a machine learning technology. We included 19 MSI-H and 25 MSS samples as training sets. First, we selected 54 feature markers from the training sets, built an RFC model, and validated the classifier using a test set comprising 21 MSI-H and 379 MSS samples. With this test set, MSIFinder achieved a sensitivity (recall) of 1.0, a specificity of 0.997, an accuracy of 0.998, a positive predictive value of 0.954, an F1 score of 0.977, and an area under the curve of 0.999. To further verify the robustness and effectiveness of the model, we used a prospective cohort consisting of 18 MSI-H samples and 122 MSS samples. MSIFinder achieved a sensitivity (recall) of 1.0 and a specificity of 1.0. We discovered that MSIFinder is less affected by a low sequencing depth and can achieve a concordance of 0.993 while exhibiting a sequencing depth of 100×. Furthermore, we realized that MSIFinder is less affected by the panel size and can achieve a concordance of 0.99 when the panel size is 0.5 M (million bases).

CONCLUSION : These results indicate that MSIFinder is a robust and effective MSI classification tool that can provide reliable MSI detection for scientific and clinical purposes.

Zhou Tao, Chen Libin, Guo Jing, Zhang Mengmeng, Zhang Yanrui, Cao Shanbo, Lou Feng, Wang Haijun

2021-Apr-12

Genome sequencing, Immunotherapy, Machine learning technology, Microsatellite instability, Random forest classifier

Radiology Radiology

Quantitative analysis based on chest CT classifies common and severe patients with coronavirus disease 2019 pneumonia in Wuhan, China.

In Chinese journal of academic radiology

Objective : This study aimed to compare quantifiable radiologic findings and their dynamic change throughout the clinical course of common and severe coronavirus disease 2019 (COVID-19), and to provide valuable evidence for radiologic classification of the two types of this disease.

Methods : 112 patients with laboratory-confirmed COVID-19 were retrospectively analyzed. Volumetric percentage of infection and density of the lung were measured by a computer-aided software. Clinical parameters were recorded to reflect disease progression. Baseline data and dynamic change were compared between two groups and a decision-tree algorithm was developed to determine the cut-off value for classification.

Results : 93 patients were finally included and were divided into common group (n = 76) and severe group (n = 17) based on current criteria. Compared with common patients, severe patients experienced shorter advanced stage, peak time and plateau, but longer absorption stage. The dynamic change of volume and density coincided with the clinical course. The interquartile range of volumetric percentage of the two groups were 1.0-7.2% and 11.4-31.2%, respectively. Baseline volumetric percentage of infection was significantly higher in severe group, and the cut-off value of it was 10.10%.

Conclusions : Volumetric percentage between severe and common patients was significantly different. Because serial CT scans are systemically performed in patients with COVID-19 pneumonia, this quantitative analysis can simultaneously provide valuable information for physicians to evaluate their clinical course and classify common and severe patients accurately.

Yang Chongtu, Cao Guijuan, Liu Fen, Liu Jiacheng, Huang Songjiang, Xiong Bin

2021-Apr-08

Artificial intelligence, Computer-assisted, Coronavirus disease 2019, Decision trees, Multidetector computed tomography, Numerical analysis

General General

Application of network link prediction in drug discovery.

In BMC bioinformatics

BACKGROUND : Technological and research advances have produced large volumes of biomedical data. When represented as a network (graph), these data become useful for modeling entities and interactions in biological and similar complex systems. In the field of network biology and network medicine, there is a particular interest in predicting results from drug-drug, drug-disease, and protein-protein interactions to advance the speed of drug discovery. Existing data and modern computational methods allow to identify potentially beneficial and harmful interactions, and therefore, narrow drug trials ahead of actual clinical trials. Such automated data-driven investigation relies on machine learning techniques. However, traditional machine learning approaches require extensive preprocessing of the data that makes them impractical for large datasets. This study presents wide range of machine learning methods for predicting outcomes from biomedical interactions and evaluates the performance of the traditional methods with more recent network-based approaches.

RESULTS : We applied a wide range of 32 different network-based machine learning models to five commonly available biomedical datasets, and evaluated their performance based on three important evaluations metrics namely AUROC, AUPR, and F1-score. We achieved this by converting link prediction problem as binary classification problem. In order to achieve this we have considered the existing links as positive example and randomly sampled negative examples from non-existant set. After experimental evaluation we found that Prone, ACT and [Formula: see text] are the top 3 best performers on all five datasets.

CONCLUSIONS : This work presents a comparative evaluation of network-based machine learning algorithms for predicting network links, with applications in the prediction of drug-target and drug-drug interactions, and applied well known network-based machine learning methods. Our work is helpful in guiding researchers in the appropriate selection of machine learning methods for pharmaceutical tasks.

Abbas Khushnood, Abbasi Alireza, Dong Shi, Niu Ling, Yu Laihang, Chen Bolun, Cai Shi-Min, Hasan Qambar

2021-Apr-12

Data-driven drug discovery, Drug-target prediction, Network link prediction, Poly-pharmacy, Poly-pharmacy side effects prediction

General General

Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images.

In Biomedical signal processing and control

The coronavirus (COVID-19) is currently the most common contagious disease which is prevalent all over the world. The main challenge of this disease is the primary diagnosis to prevent secondary infections and its spread from one person to another. Therefore, it is essential to use an automatic diagnosis system along with clinical procedures for the rapid diagnosis of COVID-19 to prevent its spread. Artificial intelligence techniques using computed tomography (CT) images of the lungs and chest radiography have the potential to obtain high diagnostic performance for Covid-19 diagnosis. In this study, a fusion of convolutional neural network (CNN), support vector machine (SVM), and Sobel filter is proposed to detect COVID-19 using X-ray images. A new X-ray image dataset was collected and subjected to high pass filter using a Sobel filter to obtain the edges of the images. Then these images are fed to CNN deep learning model followed by SVM classifier with ten-fold cross validation strategy. This method is designed so that it can learn with not many data. Our results show that the proposed CNN-SVM with Sobel filter (CNN-SVM + Sobel) achieved the highest classification accuracy, sensitivity and specificity of 99.02%, 100% and 95.23%, respectively in automated detection of COVID-19. It showed that using Sobel filter can improve the performance of CNN. Unlike most of the other researches, this method does not use a pre-trained network. We have also validated our developed model using six public databases and obtained the highest performance. Hence, our developed model is ready for clinical application.

Sharifrazi Danial, Alizadehsani Roohallah, Roshanzamir Mohamad, Joloudari Javad Hassannataj, Shoeibi Afshin, Jafari Mahboobeh, Hussain Sadiq, Sani Zahra Alizadeh, Hasanzadeh Fereshteh, Khozeimeh Fahime, Khosravi Abbas, Nahavandi Saeid, Panahiazar Maryam, Zare Assef, Islam Sheikh Mohammed Shariful, Acharya U Rajendra

2021-Apr-08

CNN., Covid-19, Data Mining, Deep Learning, Feature Extraction, Image Processing, Machine Learning, SVM, Sobel operator

Radiology Radiology

Real-World Experience with Artificial Intelligence-Based Triage in Transferred Large Vessel Occlusion Stroke Patients.

In Cerebrovascular diseases (Basel, Switzerland)

BACKGROUND AND PURPOSE : Randomized controlled trials have demonstrated the importance of time to endovascular therapy (EVT) in clinical outcomes in large vessel occlusion (LVO) acute ischemic stroke. Delays to treatment are particularly prevalent when patients require a transfer from hospitals without EVT capability onsite. A computer-aided triage system, Viz LVO, has the potential to streamline workflows. This platform includes an image viewer, a communication system, and an artificial intelligence (AI) algorithm that automatically identifies suspected LVO strokes on CTA imaging and rapidly triggers alerts. We hypothesize that the Viz application will decrease time-to-treatment, leading to improved clinical outcomes.

METHODS : A retrospective analysis of a prospectively maintained database was assessed for patients who presented to a stroke center currently utilizing Viz LVO and underwent EVT following transfer for LVO stroke between July 2018 and March 2020. Time intervals and clinical outcomes were compared for 55 patients divided into pre- and post-Viz cohorts.

RESULTS : The median initial door-to-neuroendovascular team (NT) notification time interval was significantly faster (25.0 min [IQR = 12.0] vs. 40.0 min [IQR = 61.0]; p = 0.01) with less variation (p < 0.05) following Viz LVO implementation. The median initial door-to-skin puncture time interval was 25 min shorter in the post-Viz cohort, although this was not statistically significant (p = 0.15).

CONCLUSIONS : Preliminary results have shown that Viz LVO implementation is associated with earlier, more consistent NT notification times. This application can serve as an early warning system and a failsafe to ensure that no LVO is left behind.

Morey Jacob R, Zhang Xiangnan, Yaeger Kurt A, Fiano Emily, Marayati Naoum Fares, Kellner Christopher P, De Leacy Reade A, Doshi Amish, Tuhrim Stanley, Fifi Johanna T

2021-Apr-13

Artificial intelligence, CT angiography, Stroke, Technology, Thrombectomy

General General

Machine Learning Algorithms for Predicting Fatty Liver Disease.

In Annals of nutrition & metabolism

BACKGROUND : Fatty liver disease (FLD) has become a rampant condition. It is associated with a high rate of morbidity and mortality in a population. The condition is commonly referred as FLD. Early prediction of FLD would allow patients to take necessary preventive, diagnosis, and treatment. The main objective of this research is to develop a machine learning (ML) model to predict FLD that can help medics to classify individuals at high risk of FLD, make novel diagnosis, management, and prevention for FLD.

METHODS : Total of 3,419 subjects were recruited with 845 having been screened for FLD. Classification models were used in the detection of the disease. These models include logistic regression (LR), random forest (RF), artificial neural networks (ANNs), k-nearest neighbors (KNNs), extreme gradient boosting (XGBoost), and linear discriminant analysis (LDA). Predictive accuracy was assessed by area under curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value.

RESULTS : We demonstrated that ML models give more accurate predictions, the best accuracy reached to 0.9415 in the XGBoost model. Feature importance analysis not only confirmed some well-known FLD risk factors, but also demonstrated several novel features for predicting the risk of FLD, such as hemoglobin.

CONCLUSION : By implementing the XGBoost model, physicians can efficiently identify FLD in general patients; this would help in prevention, early treatment, and management of FLD.

Pei Xieyi, Deng Qingqing, Liu Zhuo, Yan Xiang, Sun Weiping

2021-Apr-13

Classification model, Extreme gradient boosting, Fatty liver disease, Machine learning

Radiology Radiology

Current Status and Quality of Machine Learning-Based Radiomics Studies for Glioma Grading: A Systematic Review.

In Oncology

INTRODUCTION : Radiomics now has significant momentum in the era of precision medicine. Glioma is one of the pathologies that has been extensively evaluated by radiomics. However, this technique has not been incorporated into clinical practice. In this systematic review, we selected and reviewed the published studies about glioma grading by radiomics to evaluate this technique's feasibility and its challenges.

MATERIAL AND METHODS : Using seven different search strings, we considered all published English manuscripts from 2015 to September 2020 in PubMed, Embase, and Scopus databases. After implementing the exclusion and inclusion criteria, the final papers were selected for the methodological quality assessment based on our in-house Modified Radiomics Standard Scoring (RQS) containing 43 items (minimum score of 0, maximum score of 44). Finally, we offered our opinion about the challenges and weaknesses of the selected papers.

RESULTS : By our search, 1,177 manuscripts were found (485 in PubMed, 343 in Embase, and 349 in Scopus). After the implementation of inclusion and exclusion criteria, 18 papers remained for the final analysis by RQS. The total RQS score ranged from 26 (59% of maximum possible score) to 43 (97% of maximum possible score) with a mean of 33.5 (76% of maximum possible score).

CONCLUSION : The current studies are promising but very heterogeneous in design with high variation in the radiomics software, the number of extracted features, the number of selected features, and machine learning models. All of the studies were retrospective in design; many are based on small datasets and/or suffer from class imbalance and lack of external validation data-sets.

Tabatabaei Mohsen, Razaei Ali, Sarrami Amir Hossein, Saadatpour Zahra, Singhal Aparna, Sotoudeh Houman

2021-Apr-13

Artificial intelligence, Glioma, Neoplasm grading, Systematic review

General General

Automatic classification of idiopathic Parkinson's disease and atypical parkinsonian syndromes combining [11C]raclopride PET uptake and MRI grey matter morphometry.

In Journal of neural engineering ; h5-index 52.0

OBJECTIVE : To explore the viability of developing a computer-aided diagnostic system for Parkinsonian syndromes using dynamic [11C]raclopride PET and T1-weighted MRI data.

APPROACH : The biological heterogeneity of Parkinsonian syndromes renders their statistical classification a challenge. The unique combination of structural and molecular imaging data allowed different classifier designs to be tested. Datasets from dynamic [11C]raclopride PET and T1-weighted MRI scans were acquired from six groups of participants: healthy controls (CTRL n=15), patients with Parkinson's disease (PD n=27), multiple system atrophy (MSA n=8), corticobasal degeneration (CBD n=6), and dementia with Lewy bodies (DLB n=5). MSA, CBD, and DLB patients were classified into one category designated as atypical parkinsonism (AP). The distribution volume ratio (DVR) kinetic parameters obtained from PET data were used to quantify the reversible tracer binding to D2/D3 receptors in the subcortical regions of interest (ROI). Grey matter (GM) volumes obtained from the MRI data were used to quantify GM atrophy across cortical, subcortical, and cerebellar ROI.

RESULTS : The classifiers CTRL vs PD and CTRL vs AP achieved the highest balanced accuracy combining DVR and GM (DVR-GM) features (96.7%, 92.1%, respectively), followed by the classifiers designed with DVR features (93.3%, 88.8%, respectively), and GM features (69.6%, 86.1%, respectively). In contrast, the classifier PD vs AP showed the highest balanced accuracy (78.9%) using DVR features only. The integration of DVR-GM (77.9%) and GM features (72.7%) produced inferior performances. The classifier CTRL vs PD vs AP showed high weighted balanced accuracy when DVR (80.5%) or DVR-GM features (79.9%) were integrated. GM features revealed poorer performance (59.5%).

SIGNIFICANCE : This work was unique in its combination of structural and molecular imaging features in binary and triple category classifications. We were able to demonstrate improved binary classification of healthy/diseased status (concerning both PD and AP) and equate performance to DVR features in multiclass classifications.

Martins Ricardo, Oliveira Francisco Paulo Marques, Moreira Fradique, Moreira Ana Paula, Abrunhosa Antero, Januario Cristina, Castelo-Branco Miguel

2021-Apr-13

11C-Raclopride positron emission tomography, Computer-aided diagnosis, Parkinsonian syndromes, machine learning, magnetic resonance imaging, multimodality imaging

Radiology Radiology

Deep CNN-Based CAD System for COVID-19 Detection Using Multiple Lung CT Scans.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Due to the COVID-19 pandemic and the imminent collapse of healthcare systems following the excessive consumption of financial, hospital, and medicinal resources, the WHO changed the alert level on the COVID-19 pandemic from high to very high. Meanwhile, the world began to favor less expensive and more precise COVID-19 detection methods.

OBJECTIVE : Machine vision-based COVID-19 detection methods especially Deep learning as a diagnostic technique in the early stages of the disease have found great importance during the pandemic. This study aimed to design a highly efficient CAD system for COVID-19 by using a NASNet-based algorithm.

METHODS : A state-of-the-art pre-trained CNN network for image feature extraction, called NASNet, was adopted to identify patients with COVID-19 in the first stages of the disease. A local dataset, comprising 10153 CT-scan images of 190 patients with COVID-19 and 59 with Non Covid-19, was used.

RESULTS : After fitting on the training dataset, hyper-parameter tuning and finally topological alterations of the classifier block, the proposed NASNet-based model was evaluated on the test dataset and yielded remarkable results. The proposed model's performance achieved a detection sensitivity, specificity, and accuracy of 0.999, 0.986, and 0.996, respectively.

CONCLUSIONS : The proposed model achieved acceptable results in the categorization of two data classes. Therefore, a CAD system was designed based on this model for COVID-19 detection using multiple lung CT scans. The system managed to differentiate all the COVID-19 cases from non-COVID-19 ones without any error in the application phase. Overall, the proposed deep learning-based CAD system can greatly aid radiologists in the detection of COVID-19 in its early stages. During the COVID-19 pandemic, the use of CAD system as a screening tool accelerates the process of disease detection and prevents the loss of healthcare resources.

CLINICALTRIAL :

Ghaderzadeh Mustafa, Asadi Farkhondeh, Jafari Ramezan, Bashash Davood, Abolghasemi Hassan, Aria Mehrad

2021-Apr-03

Surgery Surgery

Machine Learning and Surgical Outcomes Prediction: A Systematic Review.

In The Journal of surgical research

BACKGROUND : Machine learning (ML) has garnered increasing attention as a means to quantitatively analyze the growing and complex medical data to improve individualized patient care. We herein aim to critically examine the current state of ML in predicting surgical outcomes, evaluate the quality of currently available research, and propose areas of improvement for future uses of ML in surgery.

METHODS : A systematic review was conducted in accordance with the Preferred Reporting Items for a Systematic Review and Meta-Analysis (PRISMA) checklist. PubMed, MEDLINE, and Embase databases were reviewed under search syntax "machine learning" and "surgery" for papers published between 2015 and 2020.

RESULTS : Of the initial 2677 studies, 45 papers met inclusion and exclusion criteria. Fourteen different subspecialties were represented with neurosurgery being most common. The most frequently used ML algorithms were random forest (n = 19), artificial neural network (n = 17), and logistic regression (n = 17). Common outcomes included postoperative mortality, complications, patient reported quality of life and pain improvement. All studies which compared ML algorithms to conventional studies which used area under the curve (AUC) to measure accuracy found improved outcome prediction with ML models.

CONCLUSIONS : While still in its early stages, ML models offer surgeons an opportunity to capitalize on the myriad of clinical data available and improve individualized patient care. Limitations included heterogeneous outcome and imperfect quality of some of the papers. We therefore urge future research to agree upon methods of outcome reporting and require basic quality standards.

Elfanagely Omar, Toyoda Yoshiko, Othman Sammy, Mellia Joseph A, Basta Marten, Liu Tony, Kording Konrad, Ungar Lyle, Fischer John P

2021-Apr-10

Artificial intelligence, Machine learning, Natural language processing, Surgical outcomes

Radiology Radiology

Differentiation Between Osteoporotic And Neoplastic Vertebral Fractures: State Of The Art And Future Perspectives.

In Current medical imaging

Vertebral fractures are a common condition, occurring in the context of osteoporosis and malignancy. These entities affect a group of patients in the same age range; clinical features may be indistinct and symptoms non-existing, and thus present challenges to diagnosis. In this article, we review the use and accuracy of different imaging modalities available to characterize vertebral fracture etiology, from well-established classical techniques, to the role of new and advanced imaging techniques, and the prospective use of artificial intelligence. We also address the role of imaging on treatment. In the context of osteoporosis, the importance of opportunistic diagnosis is highlighted. In the near future, the use of automated computer-aided diagnostic algorithms applied to different imaging techniques may be really useful to aid on diagnosis.

Musa Aguiar Paula, Zarantonello Paola, Aparisi Gómez Maria Pilar

2021-04-12

CT, DXA, Imaging, MRI, Neoplastic, Osteoporosis, Radiographs, Vertebral Fracture

General General

A novel framework of collaborative early warning for COVID-19 based on blockchain and smart contracts.

In Information sciences

Early warning is a vital component of emergency repsonse systems for infectious diseases. However, most early warning systems are centralized and isolated, thus there are potential risks of single evidence bias and decision-making errors. In this paper, we tackle this issue via proposing a novel framework of collaborative early warning for COVID-19 based on blockchain and smart contracts, aiming to crowdsource early warning tasks to distributed channels including medical institutions, social organinzations, and even individuals. Our framework supports two surveillance modes, namely, medical federation surveillance based on federated learning and social collaboration surveillance based on the learning markets approach, and fuses their monitoring results on emerging cases to alert. By using our framework, medical institutions are expected to obtain better federated surveillance models with privacy protection, and social participants without mutual trusts can also share verified surveillance resources such as data and models, and fuse their surveillance solutions. We implemented our proposed framework based on the Ethereum and IPFS platforms. Experimental results show that our framework has advantages of decentralized decision-making, fairness, auditability, and universality, and also has potential guidance and reference value for the early warning and prevention of unknown infectious diseases.

Ouyang Liwei, Yuan Yong, Cao Yumeng, Wang Fei-Yue

2021-Apr-08

blockchain, collaborative early warning, federated learning, learning markets, smart contracts

Radiology Radiology

A multiparametric MRI-based CAD system for accurate diagnosis of bladder cancer staging.

In Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

Appropriate treatment of bladder cancer (BC) is widely based on accurate and early BC staging. In this paper, a multiparametric computer-aided diagnostic (MP-CAD) system is developed to differentiate between BC staging, especially T1 and T2 stages, using T2-weighted (T2W) magnetic resonance imaging (MRI) and diffusion-weighted (DW) MRI. Our framework starts with the segmentation of the bladder wall (BW) and localization of the whole BC volume (Vt) and its extent inside the wall (Vw). Our segmentation framework is based on a fully connected convolution neural network (CNN) and utilized an adaptive shape model followed by estimating a set of functional, texture, and morphological features. The functional features are derived from the cumulative distribution function (CDF) of the apparent diffusion coefficient. Texture features are radiomic features estimated from T2W-MRI, and morphological features are used to describe the tumors' geometric. Due to the significant texture difference between the wall and bladder lumen cells, Vt is parcelled into a set of nested equidistance surfaces (i.e., iso-surfaces). Finally, features are estimated for individual iso-surfaces, which are then augmented and used to train and test machine learning (ML) classifier based on neural networks. The system has been evaluated using 42 data sets, and a leave-one-subject-out approach is employed. The overall accuracy, sensitivity, specificity, and area under the receiver operating characteristics (ROC) curve (AUC) are 95.24%, 95.24%, 95.24%, and 0.9864, respectively. The advantage of fusion multiparametric iso-features is highlighted by comparing the diagnostic accuracy of individual MRI modality, which is confirmed by the ROC analysis. Moreover, the accuracy of our pipeline is compared against other statistical ML classifiers (i.e., random forest (RF) and support vector machine (SVM)). Our CAD system is also compared with other techniques (e.g., end-to-end convolution neural networks (i.e., ResNet50).

Hammouda K, Khalifa F, Soliman A, Ghazal M, El-Ghar M Abou, Badawy M A, Darwish H E, Khelifi A, El-Baz A

2021-Mar-31

CAD system, Classification bladder cancer staging, Functional features, Morphological features, Texture features

Radiology Radiology

A cascade and heterogeneous neural network for CT pulmonary nodule detection and its evaluation on both phantom and patient data.

In Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

Screening of pulmonary nodules in computed tomography (CT) is crucial for early diagnosis and treatment of lung cancer. Although computer-aided diagnosis (CAD) systems have been designed to assist radiologists to detect nodules, fully automated detection is still challenging due to variations in nodule size, shape, and density. In this paper, we first propose a fully automated nodule detection method using a cascade and heterogeneous neural network trained on chest CT images of 12155 patients, then evaluate the performance by using phantom (828 CT images) and clinical datasets (2640 CT images) scanned with different imaging parameters. The nodule detection network employs two feature pyramid networks (FPNs) and a classification network (BasicNet). The first FPN is trained to achieve high sensitivity for nodule detection, and the second FPN refines the candidates for false positive reduction (FPR). Then, a BasicNet is combined with the second FPR to classify the candidates into either nodules or non-nodules for the final refinement. This study investigates the performance of nodule detection of solid and ground-glass nodules in phantom and patient data scanned with different imaging parameters. The results show that the detection of the solid nodules is robust to imaging parameters, and for GGO detection, reconstruction methods "iDose4-YA" and "STD-YA" achieve better performance. For thin-slice images, higher performance is achieved across different nodule sizes with reconstruction method "iDose4-STD". For 5 mm slice thickness, the best choice is the reconstruction method "iDose4-YA" for larger nodules (>5 mm). Overall, the reconstruction method "iDose4-YA" is suggested to achieve the best balanced results for both solid and GGO nodules.

Xiao Yi, Wang Xiang, Li Qingchu, Fan Rongrong, Chen Rutan, Shao Ying, Chen Yanbo, Gao Yaozong, Liu Aie, Chen Lei, Liu Shiyuan

2021-Mar-04

Computed tomography (CT), Deep learning, Lung nodule detection, Phantom

Surgery Surgery

Computational diagnostic methods on 2D photographs: a review of the literature.

In Journal of stomatology, oral and maxillofacial surgery

Here we provide a literature review of all the methods reported to date for analyzing 2D pictures for diagnostic purposes. Pubmed was used to screen the MEDLINE database using MeSH (Medical Subject Heading) terms and keyworks. The different recognition steps and the main results were reported. All human studies involving 2D facial photographs used to diagnose one or several conditions in healthy populations or in patients were included. We included 1515 articles and 27 publications were finally retained. 67% of the articles aimed at diagnosing one particular syndrome versus healthy controls and 33% aimed at performing multi-class syndrome recognition. Data volume varied from 15 to 17,106 patient pictures. Manual or automatic landmarks were one of the most commonly used tools in order to extract morphological information from images, in 22/27 (81%) publications. Geometrical features were extracted from landmarks based on Procrustes superimposition in 4/27 (15%). Textural features were extracted in 19/27 (70%) publications. Features were then classified using machine learning methods in 89% of publications, while deep learning methods were used in 11%. Facial recognition tools were generally successful in identifying rare conditions in dysmorphic patients, with comparable or higher recognition accuracy than clinical experts.

Hennocq Quentin, Khonsari Roman Hossein, Benoît Vincent, Rio Marlène, Garcelon Nicolas

2021-Apr-10

deep learning, diagnosis, dysmorphology, literature review, machine learning, photograph

General General

A sequence-based prediction of Kruppel-like factors proteins using XGBoost and optimized features.

In Gene

Krüppel-like factors (KLF) refer to a group of conserved zinc finger-containing transcription factors that are involved in various physiological and biological processes, including cell proliferation, differentiation, development, and apoptosis. Some bioinformatics methods such as sequence similarity searches, multiple sequence alignment, phylogenetic reconstruction, and gene synteny analysis have also been proposed to broaden our knowledge of KLF proteins. In this study, we proposed a novel computational approach by using machine learning on features calculated from primary sequences. To detail, our XGBoost-based model is efficient in identifying KLF proteins, with accuracy of 96.4% and MCC of 0.704. It also holds a promising performance when testing our model on an independent dataset. Therefore, our model could serve as an useful tool to identify new KLF proteins and provide necessary information for biologists and researchers in KLF proteins. Our machine learning source codes as well as datasets are freely available at https://github.com/khanhlee/KLF-XGB.

Quoc Khanh Le Nguyen, Thi Do Duyen, Nguyen Trinh-Trung-Duong, Anh Le Quynh

2021-Apr-10

Kruppel-like factor, SMOTE imbalance, Zinc finger, eXtreme Gradient Boosting, feature selection, protein sequence

Ophthalmology Ophthalmology

Development of classification criteria for the uveitides.

In American journal of ophthalmology ; h5-index 67.0

PURPOSE : To develop classification criteria for 25 of the most common uveitides.

DESIGN : Machine learning using 5766 cases of 25 uveitides.

METHODS : Cases were collected in an informatics-designed preliminary database. Using formal consensus techniques, a final database was constructed of 4046 cases achieving supermajority agreement on the diagnosis. Cases were analyzed within uveitic class and were split into a training set and a validation set. Machine learning used multinomial logistic regression with lasso regularization on the training set to determine a parsimonious set of criteria for each disease and to minimize misclassification rates. The resulting criteria were evaluated in the validation set. Accuracy of the rules developed to express the machine learning criteria was evaluated by a masked observer in a 10% random sample of cases.

RESULTS : Overall accuracy estimates by uveitic class in the validation set were: anterior uveitides 96.7% (95% confidence interval [CI] 92.4, 98.6); intermediate uveitides 99.3% (95% CI 96.1, 99.9); posterior uveitides 98.0% (95% CI 94.3, 99.3); panuveitides 94.0% (95% CI 89.0, 96.8); and infectious posterior/panuveitides 93.3% (95% CI 89.1, 96.3). Accuracies of the masked evaluation of the "rules" were: anterior uveitides 96.5% (95% CI 91.4, 98.6) intermediate uveitides 98.4% (91.5, 99.7), posterior uveitides 99.2% (95% CI 95.4, 99.9), panuveitides 98.9% (95% CI 94.3, 99.8), and infectious posterior/panuveitides 98.8% (95% CI 93.4, 99.9).

