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Category articles

General General

Unravelling the metabolism black-box in a dynamic wetland environment using a hybrid model framework: Storm driven changes in oxygen budgets.

In The Science of the total environment

Estimating gross primary production and ecosystem respiration from oxygen data is performed widely in aquatic systems, yet these estimates can be challenged by high advective fluxes of oxygen. In this study, we develop a hybrid framework linking data-driven and process-based modelling to examine the effect of storm events on oxygen budgets in a constructed wetland. After calibration against measured flow and water temperature data over a two-month period with three storm events, the model was successfully validated against high frequency dissolved oxygen (DO) data exhibiting large diurnal fluctuations. The results demonstrated that pulses of high-DO water injected into the wetland during storm events were able to dramatically change the wetland oxygen budget. A shift was observed in the dominant oxygen inputs, from benthic net production during non-storm periods, to inflows of oxygen during storm events, which served to dampen the classical diurnal oxygen signature. The model also demonstrated the changing balance of pelagic versus benthic production and hypoxia extent in response to storm events, which has implications for the nutrient attenuation performance of constructed wetlands. The study highlights the benefit of linking analysis of high-frequency oxygen data with process-based modelling tools to unravel the varied responses of components of the oxygen budget to storm events.

Liu Junjie, Wang Benya, Oldham Carolyn E, Hipsey Matthew R

2020-Mar-19

Constructed wetland, Hydrodynamic model, Hypoxia, Machine learning, Photosynthesis, Urban drainage

General General

SDARE: A stacked denoising autoencoder method for game dynamics network structure reconstruction.

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

Complex network is a general model to represent the interactions within technological, social, information, and biological interaction. Often, the direct detection of the interaction relationship is costly. Thus, network structure reconstruction, the inverse problem in complex networked systems, is of utmost importance for understanding many complex systems with unknown interaction structures. In addition, the data collected from real network system is often contaminated by noise, which makes the network structure inference task much more challenging. In this paper, we develop a new framework for the game dynamics network structure reconstruction based on deep learning method. In contrast to the compressive sensing methods that employ computationally complex convex/greedy algorithms to solve the network reconstruction task, we introduce a deep learning framework that can learn a structured representation from nodes data and efficiently reconstruct the game dynamics network structure with few observation data. Specifically, we propose the denoising autoencoders (DAEs) as the unsupervised feature learner to capture statistical dependencies between different nodes. Compared to the compressive sensing based method, the proposed method is a global network structure inference method, which can not only get the state-of-art performance, but also obtain the structure of network directly. Besides, the proposed method is