CONCLUSIONS : The classification criteria for these 25 uveitides had high overall accuracy (i.e. low misclassification rates) and appeared to perform well enough for use in clinical and translational research.

Jabs Douglas A, McCluskey Peter, Oden Neal, Palestine Alan G, Peterson Jan S, Saleem Sophia M, Thorne Jennifer E, Trusko Brett E

2021-Apr-10

General General

Artificial Intelligence in hair research: a proof-of-concept study on evaluating hair assembly features.

In International journal of cosmetic science

OBJECTIVE : The first objective of this study was to apply computer vision and machine learning techniques to quantify the effects of haircare treatments on hair assembly and to identify correctly whether unknown tresses were treated or not. The second objective was to explore and compare the performance of human assessment with that obtained from artificial intelligence (AI) algorithms.

METHODS : Machine learning was applied to a dataset of hair tress images (virgin and bleached), both untreated and treated with a shampoo and conditioner set, aimed at increasing hair volume whilst improving alignment and reducing the flyway of the hair. The automatic quantification of the following hair image features was conducted: local and global hair volumes and hair alignment. These features were assessed at three time points: t0 (no treatment), t1 (two treatments), t2 (three treatments). Classifier tests were applied to test the accuracy of the machine learning. A sensory test (paired comparison of t0 vs t2 ) and an online front-image based survey (paired comparison of t0 vs t1 , t1 vs t2 , t0 vs t2 ) were conducted to compare human assessment with that of the algorithms.

RESULTS : The automatic image analysis identified changes to hair volume and alignment which enabled the successful application of the classification tests, especially when the hair images were grouped into untreated and treated groups. The human assessment of hair presented in pairs confirmed the automatic image analysis. The image assessment for both virgin hair and bleached only partially agreed with the analysis of the subset of images used in the online survey. One hypothesis is that treatments changed somewhat the shape of the hair tress, with the effect being more pronounced in bleached hair. This made human assessment of flat images more challenging than when viewed directly in 3D. Overall, the bleached hair exhibited effects of higher magnitude than the virgin hair.

CONCLUSIONS : This study illustrated the capacity of artificial intelligence for hair image detection and classification, and for image analysis of hair assembly features following treatments. The human assessment partially confirmed the image analysis, and highlighted the challenges imposed by the presentation mode.

Daniels Gabriela, Tamburic Slobodanka, Benini Sergio, Randall Jane, Sanderson Tracey, Savardi Mattia

2021-Apr-13

artificial intelligence, bleached hair, hair detection, hair segmentation, machine learning, sensory assessment, virgin hair

General General

Classification of neurological diseases using multi-dimensional cerebrospinal fluid analysis.

In Brain : a journal of neurology

Although cerebrospinal fluid (CSF) analysis routinely enables diagnosis of neurological diseases, it is mainly used for gross distinction between infectious, autoimmune inflammatory, and degenerative central nervous system (CNS) disorders. To investigate, whether a multi-dimensional cellular blood and CSF characterization can support the diagnosis of clinically similar neurological diseases, we analyzed 546 patients with autoimmune neuro-inflammatory, degenerative, or vascular conditions in a cross-sectional retrospective study. By combining feature selection with dimensionality reduction and machine learning approaches we identified pan-disease parameters altered across all autoimmune neuro-inflammatory CNS-diseases and differentiating them from other neurological conditions and inter-autoimmunity classifiers sub-differentiating variants of CNS-directed autoimmunity. Pan-disease as well as diseases-specific changes formed a continuum, reflecting clinical disease evolution. A validation cohort of 231 independent patients confirmed that combining multiple parameters into composite scores can assist classification of neurological patients. Overall, we show that an integrated analysis of blood and CSF parameters improves differential diagnosis of neurological diseases, thereby facilitating early treatment decisions.

Gross Catharina C, Schulte-Mecklenbeck Andreas, Madireddy Lohith, Pawlitzki Marc, Strippel Christine, Räuber Saskia, Krämer Julia, Rolfes Leoni, Ruck Tobias, Beuker Carolin, Schmidt-Pogoda Antje, Lohmann Lisa, Schneider-Hohendorf Tilman, Hahn Tim, Schwab Nicholas, Minnerup Jens, Melzer Nico, Klotz Luisa, Meuth Sven G, Hörste Gerd Meyer Zu, Baranzini Sergio E, Wiendl Heinz

2021-Apr-12

CNS autoimmunity, CSF, differential diagnosis, immune profile, multiple sclerosis

General General

A heterogeneous ensemble learning method for neuroblastoma survival prediction.

In IEEE journal of biomedical and health informatics

Neuroblastoma is a pediatric cancer with high morbidity and mortality. Accurate survival prediction of patients with neuroblastoma plays an important role in the formulation of treatment plans. In this study, we proposed a heterogeneous ensemble learning method to predict the survival of neuroblastoma patients and extract decision rules from the proposed method to assist doctors in making decisions. After data preprocessing, five heterogeneous base learners were developed, which consisted of decision tree, random forest, support vector machine based on genetic algorithm, extreme gradient boosting and light gradient boosting machine. Subsequently, a heterogeneous feature selection method was devised to obtain the optimal feature subset of each base learner, and the optimal feature subset of each base learner guided the construction of the base learners as a priori knowledge. Furthermore, an area under curve-based ensemble mechanism was proposed to integrate the five heterogeneous base learners. Finally, the proposed method was compared with mainstream machine learning methods from different indicators, and valuable information was extracted by using the partial dependency plot analysis method and rule-extracted method from the proposed method. Experimental results show that the proposed method achieves an accuracy of 91.64%, recall of 91.14%, and AUC of 91.35% and is significantly better than the mainstream machine learning methods. In addition, interpretable rules with accuracy higher than 0.900 and predicted responses are extracted from the proposed method. Our study can effectively improve the performance of the clinical decision support system to improve the survival of neuroblastoma patients.

Feng Yi, Wang Xianglin, Zhang Juan

2021-Apr-13

General General

Attention-based LSTM for Non-Contact Sleep Stage Classification using IR-UWB radar.

In IEEE journal of biomedical and health informatics

Manual scoring of sleep stages from polysomnography (PSG) records is essential to understand the sleep quality and architecture. Since the PSG requires specialized personnel, a lab environment, and uncomfortable sensors, non-contact sleep staging methods based on machine learning techniques have been investigated over the past years. In this study, we propose an attention-based bidirectional long short-term memory (Attention Bi-LSTM) model for automatic sleep stage scoring using an impulse-radio ultra-wideband (IR-UWB) radar which can remotely detect vital signs. Sixty-five young (30.0 8.6 yrs.) and healthy volunteers underwent nocturnal PSG and IR-UWB radar measurement simultaneously; From 51 recordings, 26 were used for training, 8 for validation, and 17 for testing. Sixteen features including movement-, respiration-, and heart rate variability-related indices were extracted from the raw IR-UWB signals in each 30-s epoch. Sleep stage classification performances of Attention Bi-LSTM model with optimized hyperparameters were evaluated and compared with those of conventional LSTM networks for same test dataset. In the results, we achieved an accuracy of 82.6 6.7% and a Cohen's kappa coefficient of 0.73 0.11 in the classification of wake stage, REM sleep, light (N1+N2) sleep, and deep (N3) sleep which is significantly higher than the conventional LSTM networks (p < 0.01). Moreover, the classification performances were higher than those reported in comparative studies, demonstrating the effectiveness of the attention mechanism coupled with bi-LSTM networks for the sleep staging using cardiorespiratory signals.

Kwon Hyun Bin, Choi Sang Ho, Lee Dongseok, Son Dongyeon, Yoon Heenam, Lee Mi Hyun, Lee Yu Jin, Park Kwang Suk

2021-Apr-13

General General

Deep learning-based ECG-free Cardiac Navigation for Multi-Dimensional and Motion-Resolved Continuous Magnetic Resonance Imaging.

In IEEE transactions on medical imaging ; h5-index 74.0

For the clinical assessment of cardiac vitality, time-continuous tomographic imaging of the heart is used. To further detect e.g., pathological tissue, multiple imaging contrasts enable a thorough diagnosis using magnetic resonance imaging (MRI). For this purpose, time-continous and multi-contrast imaging protocols were proposed. The acquired signals are binned using navigation approaches for a motion-resolved reconstruction. Mostly, external sensors such as electrocardiograms (ECG) are used for navigation, leading to additional workflow efforts. Recent sensor-free approaches are based on pipelines requiring prior knowledge, e.g., typical heart rates. We present a sensor-free, deep learning-based navigation that diminishes the need for manual feature engineering or the necessity of prior knowledge compared to previous works. A classifier is trained to estimate the R-wave timepoints in the scan directly from the imaging data. Our approach is evaluated on 3-D protocols for continuous cardiac MRI, acquired in-vivo and free-breathing with single or multiple imaging contrasts. We achieve an accuracy of >98% on previously unseen subjects, and a well comparable image quality with the state-of-the-art ECG-based reconstruction. Our method enables an ECG-free workflow for continuous cardiac scans with simultaneous anatomic and functional imaging with multiple contrasts. It can be potentially integrated without adapting the sampling scheme to other continuous sequences by using the imaging data for navigation and reconstruction.

Hoppe Elisabeth, Wetzl Jens, Yoon Seung Su, Bacher Mario, Roser Philipp, Stimpel Bernhard, Preuhs Alexander, Maier Andreas

2021-Apr-13

Surgery Surgery

Abdominal Organ Transplantation: Noteworthy Literature in 2020.

In Seminars in cardiothoracic and vascular anesthesia ; h5-index 16.0

In 2020, we identified and screened over 490 peer-reviewed publications on pancreatic transplantation, over 500 on intestinal transplantation, and over 5000 on kidney transplantation. The liver transplantation section specially focused on clinical trials and systematic reviews published in 2020 and featured selected articles. This review highlights noteworthy literature pertinent to anesthesiologists and critical care physicians caring for patients undergoing abdominal organ transplantation. We explore a wide range of topics, including COVID-19 and organ transplantation, risk factors and outcomes, pain management, artificial intelligence, robotic donor surgery, and machine perfusion.

Wang Ryan F, Fagelman Erica J, Smith Natalie K, Sakai Tetsuro

2021-Apr-13

COVID-19, anesthesiology, intestine, kidney, liver, pancreas, transplantation

General General

COVID-19 information retrieval with deep-learning based semantic search, question answering, and abstractive summarization.

In NPJ digital medicine

The COVID-19 global pandemic has resulted in international efforts to understand, track, and mitigate the disease, yielding a significant corpus of COVID-19 and SARS-CoV-2-related publications across scientific disciplines. Throughout 2020, over 400,000 coronavirus-related publications have been collected through the COVID-19 Open Research Dataset. Here, we present CO-Search, a semantic, multi-stage, search engine designed to handle complex queries over the COVID-19 literature, potentially aiding overburdened health workers in finding scientific answers and avoiding misinformation during a time of crisis. CO-Search is built from two sequential parts: a hybrid semantic-keyword retriever, which takes an input query and returns a sorted list of the 1000 most relevant documents, and a re-ranker, which further orders them by relevance. The retriever is composed of a deep learning model (Siamese-BERT) that encodes query-level meaning, along with two keyword-based models (BM25, TF-IDF) that emphasize the most important words of a query. The re-ranker assigns a relevance score to each document, computed from the outputs of (1) a question-answering module which gauges how much each document answers the query, and (2) an abstractive summarization module which determines how well a query matches a generated summary of the document. To account for the relatively limited dataset, we develop a text augmentation technique which splits the documents into pairs of paragraphs and the citations contained in them, creating millions of (citation title, paragraph) tuples for training the retriever. We evaluate our system ( http://einstein.ai/covid ) on the data of the TREC-COVID information retrieval challenge, obtaining strong performance across multiple key information retrieval metrics.

Esteva Andre, Kale Anuprit, Paulus Romain, Hashimoto Kazuma, Yin Wenpeng, Radev Dragomir, Socher Richard

2021-Apr-12

Public Health Public Health

Predictive performance of machine and statistical learning methods: Impact of data-generating processes on external validity in the "large N, small p" setting.

In Statistical methods in medical research

Machine learning approaches are increasingly suggested as tools to improve prediction of clinical outcomes. We aimed to identify when machine learning methods perform better than a classical learning method. We hereto examined the impact of the data-generating process on the relative predictive accuracy of six machine and statistical learning methods: bagged classification trees, stochastic gradient boosting machines using trees as the base learners, random forests, the lasso, ridge regression, and unpenalized logistic regression. We performed simulations in two large cardiovascular datasets which each comprised an independent derivation and validation sample collected from temporally distinct periods: patients hospitalized with acute myocardial infarction (AMI, n = 9484 vs. n = 7000) and patients hospitalized with congestive heart failure (CHF, n = 8240 vs. n = 7608). We used six data-generating processes based on each of the six learning methods to simulate outcomes in the derivation and validation samples based on 33 and 28 predictors in the AMI and CHF data sets, respectively. We applied six prediction methods in each of the simulated derivation samples and evaluated performance in the simulated validation samples according to c-statistic, generalized R2, Brier score, and calibration. While no method had uniformly superior performance across all six data-generating process and eight performance metrics, (un)penalized logistic regression and boosted trees tended to have superior performance to the other methods across a range of data-generating processes and performance metrics. This study confirms that classical statistical learning methods perform well in low-dimensional settings with large data sets.

Austin Peter C, Harrell Frank E, Steyerberg Ewout W

2021-Apr-13

Machine learning, Monte Carlo simulations, data-generating process, generalized boosting methods, logistic regression, random forests

Public Health Public Health

AMD Genetics: Methods and Analyses for Association, Progression, and Prediction.

In Advances in experimental medicine and biology

Age-related macular degeneration (AMD) is a multifactorial neurodegenerative disease, which is a leading cause of vision loss among the elderly in the developed countries. As one of the most successful examples of genome-wide association study (GWAS), a large number of genetic studies have been conducted to explore the genetic basis for AMD and its progression, of which over 30 loci were identified and confirmed. In this chapter, we review the recent development and findings of GWAS for AMD risk and progression. Then, we present emerging methods and models for predicting AMD development or its progression using large-scale genetic data. Finally, we discuss a set of novel statistical and analytical methods that were recently developed to tackle the challenges such as analyzing bilateral correlated eye-level outcomes that are subject to censoring with high-dimensional genetic data. Future directions for analytical studies of AMD genetics are also proposed.

Yan Qi, Ding Ying, Weeks Daniel E, Chen Wei

2021

AMD genetics, GWAS, Machine learning, Prediction, Progression, Statistical methods

Internal Medicine Internal Medicine

Prognostic tools for elderly patients with sepsis: in search of new predictive models.

In Internal and emergency medicine ; h5-index 30.0

As a tool to support clinical decision-making, Mortality Prediction Models (MPM) can help clinicians stratify and predict patient risk. There are numerous scoring systems for patients with sepsis that predict sepsis-related mortality and the severity of sepsis. But there are currently no MPMs for adults with sepsis who meet the criteria of "good." Clinicians are unlikely to use complex MPMs that require extensive or expensive data collection to impede workflow. Machine learning applied to minimal medical records of patients diagnosed with sepsis can be a useful tool. Progress is needed in the development and validation of clinical decision support tools that can assist in patient risk stratification, prognosis, discussion of patient outcomes, and shared decision making.

Gamboa-Antiñolo Fernando-Miguel

2021-Apr-13

General General

An Explainable Artificial Intelligence Framework for the Deterioration Risk Prediction of Hepatitis Patients.

In Journal of medical systems ; h5-index 48.0

In recent years, artificial intelligence-based computer aided diagnosis (CAD) system for the hepatitis has made great progress. Especially, the complex models such as deep learning achieve better performance than the simple ones due to the nonlinear hypotheses of the real world clinical data. However,complex model as a black box, which ignores why it make a certain decision, causes the model distrust from clinicians. To solve these issues, an explainable artificial intelligence (XAI) framework is proposed in this paper to give the global and local interpretation of auxiliary diagnosis of hepatitis while retaining the good prediction performance. First, a public hepatitis classification benchmark from UCI is used to test the feasibility of the framework. Then, the transparent and black-box machine learning models are both employed to forecast the hepatitis deterioration. The transparent models such as logistic regression (LR), decision tree (DT)and k-nearest neighbor (KNN) are picked. While the black-box model such as the eXtreme Gradient Boosting (XGBoost), support vector machine (SVM), random forests (RF) are selected. Finally, the SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME) and Partial Dependence Plots (PDP) are utilized to improve the model interpretation of liver disease. The experimental results show that the complex models outperform the simple ones. The developed RF achieves the highest accuracy (91.9%) among all the models. The proposed framework combining the global and local interpretable methods improves the transparency of complex models, and gets insight into the judgments from the complex models, thereby guiding the treatment strategy and improving the prognosis of hepatitis patients. In addition, the proposed framework could also assist the clinical data scientists to design a more appropriate structure of CAD.

Peng Junfeng, Zou Kaiqiang, Zhou Mi, Teng Yi, Zhu Xiongyong, Zhang Feifei, Xu Jun

2021-Apr-13

Hepatitis, Local Interpretable Model-agnostic Explanations, Model interpretation, Partial Dependence Plots, SHapley Additive exPlanations

Radiology Radiology

Human-recognizable CT image features of subsolid lung nodules associated with diagnosis and classification by convolutional neural networks.

In European radiology ; h5-index 62.0

OBJECTIVES : The interpretability of convolutional neural networks (CNNs) for classifying subsolid nodules (SSNs) is insufficient for clinicians. Our purpose was to develop CNN models to classify SSNs on CT images and to investigate image features associated with the CNN classification.

METHODS : CT images containing SSNs with a diameter of ≤ 3 cm were retrospectively collected. We trained and validated CNNs by a 5-fold cross-validation method for classifying SSNs into three categories (benign and preinvasive lesions [PL], minimally invasive adenocarcinoma [MIA], and invasive adenocarcinoma [IA]) that were histologically confirmed or followed up for 6.4 years. The mechanism of CNNs on human-recognizable CT image features was investigated and visualized by gradient-weighted class activation map (Grad-CAM), separated activation channels and areas, and DeepDream algorithm.

RESULTS : The accuracy was 93% for classifying 586 SSNs from 569 patients into three categories (346 benign and PL, 144 MIA, and 96 IA in 5-fold cross-validation). The Grad-CAM successfully located the entire region of image features that determined the final classification. Activated areas in the benign and PL group were primarily smooth margins (p < 0.001) and ground-glass components (p = 0.033), whereas in the IA group, the activated areas were mainly part-solid (p < 0.001) and solid components (p < 0.001), lobulated shapes (p < 0.001), and air bronchograms (p < 0.001). However, the activated areas for MIA were variable. The DeepDream algorithm showed the image features in a human-recognizable pattern that the CNN learned from a training dataset.

CONCLUSION : This study provides medical evidence to interpret the mechanism of CNNs that helps support the clinical application of artificial intelligence.

KEY POINTS : • CNN achieved high accuracy (93%) in classifying subsolid nodules on CT images into three categories: benign and preinvasive lesions, MIA, and IA. • The gradient-weighted class activation map (Grad-CAM) located the entire region of image features that determined the final classification, and the visualization of the separated activated areas was consistent with radiologists' expertise for diagnosing subsolid nodules. • DeepDream showed the image features that CNN learned from a training dataset in a human-recognizable pattern.

Jiang Beibei, Zhang Yaping, Zhang Lu, H de Bock Geertruida, Vliegenthart Rozemarijn, Xie Xueqian

2021-Apr-13

Adenocarcinoma of lung, Artificial intelligence, Deep learning, X-ray computed tomography

General General

Distributed learning: a reliable privacy-preserving strategy to change multicenter collaborations using AI.

In European journal of nuclear medicine and molecular imaging ; h5-index 66.0

PURPOSE : The present scoping review aims to assess the non-inferiority of distributed learning over centrally and locally trained machine learning (ML) models in medical applications.

METHODS : We performed a literature search using the term "distributed learning" OR "federated learning" in the PubMed/MEDLINE and EMBASE databases. No start date limit was used, and the search was extended until July 21, 2020. We excluded articles outside the field of interest; guidelines or expert opinion, review articles and meta-analyses, editorials, letters or commentaries, and conference abstracts; articles not in the English language; and studies not using medical data. Selected studies were classified and analysed according to their aim(s).

RESULTS : We included 26 papers aimed at predicting one or more outcomes: namely risk, diagnosis, prognosis, and treatment side effect/adverse drug reaction. Distributed learning was compared to centralized or localized training in 21/26 and 14/26 selected papers, respectively. Regardless of the aim, the type of input, the method, and the classifier, distributed learning performed close to centralized training, but two experiments focused on diagnosis. In all but 2 cases, distributed learning outperformed locally trained models.

CONCLUSION : Distributed learning resulted in a reliable strategy for model development; indeed, it performed equally to models trained on centralized datasets. Sensitive data can get preserved since they are not shared for model development. Distributed learning constitutes a promising solution for ML-based research and practice since large, diverse datasets are crucial for success.

Kirienko Margarita, Sollini Martina, Ninatti Gaia, Loiacono Daniele, Giacomello Edoardo, Gozzi Noemi, Amigoni Francesco, Mainardi Luca, Lanzi Pier Luca, Chiti Arturo

2021-Apr-13

Clinical trial, Distributed learning, Ethics, Federated learning, Machine learning, Privacy

General General

Prediction of count phenotypes using high-resolution images and genomic data.

In G3 (Bethesda, Md.)

Genomic selection (GS) is revolutionizing plant breeding since the selection process is done with the help of statistical machine learning methods. A model is trained with a reference population and then it is used for predicting the candidate individuals available in the testing set. However, given that breeding phenotypic values are very noisy, new models must be able to integrate not only genotypic and environmental data but also high-resolution images that have been collected by breeders with advanced image technology. For this reason, this paper explores the use of generalized Poisson regression (GPR) for genome-enabled prediction of count phenotypes using genomic and hyperspectral images. The GPR model allows integrating input information of many sources like environments, genomic data, high resolution data, and interaction terms between these three sources. We found that the best prediction performance was obtained when the three sources of information were taken into account in the predictor, and those measures of high-resolution images close to the harvest day provided the best prediction performance.

Kismiantini Montesinos-López, Osval Antonio Crossa, José Setiawan, Ezra Putranda Wutsqa

2021-Feb

count data, generalized poisson regression, genomic data, genomic selection, high-resolution images, plant breeding

Public Health Public Health

Impact of Big Data Analytics on People's Health: Overview of Systematic Reviews and Recommendations for Future Studies.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Although the potential of big data analytics for health care is well recognized, evidence is lacking on its effects on public health.

OBJECTIVE : The aim of this study was to assess the impact of the use of big data analytics on people's health based on the health indicators and core priorities in the World Health Organization (WHO) General Programme of Work 2019/2023 and the European Programme of Work (EPW), approved and adopted by its Member States, in addition to SARS-CoV-2-related studies. Furthermore, we sought to identify the most relevant challenges and opportunities of these tools with respect to people's health.

METHODS : Six databases (MEDLINE, Embase, Cochrane Database of Systematic Reviews via Cochrane Library, Web of Science, Scopus, and Epistemonikos) were searched from the inception date to September 21, 2020. Systematic reviews assessing the effects of big data analytics on health indicators were included. Two authors independently performed screening, selection, data extraction, and quality assessment using the AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews 2) checklist.

RESULTS : The literature search initially yielded 185 records, 35 of which met the inclusion criteria, involving more than 5,000,000 patients. Most of the included studies used patient data collected from electronic health records, hospital information systems, private patient databases, and imaging datasets, and involved the use of big data analytics for noncommunicable diseases. "Probability of dying from any of cardiovascular, cancer, diabetes or chronic renal disease" and "suicide mortality rate" were the most commonly assessed health indicators and core priorities within the WHO General Programme of Work 2019/2023 and the EPW 2020/2025. Big data analytics have shown moderate to high accuracy for the diagnosis and prediction of complications of diabetes mellitus as well as for the diagnosis and classification of mental disorders; prediction of suicide attempts and behaviors; and the diagnosis, treatment, and prediction of important clinical outcomes of several chronic diseases. Confidence in the results was rated as "critically low" for 25 reviews, as "low" for 7 reviews, and as "moderate" for 3 reviews. The most frequently identified challenges were establishment of a well-designed and structured data source, and a secure, transparent, and standardized database for patient data.

CONCLUSIONS : Although the overall quality of included studies was limited, big data analytics has shown moderate to high accuracy for the diagnosis of certain diseases, improvement in managing chronic diseases, and support for prompt and real-time analyses of large sets of varied input data to diagnose and predict disease outcomes.

TRIAL REGISTRATION : International Prospective Register of Systematic Reviews (PROSPERO) CRD42020214048; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=214048.

Borges do Nascimento Israel Júnior, Marcolino Milena Soriano, Abdulazeem Hebatullah Mohamed, Weerasekara Ishanka, Azzopardi-Muscat Natasha, Gonçalves Marcos André, Novillo-Ortiz David

2021-Apr-13

World Health Organization, big data, big data analytics, evidence-based medicine, health status, machine learning, overview, public health, secondary data analysis, systematic review

Internal Medicine Internal Medicine

Embracing nanomaterials' interactions with the innate immune system.

In Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology

Immunotherapy has firmly established itself as a compelling avenue for treating disease. Although many clinically approved immunotherapeutics engage the adaptive immune system, therapeutically targeting the innate immune system remains much less explored. Nanomedicine offers a compelling opportunity for innate immune system engagement, as many nanomaterials inherently interact with myeloid cells (e.g., monocytes, macrophages, neutrophils, and dendritic cells) or can be functionalized to target their cell-surface receptors. Here, we provide a perspective on exploiting nanomaterials for innate immune system regulation. We focus on specific nanomaterial design parameters, including size, form, rigidity, charge, and surface decoration. Furthermore, we examine the potential of high-throughput screening and machine learning, while also providing recommendations for advancing the field. This article is categorized under: Nanotechnology Approaches to Biology > Nanoscale Systems in Biology Diagnostic Tools > In Vivo Nanodiagnostics and Imaging Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease.

Teunissen Abraham J P, Burnett Marianne E, Prévot Geoffrey, Klein Emma D, Bivona Daniel, Mulder Willem J M

2021-Apr-13

innate immunotherapy, nanomaterials, nanomedicine, nanotherapeutics

General General

Predicting Protein and Fat Content in Human Donor Milk Using Machine Learning.

In The Journal of nutrition ; h5-index 61.0

BACKGROUND : Donor milk is the standard of care for hospitalized very low birth weight (VLBW) infants when mother's milk is unavailable; however, growth of donor milk-fed infants is frequently suboptimal. Variability in nutrient composition of donated milk complicates the production of a uniform pooled product and, subsequently, the provision of adequate nutrition to promote optimal growth and development of VLBW infants. We reasoned a machine learning approach to construct batches using characteristics of the milk donation might be an effective strategy in reducing the variability in donor milk product composition.

OBJECTIVE : The objective of this study was to identify whether machine learning models can accurately predict donor milk macronutrient content. We focused on predicting fat and protein, given their well-established importance in VLBW infant growth outcomes.

METHODS : Samples of donor milk, consisting of 272 individual donations and 61 pool samples, were collected from the Rogers Hixon Ontario Human Milk Bank and analyzed for macronutrient content. Four different machine learning models were constructed using independent variable groups associated with donations, donors, and donor-pumping practices. A baseline model was established using lactation stage and infant gestational status. Predictions were made for individual donations and resultant pools.

RESULTS : Machine learning models predicted protein of individual donations and pools with a mean absolute error (MAE) of 0.16 g/dL and 0.10 g/dL, respectively. Individual donation and pooled fat predictions had an MAE of 0.91 g/dL and 0.42 g/dL, respectively. At both the individual donation and pool levels, protein predictions were significantly more accurate than baseline, whereas fat predictions were competitive with baseline.

CONCLUSIONS : Machine learning models can provide accurate predictions of macronutrient content in donor milk. The macronutrient content of pooled milk had a lower prediction error, reinforcing the value of pooling practices. Future research should examine how macronutrient content predictions can be used to facilitate milk bank pooling strategies.

Wong Rachel K, Pitino Michael A, Mahmood Rafid, Zhu Ian Yihang, Stone Debbie, O’Connor Deborah L, Unger Sharon, Chan Timothy C Y

2021-Apr-13

donor human milk, human milk banking, machine learning, macronutrient analysis, macronutrient prediction

Cardiology Cardiology

Development and Validation of Machine Learning-Based Race-Specific Models to Predict 10-Year Risk of Heart Failure: A Multi-Cohort Analysis.

In Circulation ; h5-index 165.0

Background: Heart failure (HF) risk and the underlying risk factors vary by race. Traditional models for HF risk prediction treat race as a covariate in risk prediction and do not account for significant parameters such as cardiac biomarkers. Machine learning (ML) may offer advantages over traditional modeling techniques to develop race-specific HF risk prediction models and elucidate important contributors of HF development across races. Methods: We performed a retrospective analysis of four large, community cohort studies (ARIC, DHS, JHS, and MESA) with adjudicated HF events. Participants were aged >40 years and free of HF at baseline. Race-specific ML models for HF risk prediction were developed in the JHS cohort (for Black race-specific model) and White adults from ARIC (for White rate-specific model). The models included 39 candidate variables across demographic, anthropometric, medical history, laboratory, and electrocardiographic domains. The ML models were externally validated and compared with prior established traditional and non-race specific ML models in race-specific subgroups of the pooled MESA/DHS cohort and Black participants of ARIC. Harrell's C-index and Greenwood-Nam-D'Agostino chi-square tests were used to assess discrimination and calibration, respectively. Results: The ML models had excellent discrimination in the derivation cohorts for Black (N=4,141 in JHS, C-index=0.88) and White (N=7,858 in ARIC, C-index=0.89) participants. In the external validation cohorts, the race-specific ML model demonstrated adequate calibration and superior discrimination (C-indices=0.80-0.83 [for Black individuals] and 0.82 [for White individuals]) compared with established HF risk models or with non-race specific ML models derived using race as a covariate. Among the risk factors, natriuretic peptide levels were the most important predictor of HF risk across both races, followed by troponin levels in Black and EKG-based Cornell voltage in White individuals. Other key predictors of HF risk among Black individuals were glycemic parameters and socioeconomic factors. In contrast, prevalent cardiovascular (CV) disease and traditional CV risk factors were stronger predictors of HF risk in White adults. Conclusions: Race-specific and ML-based HF risk models that integrate clinical, laboratory, and biomarker data demonstrated superior performance when compared with traditional HF risk and non-race specific ML models. This approach identifies distinct race-specific contributors of HF.

Segar Matthew W, Jaeger Byron C, Patel Kershaw V, Nambi Vijay, Ndumele Chiadi E, Correa Adolfo, Butler Javed, Chandra Alvin, Ayers Colby, Rao Shreya, Lewis Alana A, Raffield Laura M, Rodriguez Carlos J, Michos Erin D, Ballantyne Christie M, Hall Michael E, Mentz Robert J, de Lemos James A, Pandey Ambarish

2021-Apr-13

Radiology Radiology

A multi-center study of COVID-19 patient prognosis using deep learning-based CT image analysis and electronic health records.

In European journal of radiology ; h5-index 47.0

PURPOSE : As of August 30th, there were in total 25.1 million confirmed cases and 845 thousand deaths caused by coronavirus disease of 2019 (COVID-19) worldwide. With overwhelming demands on medical resources, patient stratification based on their risks is essential. In this multi-center study, we built prognosis models to predict severity outcomes, combining patients' electronic health records (EHR), which included vital signs and laboratory data, with deep learning- and CT-based severity prediction.

METHOD : We first developed a CT segmentation network using datasets from multiple institutions worldwide. Two biomarkers were extracted from the CT images: total opacity ratio (TOR) and consolidation ratio (CR). After obtaining TOR and CR, further prognosis analysis was conducted on datasets from INSTITUTE-1, INSTITUTE-2 and INSTITUTE-3. For each data cohort, generalized linear model (GLM) was applied for prognosis prediction.

RESULTS : For the deep learning model, the correlation coefficient of the network prediction and manual segmentation was 0.755, 0.919, and 0.824 for the three cohorts, respectively. The AUC (95 % CI) of the final prognosis models was 0.85(0.77,0.92), 0.93(0.87,0.98), and 0.86(0.75,0.94) for INSTITUTE-1, INSTITUTE-2 and INSTITUTE-3 cohorts, respectively. Either TOR or CR exist in all three final prognosis models. Age, white blood cell (WBC), and platelet (PLT) were chosen predictors in two cohorts. Oxygen saturation (SpO2) was a chosen predictor in one cohort.

CONCLUSION : The developed deep learning method can segment lung infection regions. Prognosis results indicated that age, SpO2, CT biomarkers, PLT, and WBC were the most important prognostic predictors of COVID-19 in our prognosis model.

Gong Kuang, Wu Dufan, Arru Chiara Daniela, Homayounieh Fatemeh, Neumark Nir, Guan Jiahui, Buch Varun, Kim Kyungsang, Bizzo Bernardo Canedo, Ren Hui, Tak Won Young, Park Soo Young, Lee Yu Rim, Kang Min Kyu, Park Jung Gil, Carriero Alessandro, Saba Luca, Masjedi Mahsa, Talari Hamidreza, Babaei Rosa, Mobin Hadi Karimi, Ebrahimian Shadi, Guo Ning, Digumarthy Subba R, Dayan Ittai, Kalra Mannudeep K, Li Quanzheng

2021-Feb-05

COVID-19, Computed tomography, Deep learning, Electronic health records, Prognosis

Pathology Pathology

The Curious Case of Hallucinations in Neural Machine Translation

ArXiv Preprint

In this work, we study hallucinations in Neural Machine Translation (NMT), which lie at an extreme end on the spectrum of NMT pathologies. Firstly, we connect the phenomenon of hallucinations under source perturbation to the Long-Tail theory of Feldman (2020), and present an empirically validated hypothesis that explains hallucinations under source perturbation. Secondly, we consider hallucinations under corpus-level noise (without any source perturbation) and demonstrate that two prominent types of natural hallucinations (detached and oscillatory outputs) could be generated and explained through specific corpus-level noise patterns. Finally, we elucidate the phenomenon of hallucination amplification in popular data-generation processes such as Backtranslation and sequence-level Knowledge Distillation.

Vikas Raunak, Arul Menezes, Marcin Junczys-Dowmunt

2021-04-14

Radiology Radiology

Outcomes of Artificial Intelligence Volumetric Assessment of Kidneys and Renal Tumors for Preoperative Assessment of Nephron Sparing Interventions.

In Journal of endourology

Background Renal cell carcinoma is the most common kidney cancer and the 13th most common cause of cancer death worldwide. Partial nephrectomy and percutaneous ablation, increasingly utilized to treat small renal masses and preserve renal parenchyma, require precise preoperative imaging interpretation. We sought to develop and evaluate a convolutional neural network (CNN), a type of deep learning artificial intelligence, to act as a surgical planning aid by determining renal tumor and kidney volumes via segmentation on single-phase computed tomography (CT). Materials and Methods After institutional review board approval, the CT images of 319 patients were retrospectively analyzed. Two distinct CNNs were developed for (1) bounding cube localization of the right and left hemi-abdomen and (2) segmentation of the renal parenchyma and tumor within each bounding cube. Training was performed on a randomly selected cohort of 269 patients. CNN performance was evaluated on a separate cohort of 50 patients using Sorensen-Dice coefficients (which measures the spatial overlap between the manually segmented and neural network derived segmentations) and Pearson correlation coefficients. Experiments were run on a GPU-optimized workstation with a single NVIDIA GeForce GTX Titan X (12GB, Maxwell architecture). Results Median Dice coefficients for kidney and tumor segmentation were 0.970 and 0.816, respectively; Pearson correlation coefficients between CNN-generated and human-annotated estimates for kidney and tumor volume were 0.998 and 0.993 (p < 0.001), respectively. End-to-end trained CNNs were able to perform renal parenchyma and tumor segmentation on a new test case in an average of 5.6 seconds. Conclusions Initial experience with automated deep learning artificial intelligence demonstrates that it is capable of rapidly and accurately segmenting kidneys and renal tumors on single-phase contrast-enhanced CT scans and calculating tumor and renal volumes.

Houshyar Roozbeh, Glavis-Bloom Justin, Bui Thanh-Lan, Chahine Chantal, Bardis Michelle D, Ushinsky Alexander, Liu Hanna, Bhatter Param, Lebby Elliott, Fujimoto Dylann, Grant William, Tran-Harding Karen, Landman Jaime, Chow Daniel S, Chang Peter D

2021-Apr-13

General General

Automatic classification of children with autism spectrum disorder by using a computerized visual-orienting task.

In PsyCh journal

Early screening and diagnosis of autism spectrum disorder (ASD) primarily rely on behavioral observations by qualified clinicians whose decision process can benefit from the combination of machine learning algorithms and sensor data. We designed a computerized visual-orienting task with gaze-related or non-gaze-related directional cues, which triggered participants' gaze-following behavior. Based on their eye-movement data registered by an eye tracker, we applied the machine learning algorithms to classify high-functioning children with ASD (HFA), low-functioning children with ASD (LFA), and typically developing children (TD). We found that TD children had higher success rates in obtaining rewards than HFA children, and HFA children had higher rates than LFA children. Based on raw eye-tracking data, our machine learning algorithm could classify the three groups with an accuracy of 81.1% and relatively high sensitivity and specificity. Classification became worse if only data from the gaze or nongaze conditions were used, suggesting that "less-social" directional cues also carry useful information for distinguishing these groups. Our findings not only provide insights about visual-orienting deficits among children with ASD but also demonstrate the promise of combining classical behavioral paradigms with machine learning algorithms for aiding the screening for individuals with ASD.

He Qiao, Wang Qiandong, Wu Yaxue, Yi Li, Wei Kunlin

2021-Apr-12

autism diagnosis, autism screening, autism spectrum disorder, automatic classification, machine learning, visual orienting

General General

Multidimensional Characterization of Single-Molecule Dynamics in a Plasmonic Nanocavity.

In Angewandte Chemie (International ed. in English)

The nanoscale manipulation and characterization of individual molecules is necessary for understanding the intricacies of molecular structure which ultimately governs phenomena such as reaction mechanisms, catalysis, local effective temperatures, surface interactions, and charge transport phenomenon. Here we demonstrate a platform that utilizes Raman enhancement between two nanostructured electrodes in combination with direct charge transport measurements to allow for simultaneous characterization of the electrical, optical, and mechanical properties of a single molecule. This multi-dimensional information yields repeatable, self-consistent, verification of single-molecule resolution, and allows for detailed analysis of structural and configurational changes of the molecule in situ. These experimental results are supported by a machine-learning based statistical analysis of the spectral information and theoretical calculations to provide insight into the correlation between structural changes in a single-molecule and its charge transport properties.

Domulevicz Lucas, Jeong Hyunhak, Paul Nayan K, Gomez-Diaz Juan Sebastian, Hihath Joshua

2021-Apr-12

Density Functional Calculations, Molecular electronics, Picocavity, Raman spectroscopy, Single-molecule studies

General General

Machine-learning models predicting osteoarthritis associated with the lead blood level.

In Environmental science and pollution research international

Lead is one of the most hazardous environmental pollutants in industrialized countries; lead exposure is a risk factor for osteoarthritis (OA) in older women. Here, the performance of several machine-learning (ML) algorithms in terms of predicting the prevalence of OA associated with lead exposure was compared. A total of 2224 women aged 50 years and older who participated in the Korea National Health and Nutrition Examination Surveys from 2005 to 2017 were divided into a training dataset (70%) for generation of ML models, and a test dataset (30%). We built and tested five ML algorithms, including logistic regression (LR), a k-nearest neighbor model, a decision tree, a random forest, and a support vector machine. All afforded acceptable predictive accuracy; the LR model was the most accurate and yielded the greatest area under the receiver operating characteristic curve. We found that various ML models can be used to predict the risk of OA associated with lead exposure effectively, using data from population-based survey.

Kim Kisok, Park Hyejin

2021-Apr-12

KNHNES, Lead exposure, Machine-learning, Osteoarthritis, Post-menopausal women, Predictive model

Ophthalmology Ophthalmology

Automatic detection of leakage point in central serous chorioretinopathy of fundus fluorescein angiography based on time sequence deep learning.

In Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie

PURPOSE : To detect the leakage points of central serous chorioretinopathy (CSC) automatically from dynamic images of fundus fluorescein angiography (FFA) using a deep learning algorithm (DLA).

METHODS : The study included 2104 FFA images from 291 FFA sequences of 291 eyes (137 right eyes and 154 left eyes) from 262 patients. The leakage points were segmented with an attention gated network (AGN). The optic disk (OD) and macula region were segmented simultaneously using a U-net. To reduce the number of false positives based on time sequence, the leakage points were matched according to their positions in relation to the OD and macula.

RESULTS : With the AGN alone, the number of cases whose detection results perfectly matched the ground truth was only 37 out of 61 cases (60.7%) in the test set. The dice on the lesion level were 0.811. Using an elimination procedure to remove false positives, the number of accurate detection cases increased to 57 (93.4%). The dice on the lesion level also improved to 0.949.

CONCLUSIONS : Using DLA, the CSC leakage points in FFA can be identified reproducibly and accurately with a good match to the ground truth. This novel finding may pave the way for potential application of artificial intelligence to guide laser therapy.

Chen Menglu, Jin Kai, You Kun, Xu Yufeng, Wang Yao, Yip Chee-Chew, Wu Jian, Ye Juan

2021-Apr-12

Central serous chorioretinopathy, Deep learning, Fundus fluorescein angiography, Time sequence

Radiology Radiology

Quantitative analysis based on chest CT classifies common and severe patients with coronavirus disease 2019 pneumonia in Wuhan, China.

In Chinese journal of academic radiology

Objective : This study aimed to compare quantifiable radiologic findings and their dynamic change throughout the clinical course of common and severe coronavirus disease 2019 (COVID-19), and to provide valuable evidence for radiologic classification of the two types of this disease.

Methods : 112 patients with laboratory-confirmed COVID-19 were retrospectively analyzed. Volumetric percentage of infection and density of the lung were measured by a computer-aided software. Clinical parameters were recorded to reflect disease progression. Baseline data and dynamic change were compared between two groups and a decision-tree algorithm was developed to determine the cut-off value for classification.

Results : 93 patients were finally included and were divided into common group (n = 76) and severe group (n = 17) based on current criteria. Compared with common patients, severe patients experienced shorter advanced stage, peak time and plateau, but longer absorption stage. The dynamic change of volume and density coincided with the clinical course. The interquartile range of volumetric percentage of the two groups were 1.0-7.2% and 11.4-31.2%, respectively. Baseline volumetric percentage of infection was significantly higher in severe group, and the cut-off value of it was 10.10%.

Conclusions : Volumetric percentage between severe and common patients was significantly different. Because serial CT scans are systemically performed in patients with COVID-19 pneumonia, this quantitative analysis can simultaneously provide valuable information for physicians to evaluate their clinical course and classify common and severe patients accurately.

Yang Chongtu, Cao Guijuan, Liu Fen, Liu Jiacheng, Huang Songjiang, Xiong Bin

2021-Apr-08

Artificial intelligence, Computer-assisted, Coronavirus disease 2019, Decision trees, Multidetector computed tomography, Numerical analysis

General General

Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images.

In Biomedical signal processing and control

The coronavirus (COVID-19) is currently the most common contagious disease which is prevalent all over the world. The main challenge of this disease is the primary diagnosis to prevent secondary infections and its spread from one person to another. Therefore, it is essential to use an automatic diagnosis system along with clinical procedures for the rapid diagnosis of COVID-19 to prevent its spread. Artificial intelligence techniques using computed tomography (CT) images of the lungs and chest radiography have the potential to obtain high diagnostic performance for Covid-19 diagnosis. In this study, a fusion of convolutional neural network (CNN), support vector machine (SVM), and Sobel filter is proposed to detect COVID-19 using X-ray images. A new X-ray image dataset was collected and subjected to high pass filter using a Sobel filter to obtain the edges of the images. Then these images are fed to CNN deep learning model followed by SVM classifier with ten-fold cross validation strategy. This method is designed so that it can learn with not many data. Our results show that the proposed CNN-SVM with Sobel filter (CNN-SVM + Sobel) achieved the highest classification accuracy, sensitivity and specificity of 99.02%, 100% and 95.23%, respectively in automated detection of COVID-19. It showed that using Sobel filter can improve the performance of CNN. Unlike most of the other researches, this method does not use a pre-trained network. We have also validated our developed model using six public databases and obtained the highest performance. Hence, our developed model is ready for clinical application.

Sharifrazi Danial, Alizadehsani Roohallah, Roshanzamir Mohamad, Joloudari Javad Hassannataj, Shoeibi Afshin, Jafari Mahboobeh, Hussain Sadiq, Sani Zahra Alizadeh, Hasanzadeh Fereshteh, Khozeimeh Fahime, Khosravi Abbas, Nahavandi Saeid, Panahiazar Maryam, Zare Assef, Islam Sheikh Mohammed Shariful, Acharya U Rajendra

2021-Apr-08

CNN., Covid-19, Data Mining, Deep Learning, Feature Extraction, Image Processing, Machine Learning, SVM, Sobel operator

General General

A novel framework of collaborative early warning for COVID-19 based on blockchain and smart contracts.

In Information sciences

Early warning is a vital component of emergency repsonse systems for infectious diseases. However, most early warning systems are centralized and isolated, thus there are potential risks of single evidence bias and decision-making errors. In this paper, we tackle this issue via proposing a novel framework of collaborative early warning for COVID-19 based on blockchain and smart contracts, aiming to crowdsource early warning tasks to distributed channels including medical institutions, social organinzations, and even individuals. Our framework supports two surveillance modes, namely, medical federation surveillance based on federated learning and social collaboration surveillance based on the learning markets approach, and fuses their monitoring results on emerging cases to alert. By using our framework, medical institutions are expected to obtain better federated surveillance models with privacy protection, and social participants without mutual trusts can also share verified surveillance resources such as data and models, and fuse their surveillance solutions. We implemented our proposed framework based on the Ethereum and IPFS platforms. Experimental results show that our framework has advantages of decentralized decision-making, fairness, auditability, and universality, and also has potential guidance and reference value for the early warning and prevention of unknown infectious diseases.

Ouyang Liwei, Yuan Yong, Cao Yumeng, Wang Fei-Yue

2021-Apr-08

blockchain, collaborative early warning, federated learning, learning markets, smart contracts

Public Health Public Health

Vascular smooth muscle-derived Trpv1+ progenitors are a source of cold-induced thermogenic adipocytes.

In Nature metabolism

Brown adipose tissue (BAT) and beige fat function in energy expenditure in part due to their role in thermoregulation, making these tissues attractive targets for treating obesity and metabolic disorders. While prolonged cold exposure promotes de novo recruitment of brown adipocytes, the exact sources of cold-induced thermogenic adipocytes are not completely understood. Here, we identify transient receptor potential cation channel subfamily V member 1 (Trpv1)+ vascular smooth muscle (VSM) cells as previously unidentified thermogenic adipocyte progenitors. Single-cell RNA sequencing analysis of interscapular brown adipose depots reveals, in addition to the previously known platelet-derived growth factor receptor (Pdgfr)α-expressing mesenchymal progenitors, a population of VSM-derived adipocyte progenitor cells (VSM-APC) expressing the temperature-sensitive cation channel Trpv1. We demonstrate that cold exposure induces the proliferation of Trpv1+ VSM-APCs and enahnces their differentiation to highly thermogenic adipocytes. Together, these findings illustrate the landscape of the thermogenic adipose niche at single-cell resolution and identify a new cellular origin for the development of brown and beige adipocytes.

Shamsi Farnaz, Piper Mary, Ho Li-Lun, Huang Tian Lian, Gupta Anushka, Streets Aaron, Lynes Matthew D, Tseng Yu-Hua

2021-Apr-12

General General

COVID-19 information retrieval with deep-learning based semantic search, question answering, and abstractive summarization.

In NPJ digital medicine

The COVID-19 global pandemic has resulted in international efforts to understand, track, and mitigate the disease, yielding a significant corpus of COVID-19 and SARS-CoV-2-related publications across scientific disciplines. Throughout 2020, over 400,000 coronavirus-related publications have been collected through the COVID-19 Open Research Dataset. Here, we present CO-Search, a semantic, multi-stage, search engine designed to handle complex queries over the COVID-19 literature, potentially aiding overburdened health workers in finding scientific answers and avoiding misinformation during a time of crisis. CO-Search is built from two sequential parts: a hybrid semantic-keyword retriever, which takes an input query and returns a sorted list of the 1000 most relevant documents, and a re-ranker, which further orders them by relevance. The retriever is composed of a deep learning model (Siamese-BERT) that encodes query-level meaning, along with two keyword-based models (BM25, TF-IDF) that emphasize the most important words of a query. The re-ranker assigns a relevance score to each document, computed from the outputs of (1) a question-answering module which gauges how much each document answers the query, and (2) an abstractive summarization module which determines how well a query matches a generated summary of the document. To account for the relatively limited dataset, we develop a text augmentation technique which splits the documents into pairs of paragraphs and the citations contained in them, creating millions of (citation title, paragraph) tuples for training the retriever. We evaluate our system ( http://einstein.ai/covid ) on the data of the TREC-COVID information retrieval challenge, obtaining strong performance across multiple key information retrieval metrics.

Esteva Andre, Kale Anuprit, Paulus Romain, Hashimoto Kazuma, Yin Wenpeng, Radev Dragomir, Socher Richard

2021-Apr-12

General General

Fiji plugins for qualitative image annotations: routine analysis and application to image classification.

In F1000Research

Quantitative measurements and qualitative description of scientific images are both important to describe the complexity of digital image data. While various software solutions for quantitative measurements in images exist, there is a lack of simple tools for the qualitative description of images in common user-oriented image analysis software. To address this issue, we developed a set of Fiji plugins that facilitate the systematic manual annotation of images or image-regions. From a list of user-defined keywords, these plugins generate an easy-to-use graphical interface with buttons or checkboxes for the assignment of single or multiple pre-defined categories to full images or individual regions of interest. In addition to qualitative annotations, any quantitative measurement from the standard Fiji options can also be automatically reported. Besides the interactive user interface, keyboard shortcuts are available to speed-up the annotation process for larger datasets. The annotations are reported in a Fiji result table that can be exported as a pre-formatted csv file, for further analysis with common spreadsheet software or custom automated pipelines. To illustrate possible use case of the annotations, and facilitate the analysis of the generated annotations, we provide examples of such pipelines, including data-visualization solutions in Fiji and KNIME, as well as a complete workflow for training and application of a deep learning model for image classification in KNIME. Ultimately, the plugins enable standardized routine sample evaluation, classification, or ground-truth category annotation of any digital image data compatible with Fiji.

Thomas Laurent S V, Schaefer Franz, Gehrig Jochen

2020

Fiji, ImageJ, KNIME, bioimage analysis, ground-truth labelling, image annotation, image classification, qualitative analysis

Surgery Surgery

Abdominal Organ Transplantation: Noteworthy Literature in 2020.

In Seminars in cardiothoracic and vascular anesthesia ; h5-index 16.0

In 2020, we identified and screened over 490 peer-reviewed publications on pancreatic transplantation, over 500 on intestinal transplantation, and over 5000 on kidney transplantation. The liver transplantation section specially focused on clinical trials and systematic reviews published in 2020 and featured selected articles. This review highlights noteworthy literature pertinent to anesthesiologists and critical care physicians caring for patients undergoing abdominal organ transplantation. We explore a wide range of topics, including COVID-19 and organ transplantation, risk factors and outcomes, pain management, artificial intelligence, robotic donor surgery, and machine perfusion.

Wang Ryan F, Fagelman Erica J, Smith Natalie K, Sakai Tetsuro

2021-Apr-13

COVID-19, anesthesiology, intestine, kidney, liver, pancreas, transplantation

General General

Towards a framework for evaluating the safety, acceptability and efficacy of AI systems for health: an initial synthesis

ArXiv Preprint

The potential presented by Artificial Intelligence (AI) for healthcare has long been recognised by the technical community. More recently, this potential has been recognised by policymakers, resulting in considerable public and private investment in the development of AI for healthcare across the globe. Despite this, excepting limited success stories, real-world implementation of AI systems into front-line healthcare has been limited. There are numerous reasons for this, but a main contributory factor is the lack of internationally accepted, or formalised, regulatory standards to assess AI safety and impact and effectiveness. This is a well-recognised problem with numerous ongoing research and policy projects to overcome it. Our intention here is to contribute to this problem-solving effort by seeking to set out a minimally viable framework for evaluating the safety, acceptability and efficacy of AI systems for healthcare. We do this by conducting a systematic search across Scopus, PubMed and Google Scholar to identify all the relevant literature published between January 1970 and November 2020 related to the evaluation of: output performance; efficacy; and real-world use of AI systems, and synthesising the key themes according to the stages of evaluation: pre-clinical (theoretical phase); exploratory phase; definitive phase; and post-market surveillance phase (monitoring). The result is a framework to guide AI system developers, policymakers, and regulators through a sufficient evaluation of an AI system designed for use in healthcare.

Jessica Morley, Caroline Morton, Kassandra Karpathakis, Mariarosaria Taddeo, Luciano Floridi

2021-04-14

General General

Inflated prediction accuracy of neuropsychiatric biomarkers caused by data leakage in feature selection.

In Scientific reports ; h5-index 158.0

In recent years, machine learning techniques have been frequently applied to uncovering neuropsychiatric biomarkers with the aim of accurately diagnosing neuropsychiatric diseases and predicting treatment prognosis. However, many studies did not perform cross validation (CV) when using machine learning techniques, or others performed CV in an incorrect manner, leading to significantly biased results due to overfitting problem. The aim of this study is to investigate the impact of CV on the prediction performance of neuropsychiatric biomarkers, in particular, for feature selection performed with high-dimensional features. To this end, we evaluated prediction performances using both simulation data and actual electroencephalography (EEG) data. The overall prediction accuracies of the feature selection method performed outside of CV were considerably higher than those of the feature selection method performed within CV for both the simulation and actual EEG data. The differences between the prediction accuracies of the two feature selection approaches can be thought of as the amount of overfitting due to selection bias. Our results indicate the importance of correctly using CV to avoid biased results of prediction performance of neuropsychiatric biomarkers.

Shim Miseon, Lee Seung-Hwan, Hwang Han-Jeong

2021-Apr-12

General General

Prediction of ambulatory outcome in patients with corona radiata infarction using deep learning.

In Scientific reports ; h5-index 158.0

Deep learning (DL) is an advanced machine learning approach used in diverse areas such as bioinformatics, image analysis, and natural language processing. Here, using brain magnetic resonance imaging (MRI) data obtained at early stages of infarcts, we attempted to develop a convolutional neural network (CNN) to predict the ambulatory outcome of corona radiata infarction at six months after onset. We retrospectively recruited 221 patients with corona radiata infarcts. A favorable outcome of ambulatory function was defined as a functional ambulation category (FAC) score of ≥ 4 (able to walk without a guardian's assistance), and a poor outcome of ambulatory function was defined as an FAC score of < 4. We used a CNN algorithm. Of the included subjects, 69.7% (n = 154) were assigned randomly to the training set and the remaining 30.3% (n = 67) were assigned to the validation set to measure the model performance. The area under the curve was 0.751 (95% CI 0.649-0.852) for the prediction of ambulatory function with the validation dataset using the CNN model. We demonstrated that a CNN model trained using brain MRIs captured at an early stage after corona radiata infarction could be helpful in predicting long-term ambulatory outcomes.

Kim Jeoung Kun, Choo Yoo Jin, Shin Hyunkwang, Choi Gyu Sang, Chang Min Cheol

2021-Apr-12

Internal Medicine Internal Medicine

Tens of images can suffice to train neural networks for malignant leukocyte detection.

In Scientific reports ; h5-index 158.0

Convolutional neural networks (CNNs) excel as powerful tools for biomedical image classification. It is commonly assumed that training CNNs requires large amounts of annotated data. This is a bottleneck in many medical applications where annotation relies on expert knowledge. Here, we analyze the binary classification performance of a CNN on two independent cytomorphology datasets as a function of training set size. Specifically, we train a sequential model to discriminate non-malignant leukocytes from blast cells, whose appearance in the peripheral blood is a hallmark of leukemia. We systematically vary training set size, finding that tens of training images suffice for a binary classification with an ROC-AUC over 90%. Saliency maps and layer-wise relevance propagation visualizations suggest that the network learns to increasingly focus on nuclear structures of leukocytes as the number of training images is increased. A low dimensional tSNE representation reveals that while the two classes are separated already for a few training images, the distinction between the classes becomes clearer when more training images are used. To evaluate the performance in a multi-class problem, we annotated single-cell images from a acute lymphoblastic leukemia dataset into six different hematopoietic classes. Multi-class prediction suggests that also here few single-cell images suffice if differences between morphological classes are large enough. The incorporation of deep learning algorithms into clinical practice has the potential to reduce variability and cost, democratize usage of expertise, and allow for early detection of disease onset and relapse. Our approach evaluates the performance of a deep learning based cytology classifier with respect to size and complexity of the training data and the classification task.

Schouten Jens P E, Matek Christian, Jacobs Luuk F P, Buck Michèle C, Bošnački Dragan, Marr Carsten

2021-Apr-12

Radiology Radiology

Impact of image compression on deep learning-based mammogram classification.

In Scientific reports ; h5-index 158.0

Image compression is used in several clinical organizations to help address the overhead associated with medical imaging. These methods reduce file size by using a compact representation of the original image. This study aimed to analyze the impact of image compression on the performance of deep learning-based models in classifying mammograms as "malignant"-cases that lead to a cancer diagnosis and treatment-or "normal" and "benign," non-malignant cases that do not require immediate medical intervention. In this retrospective study, 9111 unique mammograms-5672 normal, 1686 benign, and 1754 malignant cases were collected from the National Cancer Center in the Republic of Korea. Image compression was applied to mammograms with compression ratios (CRs) ranging from 15 to 11 K. Convolutional neural networks (CNNs) with three convolutional layers and three fully-connected layers were trained using these images to classify a mammogram as malignant or not malignant across a range of CRs using five-fold cross-validation. Models trained on images with maximum CRs of 5 K had an average area under the receiver operating characteristic curve (AUROC) of 0.87 and area under the precision-recall curve (AUPRC) of 0.75 across the five folds and compression ratios. For images compressed with CRs of 10 K and 11 K, model performance decreased (average 0.79 in AUROC and 0.49 in AUPRC). Upon generating saliency maps that visualize the areas each model views as significant for prediction, models trained on less compressed (CR <  = 5 K) images had maps encapsulating a radiologist's label, while models trained on images with higher amounts of compression had maps that missed the ground truth completely. In addition, base ResNet18 models pre-trained on ImageNet and trained using compressed mammograms did not show performance improvements over our CNN model, with AUROC and AUPRC values ranging from 0.77 to 0.87 and 0.52 to 0.71 respectively when trained and tested on images with maximum CRs of 5 K. This paper finds that while training models on images with increased the robustness of the models when tested on compressed data, moderate image compression did not substantially impact the classification performance of DL-based models.

Jo Yong-Yeon, Choi Young Sang, Park Hyun Woo, Lee Jae Hyeok, Jung Hyojung, Kim Hyo-Eun, Ko Kyounglan, Lee Chan Wha, Cha Hyo Soung, Hwangbo Yul

2021-Apr-12

General General

Predicting women with depressive symptoms postpartum with machine learning methods.

In Scientific reports ; h5-index 158.0

Postpartum depression (PPD) is a detrimental health condition that affects 12% of new mothers. Despite negative effects on mothers' and children's health, many women do not receive adequate care. Preventive interventions are cost-efficient among high-risk women, but our ability to identify these is poor. We leveraged the power of clinical, demographic, and psychometric data to assess if machine learning methods can make accurate predictions of postpartum depression. Data were obtained from a population-based prospective cohort study in Uppsala, Sweden, collected between 2009 and 2018 (BASIC study, n = 4313). Sub-analyses among women without previous depression were performed. The extremely randomized trees method provided robust performance with highest accuracy and well-balanced sensitivity and specificity (accuracy 73%, sensitivity 72%, specificity 75%, positive predictive value 33%, negative predictive value 94%, area under the curve 81%). Among women without earlier mental health issues, the accuracy was 64%. The variables setting women at most risk for PPD were depression and anxiety during pregnancy, as well as variables related to resilience and personality. Future clinical models that could be implemented directly after delivery might consider including these variables in order to identify women at high risk for postpartum depression to facilitate individualized follow-up and cost-effectiveness.

Andersson Sam, Bathula Deepti R, Iliadis Stavros I, Walter Martin, Skalkidou Alkistis

2021-Apr-12

General General

UWB-gestures, a public dataset of dynamic hand gestures acquired using impulse radar sensors.

In Scientific data

In the past few decades, deep learning algorithms have become more prevalent for signal detection and classification. To design machine learning algorithms, however, an adequate dataset is required. Motivated by the existence of several open-source camera-based hand gesture datasets, this descriptor presents UWB-Gestures, the first public dataset of twelve dynamic hand gestures acquired with ultra-wideband (UWB) impulse radars. The dataset contains a total of 9,600 samples gathered from eight different human volunteers. UWB-Gestures eliminates the need to employ UWB radar hardware to train and test the algorithm. Additionally, the dataset can provide a competitive environment for the research community to compare the accuracy of different hand gesture recognition (HGR) algorithms, enabling the provision of reproducible research results in the field of HGR through UWB radars. Three radars were placed at three different locations to acquire the data, and the respective data were saved independently for flexibility.

Ahmed Shahzad, Wang Dingyang, Park Junyoung, Cho Sung Ho

2021-Apr-12

General General

All urban areas' energy use data across 640 districts in India for the year 2011.

In Scientific data

India is the third-largest contributor to global energy-use and anthropogenic carbon emissions. India's urban energy transitions are critical to meet its climate goals due to the country's rapid urbanization. However, no baseline urban energy-use dataset covers all Indian urban districts in ways that align with national totals and integrate social-economic-infrastructural attributes to inform such transitions. This paper develops a novel bottom-up plus top-down approach, comprehensively integrating multiple field surveys and utilizing machine learning, to model All Urban areas' Energy-use (AllUrE) across all 640 districts in India, merged with social-economic-infrastructural data. Energy use estimates in this AllUrE-India dataset are evaluated by comparing with reported energy-use at three scales: nation-wide, state-wide, and city-level. Spatially granular AllUrE data aggregated nationally show good agreement with national totals (<2% difference). The goodness-of-fit ranged from 0.78-0.95 for comparison with state-level totals, and 0.90-0.99 with city-level data for different sectors. The relatively strong alignment at all three spatial scales demonstrates the value of AllUrE-India data for modelling urban energy transitions consistent with national energy and climate goals.

Tong Kangkang, Nagpure Ajay Singh, Ramaswami Anu

2021-Apr-12

General General

Ultrafast light field tomography for snapshot transient and non-line-of-sight imaging.

In Nature communications ; h5-index 260.0

Cameras with extreme speeds are enabling technologies in both fundamental and applied sciences. However, existing ultrafast cameras are incapable of coping with extended three-dimensional scenes and fall short for non-line-of-sight imaging, which requires a long sequence of time-resolved two-dimensional data. Current non-line-of-sight imagers, therefore, need to perform extensive scanning in the spatial and/or temporal dimension, restricting their use in imaging only static or slowly moving objects. To address these long-standing challenges, we present here ultrafast light field tomography (LIFT), a transient imaging strategy that offers a temporal sequence of over 1000 and enables highly efficient light field acquisition, allowing snapshot acquisition of the complete four-dimensional space and time. With LIFT, we demonstrated three-dimensional imaging of light in flight phenomena with a <10 picoseconds resolution and non-line-of-sight imaging at a 30 Hz video-rate. Furthermore, we showed how LIFT can benefit from deep learning for an improved and accelerated image formation. LIFT may facilitate broad adoption of time-resolved methods in various disciplines.

Feng Xiaohua, Gao Liang

2021-04-12

Radiology Radiology

A multi-center study of COVID-19 patient prognosis using deep learning-based CT image analysis and electronic health records.

In European journal of radiology ; h5-index 47.0

PURPOSE : As of August 30th, there were in total 25.1 million confirmed cases and 845 thousand deaths caused by coronavirus disease of 2019 (COVID-19) worldwide. With overwhelming demands on medical resources, patient stratification based on their risks is essential. In this multi-center study, we built prognosis models to predict severity outcomes, combining patients' electronic health records (EHR), which included vital signs and laboratory data, with deep learning- and CT-based severity prediction.

METHOD : We first developed a CT segmentation network using datasets from multiple institutions worldwide. Two biomarkers were extracted from the CT images: total opacity ratio (TOR) and consolidation ratio (CR). After obtaining TOR and CR, further prognosis analysis was conducted on datasets from INSTITUTE-1, INSTITUTE-2 and INSTITUTE-3. For each data cohort, generalized linear model (GLM) was applied for prognosis prediction.

RESULTS : For the deep learning model, the correlation coefficient of the network prediction and manual segmentation was 0.755, 0.919, and 0.824 for the three cohorts, respectively. The AUC (95 % CI) of the final prognosis models was 0.85(0.77,0.92), 0.93(0.87,0.98), and 0.86(0.75,0.94) for INSTITUTE-1, INSTITUTE-2 and INSTITUTE-3 cohorts, respectively. Either TOR or CR exist in all three final prognosis models. Age, white blood cell (WBC), and platelet (PLT) were chosen predictors in two cohorts. Oxygen saturation (SpO2) was a chosen predictor in one cohort.

CONCLUSION : The developed deep learning method can segment lung infection regions. Prognosis results indicated that age, SpO2, CT biomarkers, PLT, and WBC were the most important prognostic predictors of COVID-19 in our prognosis model.

Gong Kuang, Wu Dufan, Arru Chiara Daniela, Homayounieh Fatemeh, Neumark Nir, Guan Jiahui, Buch Varun, Kim Kyungsang, Bizzo Bernardo Canedo, Ren Hui, Tak Won Young, Park Soo Young, Lee Yu Rim, Kang Min Kyu, Park Jung Gil, Carriero Alessandro, Saba Luca, Masjedi Mahsa, Talari Hamidreza, Babaei Rosa, Mobin Hadi Karimi, Ebrahimian Shadi, Guo Ning, Digumarthy Subba R, Dayan Ittai, Kalra Mannudeep K, Li Quanzheng

2021-Feb-05

COVID-19, Computed tomography, Deep learning, Electronic health records, Prognosis

General General

A novel based-performance degradation indicator RUL prediction model and its application in rolling bearing.

In ISA transactions

Aiming at the problem of poor prediction performance of rolling bearing remaining useful life (RUL) with single performance degradation indicator, a novel based-performance degradation indicator RUL prediction model is established. Firstly, the vibration signal of rolling bearing is decomposed into some intrinsic scale components (ISCs) by piecewise cubic Hermite interpolating polynomial-local characteristic-scale decomposition (PCHIP-LCD), and the effective ISCs are selected to reconstruct signals based on kurtosis-correlation coefficient (K-C) criteria. Secondly, the multi-dimensional degradation feature set of reconstructed signals is extracted, and then the sensitive degradation indicator IICAMD is calculated by fusing the improved independent component analysis (IICA) and Mahalanobis Distance (MD). Thirdly, the false fluctuation of the IICAMD is repaired by using the gray regression model (GM) to obtain the health indicator (HI) of the rolling bearing, and the start prediction time (SPT) of the rolling bearing is determined according to the time mutation point of HI. Finally, generalized regression neural network (GRNN) model based on HI is constructed to predict the RUL of rolling bearing. The experimental results of two groups of different rolling bearing data-sets show that the proposed method achieves better performance in prediction accuracy and reliability.

Yang Chuangyan, Ma Jun, Wang Xiaodong, Li Xiang, Li Zhuorui, Luo Ting

2021-Mar-31

Feature fusion, GRNN, Health indicators, RUL prediction, Rolling bearing

General General

Probiotics in food allergy.

In Current opinion in allergy and clinical immunology

PURPOSE OF REVIEW : To perform a nonsystematic review of the literature on the possible role of probiotics for food allergy (FA).

RECENT FINDINGS : Animal model and in vitro evidence suggest that the gut microbiome could protect against FA and that probiotics could be a valid instrument. There is no consistent evidence in identifying the specific species, the dosage, and the optimal duration to obtain the correct immunomodulation. Early life supplementation with specific 'missing' immunomodulatory microbes - derived from machine learning approach to birth cohort studies - might represent a novel approach to the primary prevention of multiple human atopic diseases. However, further studies are needed.

SUMMARY : Currently, there is no positive recommendation from the main scientific societies to use probiotics neither for the treatment nor for the prevention of FA.

Mennini Maurizio, Arasi Stefania, Artesani Maria Cristina, Fiocchi Alessandro Giovanni

2021-Mar-31

General General

How does "A Bit of Everything American" state feel about COVID-19? A quantitative Twitter analysis of the pandemic in Ohio.

In Journal of computational social science

COVID-19 has proven itself to be one of the most important events of the last two centuries. This defining moment in our lives has created wide-ranging discussions in many segments of our societies, both politically and socially. Over time, the pandemic has been associated with many social and political topics, as well as sentiments and emotions. Twitter offers a platform to understand these effects. The primary objective of this study is to capture the awareness and sentiment about COVID-19-related issues and to find how they relate to the number of cases and deaths in a representative region of the United States. The study uses a unique dataset consisting of over 46 million tweets from over 91,000 users in 88 counties of the state of Ohio, a state-of-the-art deep learning model to measure and detect awareness and emotions. The data collected is analyzed using OLS regression and System-GMM dynamic panel. Findings indicate that the pandemic has drastically changed the perception of the Republican party in the society. Individual motivations are strongly influenced by ideological choices and this ultimately affects individual pandemic-related outcomes. The paper contributes to the literature by expanding the knowledge on COVID-19 (i), offering a representative result for the United States by focusing on an "average" state like Ohio (ii), and incorporating the sentiment and emotions into the calculation of awareness (iii).

Caliskan Cantay

2021-Apr-05

Awareness, COVID-19, Emotion classification, Twitter

Public Health Public Health

Leveraging machine learning to characterize the role of socio-economic determinants on physical health and well-being among veterans.

In Computers in biology and medicine

INTRODUCTION : We investigate the contribution of demographic, socio-economic, and geographic characteristics as determinants of physical health and well-being to guide public health policies and preventative behavior interventions (e.g., countering coronavirus).

METHODS : We use machine learning to build predictive models of overall well-being and physical health among veterans as a function of these three sets of characteristics. We link Gallup's U.S. Daily Poll between 2014 and 2017 over a range of demographic and socio-economic characteristics with zipcode characteristics from the Census Bureau to build predictive models of overall and physical well-being.

RESULTS : Although the predictive models of overall well-being have weak performance, our classification of low levels of physical well-being performed better. Gradient boosting delivered the best results (80.2% precision, 82.4% recall, and 80.4% AUROC) with perceptions of purpose in the workplace and financial anxiety as the most predictive features. Our results suggest that additional measures of socio-economic characteristics are required to better predict physical well-being, particularly among vulnerable groups, like veterans.

CONCLUSION : Socio-economic characteristics explain large differences in physical and overall well-being. Effective predictive models that incorporate socio-economic data will provide opportunities to create real-time and personalized feedback to help individuals improve their quality of life.

Makridis Christos A, Zhao David Y, Bejan Cosmin A, Alterovitz Gil

2021-Apr-03

Health informatics, Machine learning, Socioeconomics, Subjective well-being, Veterans

General General

High-throughput label-free detection of DNA-to-RNA transcription inhibition using brightfield microscopy and deep neural networks.

In Computers in biology and medicine

Drug discovery is in constant evolution and major advances have led to the development of in vitro high-throughput technologies, facilitating the rapid assessment of cellular phenotypes. One such phenotype is immunogenic cell death, which occurs partly as a consequence of inhibited RNA synthesis. Automated cell-imaging offers the possibility of combining high-throughput with high-content data acquisition through the simultaneous computation of a multitude of cellular features. Usually, such features are extracted from fluorescence images, hence requiring labeling of the cells using dyes with possible cytotoxic and phototoxic side effects. Recently, deep learning approaches have allowed the analysis of images obtained by brightfield microscopy, a technique that was for long underexploited, with the great advantage of avoiding any major interference with cellular physiology or stimulatory compounds. Here, we describe a label-free image-based high-throughput workflow that accurately detects the inhibition of DNA-to-RNA transcription. This is achieved by combining two successive deep convolutional neural networks, allowing (1) to automatically detect cellular nuclei (thus enabling monitoring of cell death) and (2) to classify the extracted nuclear images in a binary fashion. This analytical pipeline is R-based and can be easily applied to any microscopic platform.

Sauvat Allan, Cerrato Giulia, Humeau Juliette, Leduc Marion, Kepp Oliver, Kroemer Guido

2021-Apr-04

Cellular imaging, Convolutional neural network, Immunogenic cell death, Semantic segmentation, Systems biology, Transmitted light microscopy, UNET

General General

Opportunity for efficiency in clinical development: An overview of adaptive clinical trial designs and innovative machine learning tools, with examples from the cardiovascular field.

In Contemporary clinical trials

Modern data analysis tools and statistical modeling techniques are increasingly used in clinical research to improve diagnosis, estimate disease progression and predict treatment outcomes. What seems less emphasized is the importance of the study design, which can have a serious impact on the study cost, time and the statistical efficiency. This paper provides an overview on different types of adaptive designs in clinical trials and their applications to cardiovascular trials. We highlight recent proliferation of work on adaptive designs over the past two decades, including some recent regulatory guidelines on complex trial designs and master protocols. We also describe the increasing role of machine learning and use of metaheuristics to construct increasingly complex adaptive designs or to identify interesting features for improved predictions and classifications.

Sverdlov Oleksandr, Ryeznik Yevgen, Wong Weng Kee

2021-Apr-09

Adaptive designs, Cardiovascular trials, Machine learning, Master protocols, Nature-inspired metaheuristics, Rare diseases, Seamless designs

Ophthalmology Ophthalmology

Classification criteria for multiple evanescent white dot syndrome.

In American journal of ophthalmology ; h5-index 67.0

PURPOSE : To determine classification criteria for multiple evanescent white dot syndrome (MEWDS).

DESIGN : Machine learning of cases with MEWDS and 8 other posterior uveitides.

METHODS : Cases of posterior uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the infectious posterior/panuveitides. The resulting criteria were evaluated on the validation set.

RESULTS : One thousand sixty-eight cases of posterior uveitides, including 51 cases of MEWDS, were evaluated by machine learning. Key criteria for MEWDS included: 1) multifocal gray white chorioretinal spots with foveal granularity; 2) characteristic imaging on fluorescein angiography ("wreath-like" hyperfluorescent lesions) and/or optical coherence tomography (hyper-reflective lesions extending from retinal pigment epithelium through ellipsoid zone into the retinal outer nuclear layer); and 3) absent to mild anterior chamber and vitreous inflammation. Overall accuracy for posterior uveitides was 93.9% in the training set and 98.0% (95% confidence interval 94.3, 99.3) in the validation set. The misclassification rates for MEWDS were 7% in the training set and 0% in the validation set.

CONCLUSIONS : The criteria for MEWDS had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

Jabs Douglas A, Brezin Antoine P, Dick Andrew D, Levinson Ralph D, Lim Lyndell L, McCluskey Peter, Oden Neal, Palestine Alan G, Thorne Jennifer E, Trusko Brett E, Vitale Albert, Wittenberg Susan E

2021-Apr-09

Ophthalmology Ophthalmology

Classification criteria for acute posterior multifocal placoid pigment epitheliopathy.

In American journal of ophthalmology ; h5-index 67.0

PURPOSE : To determine classification criteria for acute posterior multifocal placoid pigment epitheliopathy (APMPPE).

DESIGN : Machine learning of cases with APMPPE and 8 other posterior uveitides.

METHODS : Cases of posterior uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the infectious posterior/panuveitides. The resulting criteria were evaluated on the validation set.

RESULTS : One thousand sixty-eight cases of posterior uveitides, including 82 cases of APMPPE, were evaluated by machine learning. Key criteria for APMPPE included: 1) choroidal lesions with a plaque-like or placoid appearance and 2) characteristic imaging on fluorescein angiography (lesions "block early and stain late diffusely"). Overall accuracy for posterior uveitides was 92.7% in the training set and 98.0% (95% confidence interval 94.3, 99.3) in the validation set. The misclassification rates for APMPPE were 5% in the training set and 0% in the validation set.

CONCLUSIONS : The criteria for APMPPE had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

The Standardization Of Uveitis Nomenclature Sun Working Group Jabs, Douglas A Brezin, Antoine P Dick, Andrew D Levinson, Ralph D Lim, Lyndell L McCluskey, Peter Oden, Neal Palestine, Alan G Thorne, Jennifer E Trusko, Brett E Vitale, Albert Wittenberg

2021-Apr-09

Ophthalmology Ophthalmology

Classification criteria for tubulointerstitial nephritis with uveitis syndrome.

In American journal of ophthalmology ; h5-index 67.0

PURPOSE : To determine classification criteria for tubulointerstitial nephritis with uveitis (TINU) DESIGN: Machine learning of cases with TINU and 8 other anterior uveitides.

METHODS : Cases of anterior uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the anterior uveitides. The resulting criteria were evaluated on the validation set.

RESULTS : One thousand eighty-three cases of anterior uveitides, including 94 cases of TINU, were evaluated by machine learning. The overall accuracy for anterior uveitides was 97.5% in the training set and 96.7% in the validation set (95% confidence interval 92.4, 98.6). Key criteria for TINU included anterior chamber inflammation and evidence of tubulointerstitial nephritis with either 1) a positive renal biopsy or 2) evidence of nephritis (elevated serum creatinine and/or abnormal urine analysis) and an elevated urine β-2 microglobulin. The misclassification rates for TINU were 1.2% in the training set and 0% in the validation set, respectively.

CONCLUSIONS : The criteria for TINU had a low misclassification rate and appeared to perform well enough for use in clinical and translational research.

Jabs Douglas A, Denniston Alastair K, Galor Anat, Lightman Susan, McCluskey Peter, Oden Neal, Palestine Alan G, Rosenbaum James T, Saleem Sophia M, Thorne Jennifer E, Trusko Brett E

2021-Apr-09

TINU-associated anterior uveitis

General General

Classification criteria for multiple sclerosis-associated intermediate uveitis: Multiple sclerosis uveitis.

In American journal of ophthalmology ; h5-index 67.0

PURPOSE : To determine classification criteria for multiple sclerosis-associated intermediate uveitis.

DESIGN : Machine learning of cases with multiple sclerosis-associated intermediate uveitis and 4 other intermediate uveitides.

METHODS : Cases of intermediate uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the intermediate uveitides. The resulting criteria were evaluated on the validation set.

RESULTS : Five hundred eighty-nine of cases of intermediate uveitides, including 112 cases of multiple sclerosis-associated intermediate uveitis, were evaluated by machine learning. The overall accuracy for intermediate uveitides was 99.8% in the training set and 99.3% in the validation set (95% confidence interval 96.1, 99.9). Key criteria for multiple sclerosis-associated intermediate uveitis included unilateral or bilateral intermediate uveitis and a diagnosis of multiple sclerosis by the McDonald Criteria. Key exclusions included syphilis and sarcoidosis. The misclassification rates for multiple sclerosis-associated intermediate uveitis were 0 % in the training set and 0% in the validation set, respectively.

CONCLUSIONS : The criteria for multiple sclerosis-associated intermediate uveitis had a low misclassification rate and appeared to perform sufficiently well enough for use in clinical and translational research.

**

2021-Apr-09

Ophthalmology Ophthalmology

Classification criteria for juvenile idiopathic arthritis-associated chronic anterior uveitis.

In American journal of ophthalmology ; h5-index 67.0

PURPOSE : To determine classification criteria for juvenile idiopathic arthritis (JIA)-associated chronic anterior uveitis (CAU).

DESIGN : Machine learning of cases with JIA CAU and 8 other anterior uveitides.

METHODS : Cases of anterior uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the anterior uveitides. The resulting criteria were evaluated on the validation set.

RESULTS : One thousand eighty-three cases of anterior uveitides, including 202 cases of JIA CAU, were evaluated by machine learning. The overall accuracy for anterior uveitides was 97.5% in the training set and 96.7% in the validation set (95% confidence interval 92.4, 98.6). Key criteria for JIA CAU included 1) chronic anterior uveitis (or if newly diagnosed insidious onset) and 2) JIA, except for the systemic, rheumatoid factor-positive polyarthritis, and enthesitis related arthritis variants. The misclassification rates for JIA CAU were 2.4% in the training set and 0% in the validation set, respectively.

CONCLUSIONS : The criteria for JIA CAU had a low misclassification rate and appeared to perform well enough for use in clinical and translational research.

Jabs Douglas A, Acharya Nisha R, Chee Soon Phaik, Goldstein Debra, McCluskey Peter, Murray Philip I, Oden Neal, Palestine Alan G, Rosenbaum James T, Thorne Jennifer E, Trusko Brett E

2021-Apr-09

JIA-associated chronic anterior uveitis

General General

Classification criteria for syphilitic uveitis.

In American journal of ophthalmology ; h5-index 67.0

PURPOSE : To determine classification criteria for syphilitic uveitis DESIGN: Machine learning of cases with syphilitic uveitis and 24 other uveitides.

METHODS : Cases of anterior, intermediate, posterior, and panuveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were analyzed by anatomic class, and each class was split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the different uveitic classes. The resulting criteria were evaluated on the validation set.

RESULTS : Two hundred twenty-two cases of syphilitic uveitis were evaluated by machine learning with cases evaluated against other uveitides in the relevant uveitic class. Key criteria for syphilitic uveitis included a compatible uveitic presentation, (1) anterior uveitis, 2) intermediate uveitis, or 3) posterior or panuveitis with retinal, retinal pigment epithelial, or retinal vascular inflammation) and evidence of syphilis infection with a positive treponemal test. The Centers for Disease Control and Prevention reverse screening algorithm for syphilis testing is recommended. The misclassification rates for syphilitic uveitis in the training sets were: anterior uveitides 0%, intermediate uveitides 6.0%, posterior uveitides 0%, panuveitides 0%, and infectious posterior/panuveitides 8.6%. The overall accuracy of the diagnosis of syphilitic uveitis in the validation set was 100% (99% CI 99.5, 100) - i.e. the validation sets misclassification rates were 0% for each uveitic class.

CONCLUSIONS : The criteria for syphilitic uveitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

**

2021-Apr-09

Ophthalmology Ophthalmology

Classification criteria for cytomegalovirus anterior uveitis.

In American journal of ophthalmology ; h5-index 67.0

PURPOSE : To determine classification criteria for cytomegalovirus (CMV) anterior uveitis DESIGN: : Machine learning of cases with CMV anterior uveitis and 8 other anterior uveitides.

METHODS : Cases of anterior uveitides were collected in an informatics-designed preliminary database, and a final datafubase was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the anterior uveitides. The resulting criteria were evaluated on the validation set.

RESULTS : One thousand eighty-three of cases of anterior uveitides, including 89 cases of CMV anterior uveitis, were evaluated by machine learning. The overall accuracy for anterior uveitides was 97.5% in the training set and 96.7% in the validation set (95% confidence interval 92.4, 98.6). Key criteria for CMV anterior uveitis included unilateral anterior uveitis with a positive aqueous humor polymerase chain reaction assay for CMV. No clinical features reliably diagnosed CMV anterior uveitis. The misclassification rates for CMV anterior uveitis were 1.3 % in the training set and 0% in the validation set, respectively.

CONCLUSIONS : The criteria for CMV anterior uveitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

Jabs Douglas A, Caspers Laure, Chee Soon-Phaik, Galor Anat, Goldstein Debra, McCluskey Peter, Murray Philip I, Oden Neal, Palestine Alan G, Rosenbaum James T, Thorne Jennifer E, Trusko Brett E

2021-Apr-09

Cardiology Cardiology

A Deep-Learning Algorithm for Detecting Acute Myocardial Infarction.

In EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology

BACKGROUND : Delayed or misdiagnosis of acute myocardial infarction (AMI) is not unusual in the daily practice. Since 12- lead electrocardiogram (ECG) is crucial for the detection of AMI, the systematic algorithm to strengthen ECG interpretation may have important implications for improving diagnosis.

AIMS : We aimed to develop a deep learning model (DLM) as a diagnostic support tool based on a 12-lead electrocardiogram.

METHODS : This retrospective cohort study included 1,051/697 ECGs from 737/287 coronary angiogram (CAG)-validated STEMI/NSTEMI patients and 140,336 ECGs from 76,775 not-AMI patients at the emergency department. The DLM was trained and validated by 80% and 20% of these ECGs. A human-machine competition was conducted. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to evaluate the performance of the DLM.

RESULTS : The AUC of the DLM for STEMI detection was 0.976 in the human-machine competition, which was significantly better than that of the best physicians. Furthermore, the DLM independently demonstrated sufficient diagnostic capacity for STEMI detection (AUC=0.997; sensitivity, 98.4%; specificity, 96.9%). Regarding NSTEMI detection, the AUC of the combined DLM and conventional cardiac troponin I (cTnI) increased to 0.978, which was better than that of either the DLM (0.877) or cTnI (0.950).

CONCLUSIONS : The DLM may serve as a timely, objective and precise diagnostic decision support tool to assist emergency medical system-based networks and frontline physicians in detecting AMI and subsequently initiating reperfusion therapy.

Liu Wen-Cheng, Lin Chin-Sheng, Tsai Chien-Sung, Tsao Tien-Ping, Cheng Cheng-Chung, Liou Jun-Ting, Lin Wei-Shiang, Cheng Shu-Meng, Lou Yu-Sheng, Lee Chia-Cheng, Lin Chin

2021-Apr-13

General General

COVID-19 prediction using LSTM Algorithm: GCC Case Study.

In Informatics in medicine unlocked

Coronavirus-19 (COVID-19) is the black swan of 2020. Still, the human response to restrain the virus is also creating massive ripples through different systems, such as health, economy, education, and tourism. This paper focuses on research and applying Artificial Intelligence (AI) algorithms to predict COVID-19 propagation using the available time-series data and study the effect of the quality of life, the number of tests performed, and the awareness of citizens on the virus in the Gulf Cooperation Council (GCC) countries at the Gulf area. So we focused on cases in the Kingdom of Saudi Arabia (KSA), United Arab of Emirates (UAE), Kuwait, Bahrain, Oman, and Qatar. For this aim, we accessed the time-series real-datasets collected from Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). The timeline of our data is from 22 January 2020 to 25 January 2021. We have implemented the proposed model based on Long Short-Term Memory (LSTM) with ten hidden units (neurons) to predict COVID-19 confirmed and death cases. From the experimental results, we confirmed that KSA and Qatar would take the most extended period to recover from the COVID-19 virus, and the situation will be controllable in the second half of March 2021 in UAE, Kuwait, Oman, and Bahrain. Also, we calculated the root mean square error (RMSE) between the actual and predicted values of each country for confirmed and death cases, and we found that the best values for both confirmed and death cases are 320.79 and 1.84, respectively, and both are related to Bahrain. While the worst values are 1768.35 and 21.78, respectively, and both are related to KSA. On the other hand, we also calculated the mean absolute relative errors (MARE) between the actual and predicted values of each country for confirmed and death cases, and we found that the best values for both confirmed and deaths cases are 37.76 and 0.30, and these are related to Kuwait and Qatar respectively. While the worst values are 71.45 and 1.33, respectively, and both are related to KSA.

Ghany Kareem Kamal A, Zawbaa Hossam M, Sabri Heba M

2021-Apr-06

Artificial Intelligence, COVID-19, Deep Learning, LSTM, Prediction

Ophthalmology Ophthalmology

Classification criteria for Vogt-Koyanagi-Harada Disease.

In American journal of ophthalmology ; h5-index 67.0

PURPOSE : To determine classification criteria for Vogt-Koyanagi-Harada (VKH) disease DESIGN: Machine learning of cases with VKH disease and 5 other panuveitides.

METHODS : Cases of panuveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the panuveitides. The resulting criteria were evaluated on the validation set.

RESULTS : One thousand twelve cases of panuveitides, including 156 cases of early-stage VKH and 103 cases of late-stage VKH, were evaluated. Overall accuracy for panuveitides was 96.3% in the training set and 94.0% in the validation set (95% confidence interval 89.0, 96.8). Key criteria for early-stage VKH included: 1) exudative retinal detachment with characteristic appearance on fluorescein angiogram or optical coherence tomography or 2) panuveitis with ≥2 of 5 neurologic symptoms/signs. Key criteria for late-stage VKH included history of early-stage VKH and either: 1) sunset glow fundus or 2) uveitis and ≥1 of 3 cutaneous signs. The misclassification rates in the learning and validation sets for early-stage VKH were 8.0% and 7.7%, respectively, and for late-stage VKH 1.0% and 12%, respectively.

CONCLUSIONS : The criteria for VKH had a reasonably low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

Jabs Douglas A, Denniston Alastair K, Dick Andrew D, Dunn James P, Kramer Michal, McCluskey Peter, Oden Neal, Okada Annabelle A, Palestine Alan G, Read Russell W, Thorne Jennifer E, Trusko Brett E

2021-Apr-09

Ophthalmology Ophthalmology

Classification criteria for multifocal choroiditis with panuveitis.

In American journal of ophthalmology ; h5-index 67.0

PURPOSE : To determine classification criteria for multifocal choroiditis with panuveitis (MFCPU) DESIGN: : Machine learning of cases with MFCPU and 8 other posterior uveitides.

METHODS : Cases of posterior uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the posterior uveitides. The resulting criteria were evaluated on the validation set.

RESULTS : One thousand sixty-eight cases of posterior uveitides, including 138 cases of MFCPU, were evaluated by machine learning. Key criteria for MFCPU included: 1) multifocal choroiditis with the predominant lesions size >125 µm in diameter; 2) lesions outside the posterior pole (with or without posterior involvement); and either 3) punched-out atrophic chorioretinal scars or 4) more than minimal mild anterior chamber and/or vitreous inflammation. Overall accuracy for posterior uveitides was 93.9% in the training set and 98.0% (95% confidence interval 94.3, 99.3) in the validation set. The misclassification rates for MFCPU were 15% in the training set and 0% in the validation set.

CONCLUSIONS : The criteria for MFCPU had a reasonably low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

Jabs Douglas A, Brezin Antoine P, Levinson Ralph D, McCluskey Peter, Oden Neal, Palestine Alan G, Read Russell W, Thorne Jennifer E, Trusko Brett E, Vitale Albert, Wittenberg Susan E

2021-Apr-09

Ophthalmology Ophthalmology

Classification criteria for cytomegalovirus retinitis.

In American journal of ophthalmology ; h5-index 67.0

PURPOSE : To determine classification criteria for cytomegalovirus (CMV) retinitis.

DESIGN : Machine learning of cases with CMV retinitis and 4 other infectious posterior/ panuveitides.

METHODS : Cases of infectious posterior/panuveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the infectious posterior/panuveitides. The resulting criteria were evaluated on the validation set.

RESULTS : Eight hundred three cases of infectious posterior/panuveitides, including 211 cases of CMV retinitis, were evaluated by machine learning. Key criteria for CMV retinitis included: 1) necrotizing retinitis with indistinct borders due to numerous small satellites; 2) evidence of immune compromise; and either 3) a characteristic clinical appearance or 4) positive polymerase chain assay for CMV from an intraocular specimen. Characteristic appearances for CMV retinitis included: 1) wedge-shaped area of retinitis; 2) hemorrhagic retinitis; or 3) granular retinitis. Overall accuracy for infectious posterior/panuveitides was 92.1% in the training set and 93.3% (95% confidence interval 88.2, 96.3) in the validation set. The misclassification rates for CMV retinitis were 6.9% in the training set and 6.3% in the validation set.

CONCLUSIONS : The criteria for CMV retinitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

Jabs Douglas A, Belfort Rubens, Bodaghi Bahram, Graham Elizabeth, Holland Gary N, Lightman Susan L, Oden Neal, Palestine Alan G, Smith Justine R, Thorne Jennifer E, Trusko Brett E, Van Gelder Russell N

2021-Apr-09

Ophthalmology Ophthalmology

Classification criteria for tubercular uveitis.

In American journal of ophthalmology ; h5-index 67.0

PURPOSE : To determine classification criteria for tubercular uveitis DESIGN: Machine learning of cases with tubercular uveitis and 14 other uveitides.

METHODS : Cases of non-infectious posterior or panuveitis, and of infectious posterior or panuveitis were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were analyzed by anatomic class, and each class was split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the intermediate uveitides. The resulting criteria were evaluated on the validation sets.

RESULTS : Two hundred seventy-seven cases of tubercular uveitis were evaluated by machine learning against other uveitides. Key criteria for tubercular uveitis were a compatible uveitic syndrome, including: 1) anterior uveitis with iris nodules, 2) serpiginous-like tubercular choroiditis, 3) choroidal nodule (tuberculoma), 4) occlusive retinal vasculitis, and 5) in hosts with evidence of active systemic tuberculosis, multifocal choroiditis; and evidence of tuberculosis, including: 1) histologically- or microbiologically-confirmed infection, 2) positive interferon-Ɣ release assay test, or 3) positive tuberculin skin test. The overall accuracy of the diagnosis of tubercular uveitis versus other uveitides in the validation set was 98.2% (95% CI 96.5, 99.1). The misclassification rates for tubercular uveitis were: training set 3.4%; and validation set 3.6%.

CONCLUSIONS : The criteria for tubercular uveitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

The Standardization Of Uveitis Nomenclature Sun Working Group Jabs, Douglas A Belfort, Rubens Bodaghi, Bahram Graham, Elizabeth Gupta, Vishali Holland, Gary N Lightman, Susan L Oden, Neal Palestine, Alan G Smith, Justine R Thorne, Jennifer E Trusko

2021-Apr-09

Tubercular uveitis

Ophthalmology Ophthalmology

Classification criteria for serpiginous choroiditis.

In American journal of ophthalmology ; h5-index 67.0

PURPOSE : To determine classification criteria for serpiginous choroiditis.

DESIGN : Machine learning of cases with serpiginous choroiditis and 8 other posterior uveitides.

METHODS : Cases of posterior uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the infectious posterior/panuveitides. The resulting criteria were evaluated on the validation set.

RESULTS : One thousand sixty-eight cases of posterior uveitides, including 122 cases of serpiginous choroiditis, were evaluated by machine learning. Key criteria for serpiginous choroiditis included: 1) choroiditis with an ameboid or serpentine shape; 2) characteristic imaging on fluorescein angiography or fundus autofluorescence; 3) absent to mild anterior chamber and vitreous inflammation; and 4) the exclusion of tuberculosis. Overall accuracy for posterior uveitides was 93.9% in the training set and 98.0% (95% confidence interval 94.3, 99.3) in the validation set. The misclassification rates for serpiginous choroiditis were 0% in both the training set and the validation set.

CONCLUSIONS : The criteria for serpiginous choroiditis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

Jabs Douglas A, Brezin Antoine P, Levinson Ralph D, Oden Neal, Palestine Alan G, Rao Narsing A, Thorne Jennifer E, Trusko Brett E, Vitale Albert, Wittenberg Susan E

2021-Apr-09

Ophthalmology Ophthalmology

Classification criteria for acute retinal necrosis syndrome.

In American journal of ophthalmology ; h5-index 67.0

PURPOSE : To determine classification criteria for acute retinal necrosis (ARN).

DESIGN : Machine learning of cases with ARN and 4 other infectious posterior/ panuveitides.

METHODS : Cases of infectious posterior/panuveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the infectious posterior/panuveitides. The resulting criteria were evaluated on the validation set.

RESULTS : Eight hundred three cases of infectious posterior/panuveitides, including 186 cases of ARN, were evaluated by machine learning. Key criteria for ARN included: 1) peripheral necrotizing retinitis; and either 2) polymerase chain reaction assay of an intraocular fluid specimen positive for either herpes simplex virus or varicella zoster virus; or 3) a characteristic clinical appearance with circumferential or confluent retinitis, retinal vascular sheathing and/or occlusion, and more than minimal vitritis. Overall accuracy for infectious posterior/panuveitides was 92.1% in the training set and 93.3% (95% confidence interval 88.2, 96.3) in the validation set. The misclassification rates for ARN were 15% in the training set and 11.5% in the validation set.

CONCLUSIONS : The criteria for ARN had a reasonably low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

Jabs Douglas A, Belfort Rubens, Bodaghi Bahram, Graham Elizabeth, Holland Gary N, Lightman Susan L, Margolis Todd P, Oden Neal, Palestine Alan, Smith Justine R, Thorne Jennifer E, Trusko Brett E, Van Gelder Russell N

2021-Apr-09

Acute retinal necrosis

Ophthalmology Ophthalmology

Classification criteria for punctate inner choroiditis.

In American journal of ophthalmology ; h5-index 67.0

PURPOSE : To determine classification criteria for punctate inner choroiditis (PIC).

DESIGN : Machine learning of cases with PIC and 8 other posterior uveitides.

METHODS : Cases of posterior uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the posterior uveitides. The resulting criteria were evaluated on the validation set.

RESULTS : One thousand sixty-eight cases of posterior uveitides, including 144 cases of PIC, were evaluated by machine learning. Key criteria for PIC included: 1) "punctate" appearing choroidal spots <250 µm in diameter; 2) absent to minimal anterior chamber and vitreous inflammation; and 3) involvement of the posterior pole with or without mid-periphery. Overall accuracy for posterior uveitides was 93.9% in the training set and 98.0% (95% confidence interval 94.3, 99.3) in the validation set. The misclassification rates for PIC were 15% in the training set and 9% in the validation set.

CONCLUSIONS : The criteria for PIC had a reasonably low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

The Standardization Of Uveitis Nomenclature Sun Working Group Jabs, Douglas A Brezin, Antoine P Levinson, Ralph D Lightman, Susan L McCluskey, Peter Oden, Neal Palestine, Alan G Rao, Narsing A Thorne, Jennifer E Trusko, Brett E Vitale, Albert Wittenberg

2021-Apr-09

Ophthalmology Ophthalmology

Classification criteria for varicella zoster virus anterior uveitis.

In American journal of ophthalmology ; h5-index 67.0

PURPOSE : To determine classification criteria for varicella zoster virus (VZV) anterior uveitis DESIGN: Machine learning of cases with VZV anterior uveitis and 8 other anterior uveitides.

METHODS : Cases of anterior uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the anterior uveitides. The resulting criteria were evaluated on the validation set.

RESULTS : One thousand eighty-three cases of anterior uveitides, including 123 cases of VZV anterior uveitis, were evaluated by machine learning. The overall accuracy for anterior uveitides was 97.5% in the training set and 96.7% in the validation set (95% confidence interval 92.4, 98.6). Key criteria for VZV anterior uveitis included unilateral anterior uveitis with either 1) positive aqueous humor polymerase chain reaction assay for VZV; 2) sectoral iris atrophy in a patient ≥60 years of age; or 3) concurrent or recent dermatomal herpes zoster. The misclassification rates for VZV anterior uveitis were 0.9% in the training set and 0% in the validation set, respectively.

CONCLUSIONS : The criteria for VZV anterior uveitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

Jabs Douglas A, Acharya Nisha R, Caspers Laure, Chee Soon-Phaik, Goldstein Debra, McCluskey Peter, Murray Philip I, Oden Neal, Palestine Alan G, Rosenbaum James T, Thorne Jennifer E, Trusko Brett E

2021-Apr-09

Ophthalmology Ophthalmology

Classification criteria for herpes simplex virus anterior uveitis.

In American journal of ophthalmology ; h5-index 67.0

PURPOSE : To determine classification criteria for herpes simplex virus (HSV) anterior uveitis DESIGN: : Machine learning of cases with HSV anterior uveitis and 8 other anterior uveitides.

METHODS : Cases of anterior uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the anterior uveitides. The resulting criteria were evaluated on the validation set.

RESULTS : One thousand eighty-three of cases of anterior uveitides, including 101 cases of HSV anterior uveitis, were evaluated by machine learning. The overall accuracy for anterior uveitides was 97.5% in the training set and 96.7% in the validation set (95% confidence interval 92.4, 98.6). Key criteria for HSV anterior uveitis included unilateral anterior uveitis with either 1) positive aqueous humor polymerase chain reaction assay for HSV; 2) sectoral iris atrophy in a patient ≤50 years of age; or 3) HSV keratitis. The misclassification rates for HSV anterior uveitis were 8.3% in the training set and 17% in the validation set, respectively.

CONCLUSIONS : The criteria for HSV anterior uveitis had a reasonably low misclassification rate and appeared to perform well enough for use in clinical and translational research.

Jabs Douglas A, Acharya Nisha R, Caspers Laure, Chee Soon-Phaik, Goldstein Debra, McCluskey Peter, Murray Philip I, Oden Neal, Palestine Alan G, Rosenbaum James T, Thorne Jennifer E, Trusko Brett E

2021-Apr-09

Ophthalmology Ophthalmology

Classification criteria for Behçet Disease Uveitis.

In American journal of ophthalmology ; h5-index 67.0

PURPOSE : To determine classification criteria for Behçet disease uveitis.

DESIGN : Machine learning of cases with Behçet disease and 5 other panuveitides.

METHODS : Cases of panuveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the intermediate uveitides. The resulting criteria were evaluated on the validation set.

RESULTS : One thousand twelve of cases panuveitides, including 194 cases of Behçet disease with uveitis, were evaluated by machine learning. The overall accuracy for panuveitides was 96.3% in the training set and 94.0% in the validation set (95% confidence interval 89.0, 96.8). Key criteria for Behçet disease uveitis were a diagnosis of Behçet disease using the International Study Group for Behçet Disease criteria and a compatible uveitis, including: 1) anterior uveitis; 2) anterior chamber and vitreous inflammation; 3) posterior uveitis with retinal vasculitis and/or focal infiltrates; or 4) panuveitis with retinal vasculitis and/or focal infiltrates. The misclassification rates for Behçet disease uveitis were 0.6 % in the training set and 0% in the validation set, respectively.

CONCLUSIONS : The criteria for Behçet disease uveitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

Jabs Douglas A, Dick Andrew D, Dunn James P, Kramer Michal, McCluskey Peter, Oden Neal, Okada Annabelle A, Palestine Alan G, Read Russell W, Thorne Jennifer E, Trusko Brett E, Yeh Steven

2021-Apr-09

Radiology Radiology

Quality gaps in public pancreas imaging datasets: Implications & challenges for AI applications.

In Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.]

OBJECTIVE : Quality gaps in medical imaging datasets lead to profound errors in experiments. Our objective was to characterize such quality gaps in public pancreas imaging datasets (PPIDs), to evaluate their impact on previously published studies, and to provide post-hoc labels and segmentations as a value-add for these PPIDs.

METHODS : We scored the available PPIDs on the medical imaging data readiness (MIDaR) scale, and evaluated for associated metadata, image quality, acquisition phase, etiology of pancreas lesion, sources of confounders, and biases. Studies utilizing these PPIDs were evaluated for awareness of and any impact of quality gaps on their results. Volumetric pancreatic adenocarcinoma (PDA) segmentations were performed for non-annotated CTs by a junior radiologist (R1) and reviewed by a senior radiologist (R3).

RESULTS : We found three PPIDs with 560 CTs and six MRIs. NIH dataset of normal pancreas CTs (PCT) (n = 80 CTs) had optimal image quality and met MIDaR A criteria but parts of pancreas have been excluded in the provided segmentations. TCIA-PDA (n = 60 CTs; 6 MRIs) and MSD(n = 420 CTs) datasets categorized to MIDaR B due to incomplete annotations, limited metadata, and insufficient documentation. Substantial proportion of CTs from TCIA-PDA and MSD datasets were found unsuitable for AI due to biliary stents [TCIA-PDA:10 (17%); MSD:112 (27%)] or other factors (non-portal venous phase, suboptimal image quality, non-PDA etiology, or post-treatment status) [TCIA-PDA:5 (8.5%); MSD:156 (37.1%)]. These quality gaps were not accounted for in any of the 25 studies that have used these PPIDs (NIH-PCT:20; MSD:1; both: 4). PDA segmentations were done by R1 in 91 eligible CTs (TCIA-PDA:42; MSD:49). Of these, corrections were made by R3 in 16 CTs (18%) (TCIA-PDA:4; MSD:12) [mean (standard deviation) Dice: 0.72(0.21) and 0.63(0.23) respectively].

CONCLUSION : Substantial quality gaps, sources of bias, and high proportion of CTs unsuitable for AI characterize the available limited PPIDs. Published studies on these PPIDs do not account for these quality gaps. We complement these PPIDs through post-hoc labels and segmentations for public release on the TCIA portal. Collaborative efforts leading to large, well-curated PPIDs supported by adequate documentation are critically needed to translate the promise of AI to clinical practice.

Suman Garima, Patra Anurima, Korfiatis Panagiotis, Majumder Shounak, Chari Suresh T, Truty Mark J, Fletcher Joel G, Goenka Ajit H

2021-Apr-02

Benchmarking, Bias, Deep learning, Metadata, Pancreatic carcinoma

General General

Machine Learning Approaches in COVID-19 Diagnosis, Mortality, and Severity Risk Prediction: A Review.

In Informatics in medicine unlocked

The existence of widespread COVID-19 infections has prompted worldwide efforts to control and manage the virus, and hopefully curb it completely. One important line of research is the use of machine learning (ML) to understand and fight COVID-19. This is currently an active research field. Although there are already many surveys in the literature, there is a need to keep up with the rapidly growing number of publications on COVID-19-related applications of ML. This paper presents a review of recent reports on ML algorithms used in relation to COVID-19. We focus on the potential of ML for two main applications: diagnosis of COVID-19 and prediction of mortality risk and severity, using readily available clinical and laboratory data. Aspects related to algorithm types, training data sets, and feature selection are discussed. As we cover work published between January 2020 and January 2021, a few key points have come to light. The bulk of the machine learning algorithms used in these two applications are supervised learning algorithms. The established models are yet to be used in real-world implementations, and much of the associated research is experimental. The diagnostic and prognostic features discovered by ML models are consistent with results presented in the medical literature. A limitation of the existing applications is the use of imbalanced data sets that are prone to selection bias.

Alballa Norah, Al-Turaiki Isra

2021-Apr-03

COVID-19, Machine learning, artificial intelligence, diagnosis, feature selection, prognosis

Ophthalmology Ophthalmology

Classification criteria for Fuchs uveitis syndrome.

In American journal of ophthalmology ; h5-index 67.0

PURPOSE : To determine classification criteria for Fuchs uveitis syndrome.

DESIGN : Machine learning of cases with Fuchs uveitis syndrome and 8 other anterior uveitides.

METHODS : Cases of anterior uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the anterior uveitides. The resulting criteria were evaluated on the validation set.

RESULTS : One thousand eighty-three cases of anterior uveitides, including 146 cases of Fuchs uveitis syndrome, were evaluated by machine learning. The overall accuracy for anterior uveitides was 97.5% in the training set and 96.7% in the validation set (95% confidence interval 92.4, 98.6). Key criteria for Fuchs uveitis syndrome included unilateral anterior uveitis with or without vitritis and either: 1) heterochromia or 2) unilateral diffuse iris atrophy and stellate keratic precipitates. The overall accuracy for anterior uveitides was 97.5% in the training set (95% confidence interval [CI] 96.3, 98.4) and 96.7% in the validation set (95% CI 92.4, 98.6). The misclassification rates for FUS were 4.7% in the training set and 5.5% in the validation set, respectively.

CONCLUSIONS : The criteria for Fuchs uveitis syndrome had a low misclassification rate and appeared to perform well enough for use in clinical and translational research.

Working Group The Standardization Of Uveitis Nomenclature Sun, Jabs Douglas A, Acharya Nisha R, Chee Soon-Phaik, Goldstein Debra, McCluskey Peter, Murray Philip I, Oden Neal, Palestine Alan G, Rosenbaum James T, Thorne Jennifer E, Trusko Brett E

2021-Apr-09

Ophthalmology Ophthalmology

Classification criteria for pars planitis.

In American journal of ophthalmology ; h5-index 67.0

PURPOSE : To determine classification criteria for pars planitis DESIGN: Machine learning of cases with pars planitis and 4 other intermediate uveitides.

METHODS : Cases of intermediate uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the intermediate uveitides. The resulting criteria were evaluated on the validation set.

RESULTS : Five hundred eighty-nine cases of intermediate uveitides, including 226 cases of pars planitis, were evaluated by machine learning. The overall accuracy for intermediate uveitides was 99.8% in the training set and 99.3% in the validation set (95% confidence interval 96.1, 99.9). Key criteria for pars planitis included unilateral or bilateral intermediate uveitis with either 1) snowballs in the vitreous or 2) snowbanks on the pars plana. Key exclusions included: 1) multiple sclerosis, 2) sarcoidosis, and 3) syphilis. The misclassification rates for pars planitis were 0 % in the training set and 1.7% in the validation set, respectively.

CONCLUSIONS : The criteria for pars planitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

The Standardization Of Uveitis Nomenclature Sun Working Group Jabs, Douglas A Denniston, Alastair K Dick, Andrew D Dunn, James P Kramer, Michal Oden, Neal Okada, Annabelle A Palestine, Alan G Read, Russell W Thorne, Jennifer E Trusko, Brett E Yeh

2021-Apr-09

Ophthalmology Ophthalmology

Classification criteria for Sympathetic Ophthalmia.

In American journal of ophthalmology ; h5-index 67.0

PURPOSE : To determine classification criteria for sympathetic ophthalmia DESIGN: Machine learning of cases with sympathetic ophthalmia and 5 other panuveitides.

METHODS : Cases of panuveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the panuveitides. The resulting criteria were evaluated on the validation set.

RESULTS : One thousand twelve cases of panuveitides, including 110 cases of sympathetic ophthalmia, were evaluated by machine learning. The overall accuracy for panuveitides was 96.3% in the training set and 94.0% in the validation set (95% confidence interval 89.0, 96.8). Key criteria for sympathetic ophthalmia included bilateral uveitis with 1) a history of unilateral ocular trauma or surgery and 2) an anterior chamber and vitreous inflammation or a panuveitis with choroidal involvement. The misclassification rates for sympathetic ophthalmia were 4.2 % in the training set and 6.7% in the validation set, respectively.

CONCLUSIONS : The criteria for sympathetic ophthalmia had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

Jabs Douglas A, Dick Andrew, Kramer Michal, Muccioli Cristina, Oden Neal, Okada Annabelle A, Palestine Alan G, Rao Narsing A, Read Russell W, Thorne Jennifer E, Trusko Brett E

2021-Apr-09

Ophthalmology Ophthalmology

Classification criteria for spondyloarthritis/HLA-B27-associated anterior uveitis.

In American journal of ophthalmology ; h5-index 67.0

PURPOSE : To determine classification criteria for spondyloarthritis/HLA-B27-associated anterior uveitis DESIGN: Machine learning of cases with spondyloarthritis/HLA-B27-associated anterior uveitis and 8 other anterior uveitides.

METHODS : Cases of anterior uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the anterior uveitides. The resulting criteria were evaluated on the validation set.

RESULTS : One thousand eighty-three cases of anterior uveitides, including 184 cases of spondyloarthritis/HLA-B27-associated anterior uveitis, were evaluated by machine learning. The overall accuracy for anterior uveitides was 97.5% in the training set (95% confidence interval [CI] 96.3, 98.4) and 96.7% in the validation set (95% CI 92.4, 98.6). Key criteria for spondyloarthritis/HLA-B27-associated anterior uveitis included 1) acute or recurrent acute unilateral or unilateral alternating anterior uveitis with either spondyloarthritis or a positive test for HLA-B27 or 2) chronic anterior uveitis with a history of the classic course and either spondyloarthritis or HLA-B27 or 3) anterior uveitis with both spondyloarthritis and HLA-B27. The misclassification rates for spondyloarthritis/HLA-B27-associated anterior uveitis were 0% in the training set and 3.6% in the validation set, respectively.

CONCLUSIONS : The criteria for spondyloarthritis/HLA-B27-associated anterior uveitis had a low misclassification rate and appeared to perform well enough for use in clinical and translational research.

Jabs Douglas A, Acharya Nisha R, Chee Soon-Phaik, Goldstein Debra, McCluskey Peter, Murray Philip I, Oden Neal, Palestine Alan G, Rosenbaum James T, Thorne Jennifer E, Trusko Brett E

2021-Apr-09

Ophthalmology Ophthalmology

Classification criteria for birdshot chorioretinitis.

In American journal of ophthalmology ; h5-index 67.0

PURPOSE : To determine classification criteria for birdshot chorioretinitis.

DESIGN : Machine learning of cases with birdshot chorioretinitis and 8 other posterior uveitides.

METHODS : Cases of posterior uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the infectious posterior/panuveitides. The resulting criteria were evaluated on the validation set.

RESULTS : One thousand sixty-eight cases of posterior uveitides, including 207 cases of birdshot chorioretinitis, were evaluated by machine learning. Key criteria for birdshot chorioretinitis included a multifocal choroiditis with: 1) the characteristic appearance a bilateral multifocal choroiditis with cream-colored or yellow-orange, oval or round choroidal spots ("birdshot" spots); 2) absent to mild anterior chamber inflammation; and 3) absent to moderate vitreous inflammation; or multifocal choroiditis with positive HLA-A29 testing and either: 1) classic "birdshot spots" or 2) characteristic imaging on indocyanine green angiography. Overall accuracy for posterior uveitides was 93.9% in the training set and 98.0% (95% confidence interval 94.3, 99.3) in the validation set. The misclassification rates for birdshot chorioretinitis were 10% in the training set and 0% in the validation set.

CONCLUSIONS : The criteria for birdshot chorioretinitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

The Standardization Of Uveitis Nomenclature Sun Working Group Jabs, Douglas A Brezin, Antoine P Levinson, Ralph D McCluskey, Peter Oden, Neal Palestine, Alan G Read, Russell W Thorne, Jennifer E Trusko, Brett E Vitale, Albert Wittenberg

2021-Apr-09

General General

Classification criteria for toxoplasmic retinitis.

In American journal of ophthalmology ; h5-index 67.0

PURPOSE : To determine classification criteria for toxoplasmic retinitis.

DESIGN : Machine learning of cases with toxoplasmic retinitis and 4 other infectious posterior/ panuveitides.

METHODS : Cases of infectious posterior/panuveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the infectious posterior/panuveitides. The resulting criteria were evaluated on the validation set.

RESULTS : Eight hundred three cases of infectious posterior/panuveitides, including 174 cases of toxoplasmic retinitis, were evaluated by machine learning. Key criteria for toxoplasmic retinitis included: 1) focal or paucifocal necrotizing retinitis and either; 2) positive polymerase chain reaction assay for Toxoplasma gondii from an intraocular specimen or 3) the characteristic clinical picture of a round or oval retinitis lesion proximal to a hyperpigmented and/or atrophic chorioretinal scar. Overall accuracy for infectious posterior/panuveitides was 92.1% in the training set and 93.3% (95% confidence interval 88.2, 96.3) in the validation set. The misclassification rates for toxoplasmic retinitis were 8.2% in the training set and 10% in the validation set.

CONCLUSIONS : The criteria for toxoplasmic retinitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

**

2021-Apr-09

Ophthalmology Ophthalmology

Classification criteria for sarcoidosis-associated uveitis.

In American journal of ophthalmology ; h5-index 67.0

PURPOSE : To determine classification criteria for sarcoidosis-associated uveitis DESIGN: Machine learning of cases with sarcoid uveitis and 15 other uveitides.

METHODS : Cases of anterior, intermediate, and panuveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were analyzed by anatomic class, and each class was split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training sets to determine a parsimonious set of criteria that minimized the misclassification rate among the intermediate uveitides. The resulting criteria were evaluated on the validation sets.

RESULTS : One thousand eighty-three anterior uveitides, 589 intermediate uveitides, and 1012 panuveitides, including 278 cases of sarcoidosis-associated uveitis, were evaluated by machine learning. Key criteria for sarcoidosis-associated uveitis included a compatible uveitic syndrome of any anatomic class and evidence of sarcoidosis, either 1) a tissue biopsy demonstrating non-caseating granulomata or 2) bilateral hilar adenopathy on chest imaging. The overall accuracy of the diagnosis of sarcoidosis-associated uveitis in the validation set was 99.7% (95% confidence interval 98.8, 99.9).The misclassification rates for sarcoidosis-associated uveitis in the training sets were: anterior uveitis 3.2%, intermediate uveitis 2.6%, and panuveitis 1.2%; in the validation sets the misclassification rates were: anterior uveitis 0%, intermediate uveitis 0%, and panuveitis 0%, respectively.

CONCLUSIONS : The criteria for sarcoidosis-associated uveitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

Jabs Douglas A, Acharya Nisha R, Denniston Alastair K, Lightman Susan L, McCluskey Peter, Oden Neal, Okada Annabelle A, Palestine Alan G, Thorne Jennifer E, Trusko Brett E, Vitale Albert

2021-Apr-09

General General

A direct comparison of theory-driven and machine learning prediction of suicide: A meta-analysis.

In PloS one ; h5-index 176.0

Theoretically-driven models of suicide have long guided suicidology; however, an approach employing machine learning models has recently emerged in the field. Some have suggested that machine learning models yield improved prediction as compared to theoretical approaches, but to date, this has not been investigated in a systematic manner. The present work directly compares widely researched theories of suicide (i.e., BioSocial, Biological, Ideation-to-Action, and Hopelessness Theories) to machine learning models, comparing the accuracy between the two differing approaches. We conducted literature searches using PubMed, PsycINFO, and Google Scholar, gathering effect sizes from theoretically-relevant constructs and machine learning models. Eligible studies were longitudinal research articles that predicted suicide ideation, attempts, or death published prior to May 1, 2020. 124 studies met inclusion criteria, corresponding to 330 effect sizes. Theoretically-driven models demonstrated suboptimal prediction of ideation (wOR = 2.87; 95% CI, 2.65-3.09; k = 87), attempts (wOR = 1.43; 95% CI, 1.34-1.51; k = 98), and death (wOR = 1.08; 95% CI, 1.01-1.15; k = 78). Generally, Ideation-to-Action (wOR = 2.41, 95% CI = 2.21-2.64, k = 60) outperformed Hopelessness (wOR = 1.83, 95% CI 1.71-1.96, k = 98), Biological (wOR = 1.04; 95% CI .97-1.11, k = 100), and BioSocial (wOR = 1.32, 95% CI 1.11-1.58, k = 6) theories. Machine learning provided superior prediction of ideation (wOR = 13.84; 95% CI, 11.95-16.03; k = 33), attempts (wOR = 99.01; 95% CI, 68.10-142.54; k = 27), and death (wOR = 17.29; 95% CI, 12.85-23.27; k = 7). Findings from our study indicated that across all theoretically-driven models, prediction of suicide-related outcomes was suboptimal. Notably, among theories of suicide, theories within the Ideation-to-Action framework provided the most accurate prediction of suicide-related outcomes. When compared to theoretically-driven models, machine learning models provided superior prediction of suicide ideation, attempts, and death.

Schafer Katherine M, Kennedy Grace, Gallyer Austin, Resnik Philip

2021

Public Health Public Health

Comparison of Multiple Machine Learning-based Predictions of Growth in COVID-19 Confirmed Infection Cases in Countries using Non-Pharmaceutical Interventions and Cultural Dimensions Data: Development and Validation.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : National governments have implemented non-pharmaceutical interventions to control and mitigate against the COVID-19 pandemic.

OBJECTIVE : We investigate the prediction of future daily national Confirmed Infection Growths - the percentage change in total cumulative cases across 14 days for 114 countries using non-pharmaceutical intervention metrics and cultural dimension metrics, which are metrics indicative of specific national sociocultural norms.

METHODS : We combine the OxCGRT dataset, Hofstede's cultural dimensions, and COVID-19 daily reported infection case numbers to train and evaluate five non-time series machine learning models in predicting Confirmed Infection Growth. We use three validation methods - in-distribution, out-of-distribution, and country-based cross-validation - for evaluation, each applicable to a different use case of the models.

RESULTS : Our results demonstrate high R2 values between the labels and predictions for the in-distribution method (0.959), and moderate R2 values for the out-of-distribution and country-based cross-validation methods (0.513 and 0.574, respectively) using random forest and AdaBoost regression. While these models may be used to predict the Confirmed Infection Growth, the differing accuracies obtained from the three tasks suggest a strong influence of the use case.

CONCLUSIONS : This work provides new considerations in using machine learning techniques with non-pharmaceutical interventions and cultural dimensions data for predicting the national growth of confirmed infections of COVID-19.

CLINICALTRIAL :

Yeung Arnold Ys, Roewer-Despres Francois, Rosella Laura, Rudzicz Frank

2021-Mar-23

General General

Data Extrapolation from Learned Prior Images for Truncation Correction in Computed Tomography.

In IEEE transactions on medical imaging ; h5-index 74.0

Data truncation is a common problem in computed tomography (CT). Truncation causes cupping artifacts inside the field-of-view (FOV) and anatomical structures missing outside the FOV. Deep learning has achieved impressive results in CT reconstruction from limited data. However, its robustness is still a concern for clinical applications. Although the image quality of learning-based compensation schemes may be inadequate for clinical diagnosis, they can provide prior information for more accurate extrapolation than conventional heuristic extrapolation methods. With extrapolated projection, a conventional image reconstruction algorithm can be applied to obtain a final reconstruction. In this work, a general plug-and-play (PnP) method for truncation correction is proposed based on this idea, where various deep learning methods and conventional reconstruction algorithms can be plugged in. Such a PnP method integrates data consistency for measured data and learned prior image information for truncated data. This shows to have better robustness and interpretability than deep learning only. To demonstrate the efficacy of the proposed PnP method, two state-of-the-art deep learning methods, FBPConvNet and Pix2pixGAN, are investigated for truncation correction in cone-beam CT in noise-free and noisy cases. Their robustness is evaluated by showing false negative and false positive lesion cases. With our proposed PnP method, false lesion structures are corrected for both deep learning methods. For FBPConvNet, the root-mean-square error (RMSE) inside the FOV can be improved from 92HU to around 30HU by PnP in the noisy case. Pix2pixGAN solely achieves better image quality than FBPConvNet solely for truncation correction in general. PnP further improves the RMSE inside the FOV from 42HU to around 27HU for Pix2pixGAN. The efficacy of PnP is also demonstrated on real clinical head data.

Huang Yixing, Preuhs Alexander, Manhart Michael, Lauritsch Guenter, Maier Andreas

2021-Apr-12

General General

Artificial intelligence applications in restorative dentistry: A systematic review.

In The Journal of prosthetic dentistry ; h5-index 51.0

STATEMENT OF PROBLEM : Artificial intelligence (AI) applications are increasing in restorative procedures. However, the current development and performance of AI in restorative dentistry applications has not yet been systematically documented and analyzed.

PURPOSE : The purpose of this systematic review was to identify and evaluate the ability of AI models in restorative dentistry to diagnose dental caries and vertical tooth fracture, detect tooth preparation margins, and predict restoration failure.

MATERIAL AND METHODS : An electronic systematic review was performed in 5 databases: MEDLINE/PubMed, EMBASE, World of Science, Cochrane, and Scopus. A manual search was also conducted. Studies with AI models were selected based on 4 criteria: diagnosis of dental caries, diagnosis of vertical tooth fracture, detection of the tooth preparation finishing line, and prediction of restoration failure. Two investigators independently evaluated the quality assessment of the studies by applying the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi-Experimental Studies (nonrandomized experimental studies). A third investigator was consulted to resolve lack of consensus.

RESULTS : A total of 34 articles were included in the review: 29 studies included AI techniques for the diagnosis of dental caries or the elaboration of caries and postsensitivity prediction models, 2 for the diagnosis of vertical tooth fracture, 1 for the tooth preparation finishing line location, and 2 for the prediction of the restoration failure. Among the studies reviewed, the AI models tested obtained a caries diagnosis accuracy ranging from 76% to 88.3%, sensitivity ranging from 73% to 90%, and specificity ranging from 61.5% to 93%. The caries prediction accuracy among the studies ranged from 83.6% to 97.1%. The studies reported an accuracy for the vertical tooth fracture diagnosis ranging from 88.3% to 95.7%. The article using AI models to locate the finishing line reported an accuracy ranging from 90.6% to 97.4%.

CONCLUSIONS : AI models have the potential to provide a powerful tool for assisting in the diagnosis of caries and vertical tooth fracture, detecting the tooth preparation margin, and predicting restoration failure. However, the dental applications of AI models are still in development. Further studies are required to assess the clinical performance of AI models in restorative dentistry.

Revilla-León Marta, Gómez-Polo Miguel, Vyas Shantanu, Barmak Basir A, Özcan Mutlu, Att Wael, Krishnamurthy Vinayak R

2021-Apr-09

General General

Covid, AI, and Robotics-A Neurologist's Perspective.

In Frontiers in robotics and AI

Two of the major revolutions of this century are the Artificial Intelligence and Robotics. These technologies are penetrating through all disciplines and faculties at a very rapid pace. The application of these technologies in medicine, specifically in the context of Covid 19 is paramount. This article briefly reviews the commonly applied protocols in the Health Care System and provides a perspective in improving the efficiency and effectiveness of the current system. This article is not meant to provide a literature review of the current technology but rather provides a personal perspective of the author regarding what could happen in the ideal situation.

Ahmed Syed Nizamuddin

2021

AI, COVID-19, artificial intelligence, neurologist, neurology, robotics, telemedicine

General General

Antibody Disulfide Bond Reduction and Recovery during Biopharmaceutical Process Development - A Review.

In Biotechnology and bioengineering

Antibody disulfide bond reduction has been a challenging issue in monoclonal antibody manufacturing. It could lead to decrease of product purity and failure to meet targeted product profile and/or specifications. More importantly, disulfide bond reduction could also impact drug safety and efficacy. Scientists across industry have been examining the root causes and developing mitigation strategies to address the challenge. In recent years, with the development of high titer mammalian cell culture processes to meet the rapidly growing demand for antibody biopharmaceuticals, disulfide bond reduction has been observed more frequently. Thus, it is necessary to continue evolving the disulfide reduction mitigation strategies and developing novel approaches to maintain high product quality. Additionally, in recent years as more complex molecules (such as bispecific and trispecific antibodies) emerge, the molecular heterogeneity due to incomplete formation of the interchain disulfide bonds becomes a more imperative challenging issue. Given the disulfide reduction challenges that biotech industry is facing, in this review, we provide a comprehensive scientific summary of the root cause analysis of disulfide reduction during process development of antibody therapeutics, mitigation strategies and its potential remediated recovery based on published papers. First, this paper intends to highlight different aspects of the root cause for disulfide reduction. Secondly, to provide a broader understanding of the disulfide bond reduction in downstream process, this paper discusses disulfide bond reduction impact on product stability, associated analytical methods for disulfide bond reduction detection and characterization, process control strategies as well as their manufacturing implementation. In addition, brief perspectives on the development of future mitigation strategies are also reviewed, including platform alignment, mitigation strategy application for the emerging new modalities such as bispecific and trispecific antibodies as well as using machine learning to identify molecule susceptibility of disulfide bond reduction. The data in this review are originated from the published papers. This article is protected by copyright. All rights reserved.

Ren Tingwei, Tan Zhijun, Ehamparanathan Vivekh, Lewandowski Angela, Ghose Sanchayita, Li Zheng Jian

2021-Apr-12

Antibody, Disulfide bond, Process development, Reduction/Oxidation

General General

Artificial intelligence to deep learning: machine intelligence approach for drug discovery.

In Molecular diversity

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

Gupta Rohan, Srivastava Devesh, Sahu Mehar, Tiwari Swati, Ambasta Rashmi K, Kumar Pravir

2021-Apr-12

Artificial intelligence, Artificial neural networks, Computer-aided drug design, Deep learning, Drug design and discovery, Drug repurposing, Machine learning, Quantitative structure–activity relationship, Virtual screening

Radiology Radiology

Current and emerging artificial intelligence applications for pediatric abdominal imaging.

In Pediatric radiology

Artificial intelligence (AI) uses computers to mimic cognitive functions of the human brain, allowing inferences to be made from generally large datasets. Traditional machine learning (e.g., decision tree analysis, support vector machines) and deep learning (e.g., convolutional neural networks) are two commonly employed AI approaches both outside and within the field of medicine. Such techniques can be used to evaluate medical images for the purposes of automated detection and segmentation, classification tasks (including diagnosis, lesion or tissue characterization, and prediction), and image reconstruction. In this review article we highlight recent literature describing current and emerging AI methods applied to abdominal imaging (e.g., CT, MRI and US) and suggest potential future applications of AI in the pediatric population.

Dillman Jonathan R, Somasundaram Elan, Brady Samuel L, He Lili

2021-Apr-12

Abdomen, Artificial intelligence, Children, Computed tomography, Deep learning, Machine learning, Magnetic resonance imaging

General General

Evaluation of the effluent quality parameters of wastewater treatment plant based on uncertainty analysis and post-processing approaches (case study).

In Water science and technology : a journal of the International Association on Water Pollution Research

Wastewater treatment plants (WWTPs) are highly complicated and dynamic systems and so their appropriate operation, control, and accurate simulation are essential. The simulation of WWTPs according to the process complexity has become an important issue in growing environmental awareness. In recent decades, artificial intelligence approaches have been used as effective tools in order to investigate environmental engineering issues. In this study, the effluent quality of Tabriz WWTP was assessed using two intelligence models, namely support Vector Machine (SVM) and artificial neural network (ANN). In this regard, several models were developed based on influent variables and tested via SVM and ANN methods. Three time scales, daily, weekly, and monthly, were investigated in the modeling process. On the other hand, since applied methods were sensitive to input variables, the Monte Carlo uncertainty analysis method was used to investigate the best-applied model dependability. It was found that both models had an acceptable degree of uncertainty in modeling the effluent quality of Tabriz WWTP. Next, ensemble approaches were applied to improve the prediction performance of Tabriz WWTP. The obtained results comparison showed that the ensemble methods represented better efficiency than single approaches in predicting the performance of Tabriz WWTP.

Hejabi Nasim, Saghebian Seyed Mahdi, Aalami Mohammad Taghi, Nourani Vahid

2021-Apr

General General

Sparse angle CT reconstruction with weighted dictionary learning algorithm based on adaptive group-sparsity regularization.

In Journal of X-ray science and technology

OBJECTIVE : In order to solve the blurred structural details and over-smoothing effects in sparse representation dictionary learning reconstruction algorithm, this study aims to test sparse angle CT reconstruction with weighted dictionary learning algorithm based on adaptive Group-Sparsity Regularization (AGSR-SART).

METHODS : First, a new similarity measure is defined in which Covariance is introduced into Euclidean distance, Non-local image patches are adaptively divided into groups of different sizes as the basic unit of sparse representation. Second, the weight factor of the regular constraint terms is designed through the residuals represented by the dictionary, so that the algorithm takes different smoothing effects on different regions of the image during the iterative process. The sparse reconstructed image is modified according to the difference between the estimated value and the intermediate image. Last, The SBI (Split Bregman Iteration) iterative algorithm is used to solve the objective function. An abdominal image, a pelvic image and a thoracic image are employed to evaluate performance of the proposed method.

RESULTS : In terms of quantitative evaluations, experimental results show that new algorithm yields PSNR of 48.20, the maximum SSIM of 99.06% and the minimum MAE of 0.0028.

CONCLUSIONS : This study demonstrates that new algorithm can better preserve structural details in reconstructed CT images. It eliminates the effect of excessive smoothing in sparse angle reconstruction, enhances the sparseness and non-local self-similarity of the image, and thus it is superior to several existing reconstruction algorithms.

Yang Tiejun, Tang Lu, Tang Qi, Li Lei

2021-Apr-02

Adaptive group-sparsity regularization, CT reconstruction, dictionary learning, spares angle

Surgery Surgery

Olfactory Phenotypes Differentiate Cognitively Unimpaired Seniors from Alzheimer's Disease and Mild Cognitive Impairment: A Combined Machine Learning and Traditional Statistical Approach.

In Journal of Alzheimer's disease : JAD

BACKGROUND : Olfactory dysfunction (OD) is an early symptom of Alzheimer's disease (AD). However, olfactory testing is not commonly performed to test OD in the setting of AD.

OBJECTIVE : This work investigates objective OD as a non-invasive biomarker for accurately classifying subjects as cognitively unimpaired (CU), mild cognitive impairment (MCI), and AD.

METHODS : Patients with MCI (n = 24) and AD (n = 24), and CU (n = 33) controls completed two objective tests of olfaction (Affordable, Rapid, Olfactory Measurement Array -AROMA; Sniffin' Sticks Screening 12 Test -SST12). Demographic and subjective sinonasal and olfaction symptom information was also obtained. Analyses utilized traditional statistics and machine learning to determine olfactory variables, and combinations of variables, of importance for differentiating normal and disease states.

RESULTS : Inability to correctly identify a scent after detection was a hallmark of MCI/AD. AROMA was superior to SST12 for differentiating MCI from AD. Performance on the clove scent was significantly different between all three groups. AROMA regression modeling yielded six scents with AUC of the ROC of 0.890 (p <  0.001). Random forest model machine learning algorithms considering AROMA olfactory data successfully predicted MCI versus AD disease state. Considering only AROMA data, machine learning algorithms were 87.5%accurate (95%CI 0.4735, 0.9968). Sensitivity and specificity were 100%and 75%, respectively with ROC of 0.875. When considering AROMA and subject demographic and subjective data, the AUC of the ROC increased to 0.9375.

CONCLUSION : OD differentiates CUs from those with MCI and AD and can accurately predict MCI versus AD. Leveraging OD data may meaningfully guide management and research decisions.

Li Jennifer, Bur Andres M, Villwock Mark R, Shankar Suraj, Palmer Gracie, Sykes Kevin J, Villwock Jennifer A

2021-Apr-05

Alzheimer’s disease, machine learning, mild cognitive impairment, olfaction, olfactory dysfunction

Surgery Surgery

Real-time prediction of upcoming respiratory events via machine learning using snoring sound signal.

In Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine

STUDY OBJECTIVES : The aim of the study was to inspect acoustic properties and sleep characteristics of pre-apneic snoring sound. The feasibility of forecasting upcoming respiratory events by snoring sound was also investigated.

METHODS : Participants with habitual snoring or heavy breathing sound during sleep were recruited consecutively. Polysomnography was conducted and snoring related breathing sound was recorded simultaneously. Acoustic features and sleep features were extracted from 30-second samples and a machine learning algorithm was used to establish two prediction models.

RESULTS : A total of 74 eligible participants were included. Model 1 tested by five-fold cross validation achieved the accuracy of 0.92 and area under the curve of 0.94 for respiratory event prediction. model 2 with acoustic features and sleep information tested by Leave-One-Out cross validation had the accuracy of 0.78 and area under the curve of 0.80. Sleep position was found to be the most important amongst all sleep features contributing to the performance.

CONCLUSIONS : Pre-apneic sound presented unique acoustic characteristics and snoring related breathing sound could be deployed as a real-time apneic event predictor. The model combined with sleep information served as a promising tool for an early warning system to forecast apneic events.

Wang Bochun, Yi Xuanyu, Gao Jiandong, Li Yanru, Xu Wen, Wu Ji, Han Demin

2021-Apr-12

acoustic features, early warning system, obstructive sleep apnea, real-time prediction, snoring related breathing sound

Cardiology Cardiology

Medical Education and Training Within Congenital Cardiology: Current Global Status and Future Directions in A Post COVID-19 World.

In Cardiology in the young

Despite enormous strides in our field with respect to patient care, there has been surprisingly limited dialogue on how to train and educate the next generation of congenital cardiologists. This paper reviews the current status of training and evolving developments in medical education pertinent to congenital cardiology. The adoption of competency-based medical education has been lauded as a robust framework for contemporary medical education over the last two decades. However, inconsistencies in frameworks across different jurisdictions remain, and bridging gaps between competency frameworks and clinical practice has proved challenging. Entrustable professional activities have been proposed as a solution but integration of such activities into busy clinical cardiology practices will present its own challenges. Consequently, this pivot toward a more structured approach to medical education necessitates the widespread availability of appropriately trained medical educationalists; a development that will better inform curriculum development, instructional design, and assessment. Differentiation between superficial and deep learning, the vital role of rich formative feedback and coaching, should guide our trainees to become self-regulated learners, capable of critical reasoning yet retaining an awareness of uncertainty and ambiguity. Furthermore, disruptive innovations such as 'technology enhanced learning' may be leveraged to improve education, especially for trainees from low- and middle-income countries. Each of these initiatives will require resources, widespread advocacy and raised awareness, and publication of supporting data, and so it is especially gratifying that Cardiology in The Young has fostered a progressive approach, agreeing to publish one or two articles in each journal issue in this domain.

McMahon Colin J, Tretter Justin T, Redington Andrew N, Bu’Lock Frances, Zühlke Liesl, Heying Ruth, Mattos Sandra, Kumar R Krishna, Jacobs Jeffrey P, Windram Jonathan D

2021-Apr-12

Adult Congenital Heart Disease, Congenital Cardiology, Congenital Heart Disease, Education, Paediatric Cardiology, Training

General General

Knowledge graphs and their applications in drug discovery.

In Expert opinion on drug discovery ; h5-index 34.0

INTRODUCTION : Knowledge graphs have proven to be promising systems of information storage and retrieval. Due to the recent explosion of heterogeneous multimodal data sources generated in the biomedical domain, and an industry shift toward a systems biology approach, knowledge graphs have emerged as attractive methods of data storage and hypothesis generation.

AREAS COVERED : In this review, the author summarizes the applications of knowledge graphs in drug discovery. They evaluate their utility; differentiating between academic exercises in graph theory, and useful tools to derive novel insights, highlighting target identification and drug repurposing as two areas showing particular promise. They provide a case study on COVID-19, summarizing the research that used knowledge graphs to identify repurposable drug candidates. They describe the dangers of degree and literature bias, and discuss mitigation strategies.

EXPERT OPINION : Whilst knowledge graphs and graph-based machine learning have certainly shown promise, they remain relatively immature technologies. Many popular link prediction algorithms fail to address strong biases in biomedical data, and only highlight biological associations, failing to model causal relationships in complex dynamic biological systems. These problems need to be addressed before knowledge graphs reach their true potential in drug discovery.

MacLean Finlay

2021-Apr-12

Biomedical knowledge graphs, drug repositioning, drug repurposing, graph machine learning, heterogeneous information networks, knowledge graph embedding, network embeddings, network medicine, network pharmacology

General General

PepFormer: End-to-End Transformer-Based Siamese Network to Predict and Enhance Peptide Detectability Based on Sequence Only.

In Analytical chemistry

The detectability of peptides is fundamentally important in shotgun proteomics experiments. At present, there are many computational methods to predict the detectability of peptides based on sequential composition or physicochemical properties, but they all have various shortcomings. Here, we present PepFormer, a novel end-to-end Siamese network coupled with a hybrid architecture of a Transformer and gated recurrent units that is able to predict the peptide detectability based on peptide sequences only. Specially, we, for the first time, use contrastive learning and construct a new loss function for model training, greatly improving the generalization ability of our predictive model. Comparative results demonstrate that our model performs significantly better than state-of-the-art methods on benchmark data sets in two species (Homo sapiens and Mus musculus). To make the model more interpretable, we further investigate the embedded representations of peptide sequences automatically learnt from our model, and the visualization results indicate that our model can efficiently capture high-latent discriminative information, improving the predictive performance. In addition, our model shows a strong ability of cross-species transfer learning and adaptability, demonstrating that it has great potential in robust prediction of peptides detectability on different species. The source code of our proposed method can be found via https://github.com/WLYLab/PepFormer.

Cheng Hao, Rao Bing, Liu Lei, Cui Lizhen, Xiao Guobao, Su Ran, Wei Leyi

2021-Apr-12

General General

Using machine learning to predict paediatric 30-day unplanned hospital readmissions: a case-control retrospective analysis of medical records, including written discharge documentation.

In Australian health review : a publication of the Australian Hospital Association

ObjectivesTo assess whether adding clinical information and written discharge documentation variables improves prediction of paediatric 30-day same-hospital unplanned readmission compared with predictions based on administrative information alone.MethodsA retrospective matched case-control study audited the medical records of patients discharged from a tertiary paediatric hospital in Western Australia (WA) between January 2010 and December 2014. A random selection of 470 patients with unplanned readmissions (out of 3330) were matched to 470 patients without readmissions based on age, sex, and principal diagnosis at the index admission. Prediction utility of three groups of variables (administrative, administrative and clinical, and administrative, clinical and written discharge documentation) were assessed using standard logistic regression and machine learning.ResultsInclusion of written discharge documentation variables significantly improved prediction of readmission compared with models that used only administrative and/or clinical variables in standard logistic regression analysis (χ2 17=29.4, P=0.03). Highest prediction accuracy was obtained using a gradient boosted tree model (C-statistic=0.654), followed closely by random forest and elastic net modelling approaches. Variables highlighted as important for prediction included patients' social history (legal custody or patient was under the care of the Department for Child Protection), languages spoken other than English, completeness of nursing admission and discharge planning documentation, and timing of issuing discharge summary.ConclusionsThe variables of significant social history, low English language proficiency, incomplete discharge documentation, and delay in issuing the discharge summary add value to prediction models.What is known about the topic?Despite written discharge documentation playing a critical role in the continuity of care for paediatric patients, limited research has examined its association with, and ability to predict, unplanned hospital readmissions. Machine learning approaches have been applied to various health conditions and demonstrated improved predictive accuracy. However, few published studies have used machine learning to predict paediatric readmissions.What does this paper add?This paper presents the findings of the first known study in Australia to assess and report that written discharge documentation and clinical information improves unplanned rehospitalisation prediction accuracy in a paediatric cohort compared with administrative data alone. It is also the first known published study to use machine learning for the prediction of paediatric same-hospital unplanned readmission in Australia. The results show improved predictive performance of the machine learning approach compared with standard logistic regression.What are the implications for practitioners?The identified social and written discharge documentation predictors could be translated into clinical practice through improved discharge planning and processes, to prevent paediatric 30-day all-cause same-hospital unplanned readmission. The predictors identified in this study include significant social history, low English language proficiency, incomplete discharge documentation, and delay in issuing the discharge summary.

Zhou Huaqiong, Albrecht Matthew A, Roberts Pamela A, Porter Paul, Della Philip R

2021-Apr-12

General General

Evaluating Simulations as Preparation for Health Crises like CoVID-19: Insights on Incorporating Simulation Exercises for Effective Response.

In International journal of disaster risk reduction : IJDRR

Today's health emergencies are increasingly complex due to factors such as globalization, urbanization and increased connectivity where people, goods and potential vectors of disease are constantly on the move. These factors amplify the threats to our health from infectious hazards, natural disasters, armed conflicts and other emergencies wherever they may occur. The current CoVID-19 pandemic has provided a clear demonstration of the fact that our ability to detect and predict the initial emergence of a novel human pathogen (for example, the spill-over of a virus from its animal reservoir to a human host), and our capacity to forecast the spread and transmission the pathogen in human society remains limited. Improving ways in which we prepare will enable a more rapid and effective response and enable proactive preparations (including exercising) to respond to any novel emerging infectious disease outbreaks. This study aims to explore the current state of pandemic preparedness exercising and provides an assessment of a number of case study exercises for health hazards against the key components of the WHO's Exercises for Pandemic Prepared Plans (EPPP) framework in order to gauge their usefulness in preparation for pandemics. The paper also examines past crises involving large-scale epidemics and pandemics and whether simulations took place to test health security capacities either in advance of the crisis based on risk assessments, strategy and plans or after the crisis in order to be better prepared should a similar scenario arise in the future. Exercises for animal and human diseases have been included to provide a "one health" perspective [1,2]. This article then goes on to examine approaches to simulation exercises relevant to prepare for health crisis involving a novel emergent pathogen like CoVID-19. This article demonstrates that while simulations are useful as part of a preparedness strategy, the key is to ensure that lessons from these simulations are learned and the associated changes made as soon as possible following any simulation in order to ensure that simulations are effective in bringing about changes in practice that will improve pandemic preparedness. Furthermore, Artificial Intelligence (AI) technologies could also be applied in preparing communities for outbreak detection, surveillance and containment, and be a useful tool for providing immersive environments for simulation exercises for pandemic preparedness and associated interventions which may be particularly useful at the strategic level. This article contributes to the limited literature in pandemic preparedness simulation exercising to deal with novel health crises, like CoVID-19. The analysis has also identified potential areas for further research or work on pandemic preparedness exercising.

Reddin Karen, Bang Henry, Miles Lee

2021-Apr-05

Emergency Exercise, Epidemic, Lessons learnt, Pandemic, Simulation

Radiology Radiology

Impact of coronary calcium score and lesion characteristics on the diagnostic performance of machine-learning-based computed tomography-derived fractional flow reserve.

In European heart journal cardiovascular Imaging

AIMS : To evaluate the impact of coronary artery calcium (CAC) score, minimal lumen area (MLA), and length of coronary artery stenosis on the diagnostic performance of the machine-learning-based computed tomography-derived fractional flow reserve (ML-FFR).

METHODS AND RESULTS : In 471 patients with coronary artery disease, computed tomography angiography (CTA) and invasive coronary angiography were performed with fractional flow reserve (FFR) in 557 lesions at a single centre. Diagnostic performances of ML-FFR, computational fluid dynamics-based CT-FFR (CFD-FFR), MLA, quantitative coronary angiography (QCA), and visual stenosis grading were evaluated using invasive FFR as a reference standard. Diagnostic performances were analysed according to lesion characteristics including the MLA, length of stenosis, CAC score, and stenosis degree. ML-FFR was obtained by automated feature selection and model building from quantitative CTA. A total of 272 lesions showed significant ischaemia, defined by invasive FFR ≤0.80. There was a significant correlation between CFD-FFR and ML-FFR (r = 0.99, P < 0.001). ML-FFR showed moderate sensitivity and specificity in the per-patient analysis. Diagnostic performances of CFD-FFR and ML-FFR did not decline in patients with high CAC scores (CAC > 400). Sensitivities of CFD-FFR and ML-FFR showed a downward trend along with the increase in lesion length and decrease in MLA. The area under the curve (AUC) of ML-FFR (0.73) was higher than those of QCA and visual grading (AUC = 0.65 for both, P < 0.001) and comparable to those of MLA (AUC = 0.71, P = 0.21) and CFD-FFR (AUC = 0.73, P = 0.86).

CONCLUSION : ML-FFR showed comparable results to MLA and CFD-FFR for the prediction of lesion-specific ischaemia. Specificities and accuracies of CFD-FFR and ML-FFR decreased with smaller MLA and long lesion length.

Koo Hyun Jung, Kang Joon-Won, Kang Soo-Jin, Kweon Jihoon, Lee June-Goo, Ahn Jung-Min, Park Duk-Woo, Lee Seung Whan, Lee Cheol Whan, Park Seong-Wook, Park Seung-Jung, Kim Young-Hak, Yang Dong Hyun

2021-Apr-11

CT fractional flow reserve, coronary artery disease, coronary calcium, machine-learning

General General

Machine learning approach to gene essentiality prediction: a review.

In Briefings in bioinformatics

  : Essential genes are critical for the growth and survival of any organism. The machine learning approach complements the experimental methods to minimize the resources required for essentiality assays. Previous studies revealed the need to discover relevant features that significantly classify essential genes, improve on the generalizability of prediction models across organisms, and construct a robust gold standard as the class label for the train data to enhance prediction. Findings also show that a significant limitation of the machine learning approach is predicting conditionally essential genes. The essentiality status of a gene can change due to a specific condition of the organism. This review examines various methods applied to essential gene prediction task, their strengths, limitations and the factors responsible for effective computational prediction of essential genes. We discussed categories of features and how they contribute to the classification performance of essentiality prediction models. Five categories of features, namely, gene sequence, protein sequence, network topology, homology and gene ontology-based features, were generated for Caenorhabditis elegans to perform a comparative analysis of their essentiality prediction capacity. Gene ontology-based feature category outperformed other categories of features majorly due to its high correlation with the genes' biological functions. However, the topology feature category provided the highest discriminatory power making it more suitable for essentiality prediction. The major limiting factor of machine learning to predict essential genes conditionality is the unavailability of labeled data for interest conditions that can train a classifier. Therefore, cooperative machine learning could further exploit models that can perform well in conditional essentiality predictions.

SHORT ABSTRACT : Identification of essential genes is imperative because it provides an understanding of the core structure and function, accelerating drug targets' discovery, among other functions. Recent studies have applied machine learning to complement the experimental identification of essential genes. However, several factors are limiting the performance of machine learning approaches. This review aims to present the standard procedure and resources available for predicting essential genes in organisms, and also highlight the factors responsible for the current limitation in using machine learning for conditional gene essentiality prediction. The choice of features and ML technique was identified as an important factor to predict essential genes effectively.

Aromolaran Olufemi, Aromolaran Damilare, Isewon Itunuoluwa, Oyelade Jelili

2021-Apr-10

conditional essentiality, conditionally essential genes, essential genes, essential proteins, feature selection, supervised learning

General General

Addressing cervical cancer screening disparities through advances in artificial intelligence and nanotechnologies for cellular profiling.

In Biophysics reviews

Almost all cases of cervical cancer are caused by the human papilloma virus (HPV). Detection of pre-cancerous cervical changes provides a window of opportunity for cure of an otherwise lethal disease when metastatic. With a greater understanding of the biology and natural course of high-risk HPV infections, screening methods have shifted beyond subjective Pap smears toward more sophisticated and objective tactics. This has led to a substantial growth in the breadth and depth of HPV-based cervical cancer screening tests, especially in developed countries without constrained resources. Many low- and middle-income countries (LMICs) have less access to advanced laboratories and healthcare resources, so new point-of-care (POC) technologies have been developed to provide test results in real time, improve the efficiency of techniques, and increase screening adoption. In this Review, we will discuss how novel decentralized screening technologies and computational strategies improve upon traditional methods and how their realized promise could further democratize cervical cancer screening and promote greater disease prevention.

Yang Zhenzhong, Francisco Jack, Reese Alexandra S, Spriggs David R, Im Hyungsoon, Castro Cesar M

2021-Mar

General General

Object discrimination performance and dynamics evaluated by inferotemporal cell population activity.

In IBRO neuroscience reports

We have previously reported an increase in response tolerance of inferotemporal cells around trained views. However, an inferotemporal cell usually displays different response patterns in an initial response phase immediately after the stimulus onset and in a late phase from approximately 260 ms after stimulus onset. This study aimed to understand the difference between the two time periods and their involvement in the view-invariant object recognition. Responses to object images with and without prior experience of object discrimination across views, recorded by microelectrodes, were pooled together from our previous experiments. With a machine learning algorithm, we trained to build classifiers for object discrimination. In the early phase, the performance of classifiers created based on data of responses to the object images with prior training of object discrimination across views did not significantly differ from that based on data of responses to the object images without prior experience of object discrimination across views. However, the performance was significantly better in the late phase. Furthermore, compared to the preferred stimulus image in the early phase, we found 2/3 of cells changed their preference in the late phase. For object images with prior experience of training with object discrimination across views, a significant higher percentage of cells responded in the late phase to the same objects as in the early phase, but under different views. The results demonstrate the dynamics of selectivity changes and suggest the involvement of the late phase in the view-invariant object recognition rather than that of the early phase.

Wang Ridey H, Dai Lulin, Okamura Jun-Ya, Fuchida Takayasu, Wang Gang

2021-Jun

Discrimination, Inferotemporal cortex, Learning, Monkey, Object recognition, View-invariance

Radiology Radiology

Integrating Eye Tracking and Speech Recognition Accurately Annotates MR Brain Images for Deep Learning: Proof of Principle.

In Radiology. Artificial intelligence

Purpose : To generate and assess an algorithm combining eye tracking and speech recognition to extract brain lesion location labels automatically for deep learning (DL).

Materials and Methods : In this retrospective study, 700 two-dimensional brain tumor MRI scans from the Brain Tumor Segmentation database were clinically interpreted. For each image, a single radiologist dictated a standard phrase describing the lesion into a microphone, simulating clinical interpretation. Eye-tracking data were recorded simultaneously. Using speech recognition, gaze points corresponding to each lesion were obtained. Lesion locations were used to train a keypoint detection convolutional neural network to find new lesions. A network was trained to localize lesions for an independent test set of 85 images. The statistical measure to evaluate our method was percent accuracy.

Results : Eye tracking with speech recognition was 92% accurate in labeling lesion locations from the training dataset, thereby demonstrating that fully simulated interpretation can yield reliable tumor location labels. These labels became those that were used to train the DL network. The detection network trained on these labels predicted lesion location of a separate testing set with 85% accuracy.

Conclusion : The DL network was able to locate brain tumors on the basis of training data that were labeled automatically from simulated clinical image interpretation.© RSNA, 2020.

Stember Joseph N, Celik Haydar, Gutman David, Swinburne Nathaniel, Young Robert, Eskreis-Winkler Sarah, Holodny Andrei, Jambawalikar Sachin, Wood Bradford J, Chang Peter D, Krupinski Elizabeth, Bagci Ulas

2021-Jan

Public Health Public Health

Impact of screening on the prevalence and incidence of Mycoplasma genitalium and its macrolide resistance in men who have sex with men living in Australia: A mathematical model.

In EClinicalMedicine

Background : Mycoplasma genitalium (MG) causes a sexually transmitted infection (STI) with a rising rate of antimicrobial resistance. Currently, guidelines do not recommend screening asymptomatic men who have sex with men (MSM). We developed a mathematical model of MG transmission to examine the impact of various screening strategies on the incidence and prevalence of MG among MSM attending a sexual health clinic.

Methods : A compartmental mathematical model of MG transmission among MSM was constructed and calibrated using data from the Melbourne Sexual Health center, where resistance-guided therapy provides high treatment effectiveness (92-95%). The model stratified men by symptom status, sexual risk behaviours and whether or not they had MG with macrolide resistance. We simulated the impact on endemic steady-state MG prevalence and incidence of the following screening scenarios, namely screening: 1) no MSM; 2) only symptomatic MSM (the current recommendation); 3) all symptomatic and high-risk asymptomatic MSM; and 4) all MSM. Our base case analysis assumed a treatment effectiveness of 92-95% using resistance-guided therapy. We also examined the impact of treatment effectiveness (i.e. the proportion of detected MG that were cured) and screening coverage (i.e. testing rate) on MG prevalence.

Findings : The model predicts that the overall endemic MG prevalence is 9.1% (95% CI: 7.9-10.0) in the current situation where screening is only offered to symptomatic MSM (base-case). This would increase to 11·4% (95% confidence intervals (CI): 10.2-13.7) if no MSM are offered screening, but would decrease to 7.3% (95% CI: 5.7-8.4) if all symptomatic and high-risk asymptomatic MSM were offered screening and 6.4% (95% CI: 4.7-7·7) if all MSM were offered screening. Increasing coverage of MSM screening strategies shows a similar effect on decreasing endemic MG incidence. When evaluating the simultaneous impact of treatment effectiveness and screening coverage, we found that offering screening to more MSM may reduce the overall prevalence but leads to a higher proportion of macrolide-resistant MG, particularly when using treatment regimens with lower effectiveness.

Interpretation : Based on the available treatment options, offering screening for MG to other MSM (beyond the currently recommended group of symptomatic MSM) could slightly reduce the prevalence and incidence of MG. However, further increasing screening coverage must be weighed against the impact of lower treatment effectiveness (i.e. when not using resistance-guided therapy), increasing the selection of macrolide resistance, and other negative consequences related to AMR and management (e.g. unnecessary psychological morbidity from infections that do not need treatment).

Ong Jason J, Ruan Luanqi, Lim Aaron G, Bradshaw Catriona S, Taylor-Robinson David, Unemo Magnus, Horner Patrick J, Vickerman Peter, Zhang Lei

2021-Mar

Public Health Public Health

Nanoindentation for Monitoring the Time-Variant Mechanical Strength of Drug-Loaded Collagen Hydrogel Regulated by Hydroxyapatite Nanoparticles.

In ACS omega

Hydroxyapatite nanoparticle-complexed collagen (HAP/Col) hydrogels have been widely used in biomedical applications as a scaffold for controlled drug release (DR). The time-variant mechanical properties (Young's modulus, E) of HAP/Col hydrogels are highly relevant to the precise and efficient control of DR. However, the correlation between the DR and the E of hydrogels remains unclear because of the lack of a nondestructive and continuous measuring system. To reveal the correlations, herein, we investigate the time-variant behavior of E for HAP/Col hydrogels during 28 days using the atomic force microscopy (AFM) nanoindentation technique. The initial E of hydrogels was controlled from 200 to 9000 Pa by the addition of HAPs. Subsequently, we analyzed the relationship between the DR of the hydrogels and the changes in their mechanical properties (ΔE) during hydrogel degradation. Interestingly, the higher the initial E value of HAP/Col hydrogels is, the higher is the rate of hydrogel degradation over time. However, the DR of hydrogels with higher initial E appeared to be significantly delayed by up to 40% at a maximum. The results indicate that adding an appropriate amount of HAPs into hydrogels plays a crucial role in determining the initial E and their degradation rate, which can contribute to the properties that prolong DR. Our findings may provide insights into designing hydrogels for biomedical applications such as bone regeneration and drug-delivery systems.

Jung Hyo Gi, Lee Dongtak, Lee Sang Won, Kim Insu, Kim Yonghwan, Jang Jae Won, Lee Jeong Hoon, Lee Gyudo, Yoon Dae Sung

2021-Apr-06

General General

How does "A Bit of Everything American" state feel about COVID-19? A quantitative Twitter analysis of the pandemic in Ohio.

In Journal of computational social science

COVID-19 has proven itself to be one of the most important events of the last two centuries. This defining moment in our lives has created wide-ranging discussions in many segments of our societies, both politically and socially. Over time, the pandemic has been associated with many social and political topics, as well as sentiments and emotions. Twitter offers a platform to understand these effects. The primary objective of this study is to capture the awareness and sentiment about COVID-19-related issues and to find how they relate to the number of cases and deaths in a representative region of the United States. The study uses a unique dataset consisting of over 46 million tweets from over 91,000 users in 88 counties of the state of Ohio, a state-of-the-art deep learning model to measure and detect awareness and emotions. The data collected is analyzed using OLS regression and System-GMM dynamic panel. Findings indicate that the pandemic has drastically changed the perception of the Republican party in the society. Individual motivations are strongly influenced by ideological choices and this ultimately affects individual pandemic-related outcomes. The paper contributes to the literature by expanding the knowledge on COVID-19 (i), offering a representative result for the United States by focusing on an "average" state like Ohio (ii), and incorporating the sentiment and emotions into the calculation of awareness (iii).

Caliskan Cantay

2021-Apr-05

Awareness, COVID-19, Emotion classification, Twitter

General General

A review of machine learning experiments in equity investment decision-making: why most published research findings do not live up to their promise in real life.

In International journal of data science and analytics

The numerical nature of financial markets makes market forecasting and portfolio construction a good use case for machine learning (ML), a branch of artificial intelligence (AI). Over the past two decades, a number of academics worldwide (mostly from the field of computer science) produced a sizeable body of experimental research. Many publications claim highly accurate forecasts or highly profitable investment strategies. At the same time, the picture of real-world AI-driven investments is ambiguous and conspicuously lacking in high-profile success cases (while it is not lacking in high-profile failures). We conducted a literature review of 27 academic experiments spanning over two decades and contrasted them with real-life examples of machine learning-driven funds to try to explain this apparent contradiction. The specific contributions our article will make are as follows: (1) A comprehensive, thematic review (quantitative and qualitative) of multiple academic experiments from the investment management perspective. (2) A critical evaluation of running multiple versions of the same models in parallel and disclosing the best-performing ones only ("cherry-picking"). (3) Recommendations on how to approach future experiments so that their outcomes are unambiguously measurable and useful for the investment industry. (4) An in-depth comparison of real-life cases of ML-driven funds versus academic experiments. We will discuss whether present-day ML algorithms could make feasible and profitable investments in the equity markets.

Buczynski Wojtek, Cuzzolin Fabio, Sahakian Barbara

2021-Apr-05

Artificial Intelligence, Backtest overfit, Investing, Investment decision-making, Investment management, Investments, Machine Learning

General General

COVID-19 prediction using LSTM Algorithm: GCC Case Study.

In Informatics in medicine unlocked

Coronavirus-19 (COVID-19) is the black swan of 2020. Still, the human response to restrain the virus is also creating massive ripples through different systems, such as health, economy, education, and tourism. This paper focuses on research and applying Artificial Intelligence (AI) algorithms to predict COVID-19 propagation using the available time-series data and study the effect of the quality of life, the number of tests performed, and the awareness of citizens on the virus in the Gulf Cooperation Council (GCC) countries at the Gulf area. So we focused on cases in the Kingdom of Saudi Arabia (KSA), United Arab of Emirates (UAE), Kuwait, Bahrain, Oman, and Qatar. For this aim, we accessed the time-series real-datasets collected from Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). The timeline of our data is from 22 January 2020 to 25 January 2021. We have implemented the proposed model based on Long Short-Term Memory (LSTM) with ten hidden units (neurons) to predict COVID-19 confirmed and death cases. From the experimental results, we confirmed that KSA and Qatar would take the most extended period to recover from the COVID-19 virus, and the situation will be controllable in the second half of March 2021 in UAE, Kuwait, Oman, and Bahrain. Also, we calculated the root mean square error (RMSE) between the actual and predicted values of each country for confirmed and death cases, and we found that the best values for both confirmed and death cases are 320.79 and 1.84, respectively, and both are related to Bahrain. While the worst values are 1768.35 and 21.78, respectively, and both are related to KSA. On the other hand, we also calculated the mean absolute relative errors (MARE) between the actual and predicted values of each country for confirmed and death cases, and we found that the best values for both confirmed and deaths cases are 37.76 and 0.30, and these are related to Kuwait and Qatar respectively. While the worst values are 71.45 and 1.33, respectively, and both are related to KSA.

Ghany Kareem Kamal A, Zawbaa Hossam M, Sabri Heba M

2021-Apr-06

Artificial Intelligence, COVID-19, Deep Learning, LSTM, Prediction

Pathology Pathology

Special Issue on Digital Pathology, Tissue Image Analysis, Artificial Intelligence, and Machine Learning: Approximation of the Effect of Novel Technologies on Toxicologic Pathology.

In Toxicologic pathology

For decades, it has been postulated that digital pathology is the future. By now it is safe to say that we are living that future. Digital pathology has expanded into all aspects of pathology, including human diagnostic pathology, veterinary diagnostics, research, drug development, regulatory toxicologic pathology primary reads, and peer review. Digital tissue image analysis has enabled users to extract quantitative and complex data from digitized whole-slide images. The following editorial provides an overview of the content of this special issue of Toxicologic Pathology to highlight the range of key topics that are included in this compilation. In addition, the editors provide a commentary on important current aspects to consider in this space, such as accessibility of publication content to the machine learning-novice pathologist, the importance of adequate test set selection, and allowing for data reproducibility.

Aeffner Famke, Sing Tobias, Turner Oliver C

2021-Apr-12

artificial intelligence, digital pathology, machine learning, tissue image analysis

General General

K-SEIR-Sim: A simple customized software for simulating the spread of infectious diseases.

In Computational and structural biotechnology journal

Infectious disease is a great enemy of humankind. The ravages of COVID-19 are leading to profound crises across the world. There is an urgent requirement for analyzing the current pandemic situation, predicting trends over time, and assessing the effectiveness of containment measures. Thus, numerous statistical models, primarily based on the susceptible-exposed-infected-recovered or removed (SEIR) model, have been established. However, these models are highly technical, which are difficult for the public and governing bodies to understand and use. To address this issue, we developed a simple operating software based on our improved K-SEIR model termed as the kernelkernel SEIR simulator (K-SEIR-Sim). This software includes natural propagation parameters, containment measure parameters, and certain characteristic parameters that can deduce the effects of natural propagation and containment measures. Further, the applicability of the proposed software was demonstrated using the example of the COVID-19 outbreak in the United States and the city of Wuhan, China. Operating results verified the potency of the proposed software in evaluating the epidemic situation and human intervention during COVID-19. Importantly, the software can perform real-time, backward-looking, and forward-looking analysis by functioning in data-driven and model-driven ways. All of them have considerable practical values in their applications according to the actual needs of personal use. Conclusively, K-SEIR-Sim is the first simple customized operating software that is highly valuable for the global fight against COVID-19 and other infectious diseases.

Wang Hongzhi, Miao Zhiying, Zhang Chaobao, Wei Xiaona, Li Xiangqi

2021-Apr-07

2019-nCoV, COVID-19, SEIR model, artificial intelligence, python, simulation analysis, software

General General

Machine Learning Approaches in COVID-19 Diagnosis, Mortality, and Severity Risk Prediction: A Review.

In Informatics in medicine unlocked

The existence of widespread COVID-19 infections has prompted worldwide efforts to control and manage the virus, and hopefully curb it completely. One important line of research is the use of machine learning (ML) to understand and fight COVID-19. This is currently an active research field. Although there are already many surveys in the literature, there is a need to keep up with the rapidly growing number of publications on COVID-19-related applications of ML. This paper presents a review of recent reports on ML algorithms used in relation to COVID-19. We focus on the potential of ML for two main applications: diagnosis of COVID-19 and prediction of mortality risk and severity, using readily available clinical and laboratory data. Aspects related to algorithm types, training data sets, and feature selection are discussed. As we cover work published between January 2020 and January 2021, a few key points have come to light. The bulk of the machine learning algorithms used in these two applications are supervised learning algorithms. The established models are yet to be used in real-world implementations, and much of the associated research is experimental. The diagnostic and prognostic features discovered by ML models are consistent with results presented in the medical literature. A limitation of the existing applications is the use of imbalanced data sets that are prone to selection bias.

Alballa Norah, Al-Turaiki Isra

2021-Apr-03

COVID-19, Machine learning, artificial intelligence, diagnosis, feature selection, prognosis

General General

Recent advances in convex probe endobronchial ultrasound: a narrative review.

In Annals of translational medicine

Convex probe endobronchial ultrasound (CP-EBUS) has been widely used in the lymph node staging and restaging of lung tumors and the diagnosis of mediastinal diseases. Recent years have seen continuous progress in this technology. For diagnosis, elastography technology can preliminarily distinguish between benign and malignant lesions, so that reduce the number of punctures. CP-EBUS can also be used as an endoscopic ultrasound (EUS) to guide needle aspirations of liver lesions, retroperitoneal lymph nodes and left adrenal gland (LAG) lesions sometimes. Some advances help diagnosing more accurately and effectively, such as the intranodal forceps biopsy (IFB), the new type of 22G needle, the rapid on-site evaluation (ROSE) and the cancer gene methylation, etc. In addition, special advances are being made in diagnosis using artificial intelligence (AI). For treatment, CP-EBUS has yielded novel research results when applied to transbronchial needle injection (TBNI) and radioactive seed implantation in clinical cases, and blocking of the cardiac plexus in animal studies. The next-generation CP-EBUS is also ready for use in the clinic and the technology will be improving continuously. Through this review, we hope to educate clinicians on the latest uses of CP-EBUS and open up further research ideas for readers interested in this technology.

Wu Jian, Wu Cen, Zhou Chuming, Zheng Wei, Li Peng

2021-Mar

Endobronchial ultrasound (EBUS), elastography, intranodal forceps biopsy (IFB), rapid on-site evaluation (ROSE), transbronchial needle aspiration (TBNA), transbronchial needle injection (TBNI)

Ophthalmology Ophthalmology

An artificial intelligence platform for the diagnosis and surgical planning of strabismus using corneal light-reflection photos.

In Annals of translational medicine

Background : Strabismus affects approximately 0.8-6.8% of the world's population and can lead to abnormal visual function. However, Strabismus screening and measurement are laborious and require professional training. This study aimed to develop an artificial intelligence (AI) platform based on corneal light-reflection photos for the diagnosis of strabismus and to provide preoperative advice.

Methods : An AI platform consisting of three deep learning (DL) systems for strabismus diagnosis, angle evaluation, and operation plannings based on corneal light-reflection photos was trained and retrospectively validated using a retrospective development data set obtained between Jan 1, 2014, and Dec 31, 2018. Corneal light-reflection photos were collected to train the DL systems for strabismus screening and deviation evaluations in the horizontal strabismus while concatenated images (each composed of two photos representing different gaze states) were procured to train the DL system for operative advice regarding exotropia. The AI platform was further prospectively validated using a prospective development data set captured between Sep 1, 2019, and Jun 10, 2020.

Results : In total, 5,797 and 571 photos were included in the retrospective and prospectively development data sets, respectively. In the retrospective test sets, the screening system detected strabismus with a sensitivity of 99.1% [95% confidence interval (95% CI), 98.1-99.7%], a specificity of 98.3% (95% CI, 94.6-99.5%), and an AUC of 0.998 (95% CI, 0.993-1.000, P<0.001). Compared to the angle measured by the perimeter arc, the deviation evaluation system achieved a level of accuracy of ±6.6º (95% LoA) with a small bias of 1.0º. Compared to the real design, the operation advice system provided advice regarding the target angle within ±5.5º (95% LoA). Regarding strabismus in the prospective test set, the AUC was 0.980. The platform achieved a level of accuracy of ±7.0º (95% LoA) in the deviation evaluation and ±6.1º (95% LoA) in the target angle suggestion.

Conclusions : The AI platform based on corneal light-reflection photos can provide reliable references for strabismus diagnosis, angle evaluation, and surgical plannings.

Mao Keli, Yang Yahan, Guo Chong, Zhu Yi, Chen Chuan, Chen Jingchang, Liu Li, Chen Lifei, Mo Zijun, Lin Bingsen, Zhang Xinliang, Li Sijin, Lin Xiaoming, Lin Haotian

2021-Mar

Artificial intelligence (AI), corneal light-reflection photos, machine learning, strabismus

General General

From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation.

In Frontiers in robotics and AI

The exponentially increasing advances in robotics and machine learning are facilitating the transition of robots from being confined to controlled industrial spaces to performing novel everyday tasks in domestic and urban environments. In order to make the presence of robots safe as well as comfortable for humans, and to facilitate their acceptance in public environments, they are often equipped with social abilities for navigation and interaction. Socially compliant robot navigation is increasingly being learned from human observations or demonstrations. We argue that these techniques that typically aim to mimic human behavior do not guarantee fair behavior. As a consequence, social navigation models can replicate, promote, and amplify societal unfairness, such as discrimination and segregation. In this work, we investigate a framework for diminishing bias in social robot navigation models so that robots are equipped with the capability to plan as well as adapt their paths based on both physical and social demands. Our proposed framework consists of two components: learning which incorporates social context into the learning process to account for safety and comfort, and relearning to detect and correct potentially harmful outcomes before the onset. We provide both technological and societal analysis using three diverse case studies in different social scenarios of interaction. Moreover, we present ethical implications of deploying robots in social environments and propose potential solutions. Through this study, we highlight the importance and advocate for fairness in human-robot interactions in order to promote more equitable social relationships, roles, and dynamics and consequently positively influence our society.

Hurtado Juana Valeria, Londoño Laura, Valada Abhinav

2021

algorithmic fairness, ethics, fairness-aware learning, responsible innovation, robot learning, social robot navigation

General General

Covid, AI, and Robotics-A Neurologist's Perspective.

In Frontiers in robotics and AI

Two of the major revolutions of this century are the Artificial Intelligence and Robotics. These technologies are penetrating through all disciplines and faculties at a very rapid pace. The application of these technologies in medicine, specifically in the context of Covid 19 is paramount. This article briefly reviews the commonly applied protocols in the Health Care System and provides a perspective in improving the efficiency and effectiveness of the current system. This article is not meant to provide a literature review of the current technology but rather provides a personal perspective of the author regarding what could happen in the ideal situation.

Ahmed Syed Nizamuddin

2021

AI, COVID-19, artificial intelligence, neurologist, neurology, robotics, telemedicine

General General

Registered Trials on Artificial Intelligence Conducted in Emergency Department and Intensive Care Unit: A Cross-Sectional Study on ClinicalTrials.gov.

In Frontiers in medicine

Objective: Clinical trials contribute to the development of clinical practice. However, little is known about the current status of trials on artificial intelligence (AI) conducted in emergency department and intensive care unit. The objective of the study was to provide a comprehensive analysis of registered trials in such field based on ClinicalTrials.gov. Methods: Registered trials on AI conducted in emergency department and intensive care unit were searched on ClinicalTrials.gov up to 12th January 2021. The characteristics were analyzed using SPSS21.0 software. Results: A total of 146 registered trials were identified, including 61 in emergency department and 85 in intensive care unit. They were registered from 2004 to 2021. Regarding locations, 58 were conducted in Europe, 58 in America, 9 in Asia, 4 in Australia, and 17 did not report locations. The enrollment of participants was from 0 to 18,000,000, with a median of 233. Universities were the primary sponsors, which accounted for 43.15%, followed by hospitals (35.62%), and industries/companies (9.59%). Regarding study designs, 85 trials were interventional trials, while 61 were observational trials. Of the 85 interventional trials, 15.29% were for diagnosis and 38.82% for treatment; of the 84 observational trials, 42 were prospective, 14 were retrospective, 2 were cross-sectional, 2 did not report clear information and 1 was unknown. Regarding the trials' results, 69 trials had been completed, while only 10 had available results on ClinicalTrials.gov. Conclusions: Our study suggest that more AI trials are needed in emergency department and intensive care unit and sponsors are encouraged to report the results.

Liu Guina, Li Nian, Chen Lingmin, Yang Yi, Zhang Yonggang

2021

ClinicalTrials.gov, artificial intelligence, cross-sectional, emergency department, intensive care unit, trial

General General

Principal Component Analysis of the Running Ground Reaction Forces With Different Speeds.

In Frontiers in bioengineering and biotechnology

Ground reaction force (GRF) is a key metric in biomechanical research, including parameters of loading rate (LR), first impact peak, second impact peak, and transient between first and second impact peaks in heel strike runners. The GRFs vary over time during stance. This study was aimed to investigate the variances of GRFs in rearfoot striking runners across incremental speeds. Thirty female and male runners joined the running tests on the instrumented treadmill with speeds of 2.7, 3.0, 3.3, and 3.7 m/s. The discrete parameters of vertical average loading rate in the current study are consistent with the literature findings. The principal component analysis was modeled to investigate the main variances (95%) in the GRFs over stance. The females varied in the magnitude of braking and propulsive forces (PC1, 84.93%), whereas the male runners varied in the timing of propulsion (PC1, 53.38%). The female runners dominantly varied in the transient between the first and second peaks of vertical GRF (PC1, 36.52%) and LR (PC2, 33.76%), whereas the males variated in the LR and second peak of vertical GRF (PC1, 78.69%). Knowledge reported in the current study suggested the difference of the magnitude and patterns of GRF between male and female runners across different speeds. These findings may have implications for the prevention of sex-specific running-related injuries and could be integrated with wearable signals for the in-field prediction and estimation of impact loadings and GRFs.

Yu Lin, Mei Qichang, Xiang Liangliang, Liu Wei, Mohamad Nur Ikhwan, István Bíró, Fernandez Justin, Gu Yaodong

2021

gait biomechanics, gender difference, ground reaction force, machine learning, running velocity

General General

Investigation of a Novel Deep Learning-Based Computed Tomography Perfusion Mapping Framework for Functional Lung Avoidance Radiotherapy.

In Frontiers in oncology

Functional lung avoidance radiation therapy aims to minimize dose delivery to the normal lung tissue while favoring dose deposition in the defective lung tissue based on the regional function information. However, the clinical acquisition of pulmonary functional images is resource-demanding, inconvenient, and technically challenging. This study aims to investigate the deep learning-based lung functional image synthesis from the CT domain. Forty-two pulmonary macro-aggregated albumin SPECT/CT perfusion scans were retrospectively collected from the hospital. A deep learning-based framework (including image preparation, image processing, and proposed convolutional neural network) was adopted to extract features from 3D CT images and synthesize perfusion as estimations of regional lung function. Ablation experiments were performed to assess the effects of each framework component by removing each element of the framework and analyzing the testing performances. Major results showed that the removal of the CT contrast enhancement component in the image processing resulted in the largest drop in framework performance, compared to the optimal performance (~12%). In the CNN part, all the three components (residual module, ROI attention, and skip attention) were approximately equally important to the framework performance; removing one of them resulted in a 3-5% decline in performance. The proposed CNN improved ~4% overall performance and ~350% computational efficiency, compared to the U-Net model. The deep convolutional neural network, in conjunction with image processing for feature enhancement, is capable of feature extraction from CT images for pulmonary perfusion synthesis. In the proposed framework, image processing, especially CT contrast enhancement, plays a crucial role in the perfusion synthesis. This CTPM framework provides insights for relevant research studies in the future and enables other researchers to leverage for the development of optimized CNN models for functional lung avoidance radiation therapy.

Ren Ge, Lam Sai-Kit, Zhang Jiang, Xiao Haonan, Cheung Andy Lai-Yin, Ho Wai-Yin, Qin Jing, Cai Jing

2021

CT based image analysis, deep learning, functional lung avoidance radiation therapy, lung function imaging, perfusion imaging, perfusion synthesis

oncology Oncology

Deep Learning for Automatic Segmentation of Oral and Oropharyngeal Cancer Using Narrow Band Imaging: Preliminary Experience in a Clinical Perspective.

In Frontiers in oncology

Introduction : Fully convoluted neural networks (FCNN) applied to video-analysis are of particular interest in the field of head and neck oncology, given that endoscopic examination is a crucial step in diagnosis, staging, and follow-up of patients affected by upper aero-digestive tract cancers. The aim of this study was to test FCNN-based methods for semantic segmentation of squamous cell carcinoma (SCC) of the oral cavity (OC) and oropharynx (OP).

Materials and Methods : Two datasets were retrieved from the institutional registry of a tertiary academic hospital analyzing 34 and 45 NBI endoscopic videos of OC and OP lesions, respectively. The dataset referring to the OC was composed of 110 frames, while 116 frames composed the OP dataset. Three FCNNs (U-Net, U-Net 3, and ResNet) were investigated to segment the neoplastic images. FCNNs performance was evaluated for each tested network and compared to the gold standard, represented by the manual annotation performed by expert clinicians.

Results : For FCNN-based segmentation of the OC dataset, the best results in terms of Dice Similarity Coefficient (Dsc) were achieved by ResNet with 5(×2) blocks and 16 filters, with a median value of 0.6559. In FCNN-based segmentation for the OP dataset, the best results in terms of Dsc were achieved by ResNet with 4(×2) blocks and 16 filters, with a median value of 0.7603. All tested FCNNs presented very high values of variance, leading to very low values of minima for all metrics evaluated.

Conclusions : FCNNs have promising potential in the analysis and segmentation of OC and OP video-endoscopic images. All tested FCNN architectures demonstrated satisfying outcomes in terms of diagnostic accuracy. The inference time of the processing networks were particularly short, ranging between 14 and 115 ms, thus showing the possibility for real-time application.

Paderno Alberto, Piazza Cesare, Del Bon Francesca, Lancini Davide, Tanagli Stefano, Deganello Alberto, Peretti Giorgio, De Momi Elena, Patrini Ilaria, Ruperti Michela, Mattos Leonardo S, Moccia Sara

2021

deep learning, machine learning, narrow band imaging, neural network, oral cancer, oropharyngeal cancer, segmentation

Radiology Radiology

Radiomics Model Based on MR Images to Discriminate Pancreatic Ductal Adenocarcinoma and Mass-Forming Chronic Pancreatitis Lesions.

In Frontiers in oncology

Background : It is difficult to identify pancreatic ductal adenocarcinoma (PDAC) and mass-forming chronic pancreatitis (MFCP) lesions through conventional CT or MR examination. As an innovative image analysis method, radiomics may possess potential clinical value in identifying PDAC and MFCP. To develop and validate radiomics models derived from multiparametric MRI to distinguish pancreatic ductal adenocarcinoma (PDAC) and mass-forming chronic pancreatitis (MFCP) lesions.

Methods : This retrospective study included 119 patients from two independent institutions. Patients from one institution were used as the training cohort (51 patients with PDAC and 13 patients with MFCP), and patients from the other institution were used as the testing cohort (45 patients with PDAC and 10 patients with MFCP). All the patients had pathologically confirmed results, and preoperative MRI was performed. Four feature sets were extracted from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and the artery (A) and portal (P) phases of dynamic contrast-enhanced MRI, and the corresponding radiomics models were established. Several clinical characteristics were used to discriminate PDAC and MFCP lesions, and clinical model was established. The results of radiologists' evaluation were compared with pathology and radiomics models. Univariate analysis and the least absolute shrinkage and selection operator algorithm were performed for feature selection, and a support vector machine was used for classification. The receiver operating characteristic (ROC) curve was applied to assess the model discrimination.

Results : The areas under the ROC curves (AUCs) for the T1WI, T2WI, A and, P and clinical models were 0.893, 0.911, 0.958, 0.997 and 0.516 in the primary cohort, and 0.882, 0.902, 0.920, 0.962 and 0.649 in the validation cohort, respectively. All radiomics models performed better than clinical model and radiologists' evaluation both in the training and testing cohorts by comparing the AUC of various models, all P<0.050. Good calibration was achieved.

Conclusions : The radiomics models based on multiparametric MRI have the potential ability to classify PDAC and MFCP lesions.

Deng Yan, Ming Bing, Zhou Ting, Wu Jia-Long, Chen Yong, Liu Pei, Zhang Ju, Zhang Shi-Yong, Chen Tian-Wu, Zhang Xiao-Ming

2021

machine learning, magnetic resonance imaging, mass-forming chronic pancreatitis, pancreatic ductal adenocarcinoma, radiomics

General General

Social Media Activism and Convergence in Tweet Topics After the Initial #MeToo Movement for Two Distinct Groups of Twitter Users.

In Journal of interpersonal violence

Online social media movements are now common and support cultural discussions on difficult health and social topics. The #MeToo movement, focusing on the pervasiveness of sexual assault and harassment, has been one of the largest and most influential online movements. Our study examines topics of conversation on Twitter by supporters of the #MeToo movement and by Twitter users who were uninvolved in the movement to explore the extent to which tweet topics for these two groups converge over time. We identify and collect one year's worth of tweets for supporters of the #MeToo movement (N = 168 users; N = 105,538 tweets) and users not involved in the movement (N = 147 users; N = 112,301 tweets referred to as the Neutral Sample). We conduct topic frequency analysis and implement an unsupervised machine learning topic modeling algorithm, latent Dirichlet allocation, to explore topics of discussion on Twitter for these two groups of users before and after the initial #MeToo movement. Our results suggest that supporters of #MeToo discussed different topics compared to the Neutral Sample of Twitter users before #MeToo with some overlap on politics. The supporters were already discussing sexual assault and harassment issues six months before #MeToo, and discussion on this topic increased 13.7-fold in the six months after. For the Neutral Sample, sexual assault and harassment was not a key topic of discussion on Twitter before #MeToo, but there was some limited increase afterward. Results of bigram frequency analysis and topic modeling showed a clear increase in topic related to gender for the supporters of #MeToo but gave mixed results for the Neutral Sample comparison group. Our results suggest limited shifts in the conversation on Twitter for the Neutral Sample. Our methods and results have implications for measuring the extent to which online social media movements, like #MeToo, reach a broad audience.

Baik Jason M, Nyein Thet H, Modrek Sepideh

2021-Apr-12

bigram analysis, sexual assault and harassment, social media movements, topic modeling

General General

A statistical and deep learning-based daily infected count prediction system for the coronavirus pandemic.

In Evolutionary intelligence

** : We present new data analytics-based predictions results that can help governments to plan their future actions and also help medical services to be better prepared for the future. Our system can predict new corona cases with 99.82% accuracy using susceptible infected recovered (SIR) model. We have predicted the results of new COVID cases per day for dense and highly populated country i.e. India. We found that traditional statistical methods will not work efficiently as they do not consider the limited population in a particular country. Using the data analytics-based curve we predicted four most likely possibilities for the number of new cases in India. Hence, we expect that the results mentioned in the manuscript help people to better understand the progress of this disease.

Supplementary Information : The online version contains supplementary material available at 10.1007/s12065-021-00600-2.

Shah Vruddhi, Shelke Ankita, Parab Mamata, Shah Jainam, Mehendale Ninad

2021-Apr-03

Coronavirus, Covid-19 simulations, Daily count

Internal Medicine Internal Medicine

Development of a Novel Prognostic Model for Predicting Lymph Node Metastasis in Early Colorectal Cancer: Analysis Based on the Surveillance, Epidemiology, and End Results Database.

In Frontiers in oncology

Background : Identification of a simplified prediction model for lymph node metastasis (LNM) for patients with early colorectal cancer (CRC) is urgently needed to determine treatment and follow-up strategies. Therefore, in this study, we aimed to develop an accurate predictive model for LNM in early CRC.

Methods : We analyzed data from the 2004-2016 Surveillance Epidemiology and End Results database to develop and validate prediction models for LNM. Seven models, namely, logistic regression, XGBoost, k-nearest neighbors, classification and regression trees model, support vector machines, neural network, and random forest (RF) models, were used.

Results : A total of 26,733 patients with a diagnosis of early CRC (T1) were analyzed. The models included 8 independent prognostic variables; age at diagnosis, sex, race, primary site, histologic type, tumor grade, and, tumor size. LNM was significantly more frequent in patients with larger tumors, women, younger patients, and patients with more poorly differentiated tumor. The RF model showed the best predictive performance in comparison to the other method, achieving an accuracy of 96.0%, a sensitivity of 99.7%, a specificity of 92.9%, and an area under the curve of 0.991. Tumor size is the most important features in predicting LNM in early CRC.

Conclusion : We established a simplified reproducible predictive model for LNM in early CRC that could be used to guide treatment decisions. These findings warrant further confirmation in large prospective clinical trials.

Ahn Ji Hyun, Kwak Min Seob, Lee Hun Hee, Cha Jae Myung, Shin Hyun Phil, Jeon Jung Won, Yoon Jin Young

2021

colorectal cancer, machine learning, metastasis, model, prediction

Radiology Radiology

Machine Learning-Based Radiomics Nomogram for Detecting Extramural Venous Invasion in Rectal Cancer.

In Frontiers in oncology

Objective : To establish and validate a radiomics nomogram based on the features of the primary tumor for predicting preoperative pathological extramural venous invasion (EMVI) in rectal cancer using machine learning.

Methods : The clinical and imaging data of 281 patients with primary rectal cancer from April 2012 to May 2018 were retrospectively analyzed. All the patients were divided into a training set (n = 198) and a test set (n = 83) respectively. The radiomics features of the primary tumor were extracted from the enhanced computed tomography (CT), the T2-weighted imaging (T2WI) and the gadolinium contrast-enhanced T1-weighted imaging (CE-TIWI) of each patient. One optimal radiomics signature extracted from each modal image was generated by receiver operating characteristic (ROC) curve analysis after dimensionality reduction. Three kinds of models were constructed based on training set, including the clinical model (the optimal radiomics signature combining with the clinical features), the magnetic resonance imaging model (the optimal radiomics signature combining with the mrEMVI status) and the integrated model (the optimal radiomics signature combining with both the clinical features and the mrEMVI status). Finally, the optimal model was selected to create a radiomics nomogram. The performance of the nomogram to evaluate clinical efficacy was verified by ROC curves and decision curve analysis curves.

Results : The radiomics signature constructed based on T2WI showed the best performance, with an AUC value of 0.717, a sensitivity of 0.742 and a specificity of 0.621. The radiomics nomogram had the highest prediction efficiency, of which the AUC was 0.863, the sensitivity was 0.774 and the specificity was 0.801.

Conclusion : The radiomics nomogram had the highest efficiency in predicting EMVI. This may help patients choose the best treatment strategy and may strengthen personalized treatment methods to further optimize the treatment effect.

Liu Siye, Yu Xiaoping, Yang Songhua, Hu Pingsheng, Hu Yingbin, Chen Xiaoyan, Li Yilin, Zhang Zhe, Li Cheng, Lu Qiang

2021

computed tomography, extramural venous invasion, magnetic resonance imaging, prediction, radiomics, rectal cancer

Surgery Surgery

Machine Learning-Based Ultrasomics Improves the Diagnostic Performance in Differentiating Focal Nodular Hyperplasia and Atypical Hepatocellular Carcinoma.

In Frontiers in oncology

Background : The typical enhancement patterns of hepatocellular carcinoma (HCC) on contrast-enhanced ultrasound (CEUS) are hyper-enhanced in the arterial phase and washed out during the portal venous and late phases. However, atypical variations make a differential diagnosis both challenging and crucial. We aimed to investigate whether machine learning-based ultrasonic signatures derived from CEUS images could improve the diagnostic performance in differentiating focal nodular hyperplasia (FNH) and atypical hepatocellular carcinoma (aHCC).

Patients and Methods : A total of 226 focal liver lesions, including 107 aHCC and 119 FNH lesions, examined by CEUS were reviewed retrospectively. For machine learning-based ultrasomics, 3,132 features were extracted from the images of the baseline, arterial, and portal phases. An ultrasomics signature was generated by a machine learning model. The predictive model was constructed using the support vector machine method trained with the following groups: ultrasomics features, radiologist's score, and combination of ultrasomics features and radiologist's score. The diagnostic performance was explored using the area under the receiver operating characteristic curve (AUC).

Results : A total of 14 ultrasomics features were chosen to build an ultrasomics model, and they presented good performance in differentiating FNH and aHCC with an AUC of 0.86 (95% confidence interval [CI]: 0.80, 0.89), a sensitivity of 76.6% (95% CI: 67.5%, 84.3%), and a specificity of 80.5% (95% CI: 70.6%, 85.9%). The model trained with a combination of ultrasomics features and the radiologist's score achieved a significantly higher AUC (0.93, 95% CI: 0.89, 0.96) than that trained with the radiologist's score (AUC: 0.84, 95% CI: 0.79, 0.89, P < 0.001). For the sub-group of HCC with normal AFP value, the model trained with a combination of ultrasomics features, and the radiologist's score remain achieved the highest AUC of 0.92 (95% CI: 0.87, 0.96) compared to that with the ultrasomics features (AUC: 0.86, 95% CI: 0.74, 0.89, P < 0.001) and radiologist's score (AUC: 0.86, 95% CI: 0.79, 0.91, P < 0.001).

Conclusions : Machine learning-based ultrasomics performs as well as the staff radiologist in predicting the differential diagnosis of FNH and aHCC. Incorporating an ultrasomics signature into the radiologist's score improves the diagnostic performance in differentiating FNH and aHCC.

Li Wei, Lv Xiao-Zhou, Zheng Xin, Ruan Si-Min, Hu Hang-Tong, Chen Li-Da, Huang Yang, Li Xin, Zhang Chu-Qing, Xie Xiao-Yan, Kuang Ming, Lu Ming-De, Zhuang Bo-Wen, Wang Wei

2021

focal nodular hyperplasia, hepatocellular carcinoma, machine learning, ultrasomics, ultrasonography

General General

Evaluating Simulations as Preparation for Health Crises like CoVID-19: Insights on Incorporating Simulation Exercises for Effective Response.

In International journal of disaster risk reduction : IJDRR

Today's health emergencies are increasingly complex due to factors such as globalization, urbanization and increased connectivity where people, goods and potential vectors of disease are constantly on the move. These factors amplify the threats to our health from infectious hazards, natural disasters, armed conflicts and other emergencies wherever they may occur. The current CoVID-19 pandemic has provided a clear demonstration of the fact that our ability to detect and predict the initial emergence of a novel human pathogen (for example, the spill-over of a virus from its animal reservoir to a human host), and our capacity to forecast the spread and transmission the pathogen in human society remains limited. Improving ways in which we prepare will enable a more rapid and effective response and enable proactive preparations (including exercising) to respond to any novel emerging infectious disease outbreaks. This study aims to explore the current state of pandemic preparedness exercising and provides an assessment of a number of case study exercises for health hazards against the key components of the WHO's Exercises for Pandemic Prepared Plans (EPPP) framework in order to gauge their usefulness in preparation for pandemics. The paper also examines past crises involving large-scale epidemics and pandemics and whether simulations took place to test health security capacities either in advance of the crisis based on risk assessments, strategy and plans or after the crisis in order to be better prepared should a similar scenario arise in the future. Exercises for animal and human diseases have been included to provide a "one health" perspective [1,2]. This article then goes on to examine approaches to simulation exercises relevant to prepare for health crisis involving a novel emergent pathogen like CoVID-19. This article demonstrates that while simulations are useful as part of a preparedness strategy, the key is to ensure that lessons from these simulations are learned and the associated changes made as soon as possible following any simulation in order to ensure that simulations are effective in bringing about changes in practice that will improve pandemic preparedness. Furthermore, Artificial Intelligence (AI) technologies could also be applied in preparing communities for outbreak detection, surveillance and containment, and be a useful tool for providing immersive environments for simulation exercises for pandemic preparedness and associated interventions which may be particularly useful at the strategic level. This article contributes to the limited literature in pandemic preparedness simulation exercising to deal with novel health crises, like CoVID-19. The analysis has also identified potential areas for further research or work on pandemic preparedness exercising.

Reddin Karen, Bang Henry, Miles Lee

2021-Apr-05

Emergency Exercise, Epidemic, Lessons learnt, Pandemic, Simulation

General General

Thermal Infrared Face Recognition.

In Cureus

The technology for deep learning in the field of thermal infrared face recognition has recently become more available for use in research, therefore allowing for the many groups working on this subject to achieve many novel findings. Thermal infrared face recognition helps recognize faces that are not able to be recognized in visible light and can additionally recognize facial blood vessel structure. Previous research regarding temperature variations, mathematical formulas, wave types, and methods in thermal infrared face recognition is reviewed.

Weidlich Vincent A

2021-Mar-06

artificial intelligence, blood vessel pattern, deep learning, face recognition, facial vascular pattern, long wave infrared, machine learning, neural networks, thermal infrared imagery, vascular map

General General

Optimizing quantum cloning circuit parameters based on adaptive guided differential evolution algorithm.

In Journal of advanced research

Introduction : Quantum cloning operation, started with no-go theorem which proved that there is no capability to perform a cloning operation on an unknown quantum state, however, a number of trials proved that we can make approximate quantum state cloning that is still with some errors.

Objectives : To the best of our knowledge, this paper is the first of its kind to attempt using meta-heuristic algorithm such as Adaptive Guided Differential Evolution (AGDE), to tackle the problem of quantum cloning circuit parameters to enhance the cloning fidelity.

Methods : To investigate the effectiveness of the AGDE, the extensive experiments have demonstrated that the AGDE can achieve outstanding performance compared to other well-known meta-heuristics including; Enhanced LSHADE-SPACMA Algorithm (ELSHADE-SPACMA), Enhanced Differential Evolution algorithm with novel control parameter adaptation (PaDE), Improved Multi-operator Differential Evolution Algorithm (IMODE), Parameters with adaptive learning mechanism (PALM), QUasi-Affine TRansformation Evolutionary algorithm (QUATRE), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Cuckoo Search (CS), Bat-inspired Algorithm (BA), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA).

Results : In the present study, AGDE is applied to improve the fidelity of quantum cloning problem and the obtained parameter values minimize the cloning difference error value down to 10 - 8 .

Conclusion : Accordingly, the qualitative and quantitative measurements including average, standard deviation, convergence curves of the competitive algorithms over 30 independent runs, proved the superiority of AGDE to enhance the cloning fidelity.

Houssein Essam H, Mahdy Mohamed A, Eldin Manal G, Shebl Doaa, Mohamed Waleed M, Abdel-Aty Mahmoud

2021-Mar

AGDE, Adaptive guided differential evolution, Cloned qubits, Cloning fidelity, Meta-heuristics, Quantum cloning

Radiology Radiology

Can peritumoral regions increase the efficiency of machine-learning prediction of pathological invasiveness in lung adenocarcinoma manifesting as ground-glass nodules?

In Journal of thoracic disease ; h5-index 52.0

Background : The peri-tumor microenvironment plays an important role in the occurrence, growth and metastasis of cancer. The aim of this study is to explore the value and application of a CT image-based deep learning model of tumors and peri-tumors in predicting the invasiveness of ground-glass nodules (GGNs).

Methods : Preoperative thin-section chest CT images were reviewed retrospectively in 622 patients with a total of 687 pulmonary GGNs. GGNs are classified according to clinical management strategies as invasive lesions (IAC) and non-invasive lesions (AAH, AIS and MIA). The two volumes of interest (VOIs) identified on CT were the gross tumor volume (GTV) and the gross volume of tumor incorporating peritumoral region (GPTV). Three dimensional (3D) DenseNet was used to model and predict GGN invasiveness, and five-fold cross validation was performed. We used GTV and GPTV as inputs for the comparison model. Prediction performance was evaluated by sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).

Results : The GTV-based model was able to successfully predict GGN invasiveness, with an AUC of 0.921 (95% CI, 0.896-0.937). Using GPTV, the AUC of the model increased to 0.955 (95% CI, 0.939-0.971).

Conclusions : The deep learning method performed well in predicting GGN invasiveness. The predictive ability of the GPTV-based model was more effective than that of the GTV-based model.

Wang Xiang, Chen Kaili, Wang Wei, Li Qingchu, Liu Kai, Li Qianyun, Cui Xing, Tu Wenting, Sun Hongbiao, Xu Shaochun, Zhang Rongguo, Xiao Yi, Fan Li, Liu Shiyuan

2021-Mar

Pulmonary adenocarcinoma, X-ray computed tomography (X-ray CT), deep learning, peritumoral region, tumor invasiveness

General General

Fiji plugins for qualitative image annotations: routine analysis and application to image classification.

In F1000Research

Quantitative measurements and qualitative description of scientific images are both important to describe the complexity of digital image data. While various software solutions for quantitative measurements in images exist, there is a lack of simple tools for the qualitative description of images in common user-oriented image analysis software. To address this issue, we developed a set of Fiji plugins that facilitate the systematic manual annotation of images or image-regions. From a list of user-defined keywords, these plugins generate an easy-to-use graphical interface with buttons or checkboxes for the assignment of single or multiple pre-defined categories to full images or individual regions of interest. In addition to qualitative annotations, any quantitative measurement from the standard Fiji options can also be automatically reported. Besides the interactive user interface, keyboard shortcuts are available to speed-up the annotation process for larger datasets. The annotations are reported in a Fiji result table that can be exported as a pre-formatted csv file, for further analysis with common spreadsheet software or custom automated pipelines. To facilitate and spread the usage of analysis tools, we provide examples of such pipelines, including a complete workflow for training and application of a deep learning model for image classification in KNIME. Ultimately, the plugins enable standardized routine sample evaluation, classification, or ground-truth category annotation of any digital image data compatible with Fiji.

Thomas Laurent S V, Schaefer Franz, Gehrig Jochen

2020

Fiji, ImageJ, KNIME, bioimage analysis, ground-truth labelling, image annotation, image classification, qualitative analysis

General General

A review on compound-protein interaction prediction methods: Data, format, representation and model.

In Computational and structural biotechnology journal

There has recently been a rapid progress in computational methods for determining protein targets of small molecule drugs, which will be termed as compound protein interaction (CPI). In this review, we comprehensively review topics related to computational prediction of CPI. Data for CPI has been accumulated and curated significantly both in quantity and quality. Computational methods have become powerful ever to analyze such complex the data. Thus, recent successes in the improved quality of CPI prediction are due to use of both sophisticated computational techniques and higher quality information in the databases. The goal of this article is to provide reviews of topics related to CPI, such as data, format, representation, to computational models, so that researchers can take full advantages of these resources to develop novel prediction methods. Chemi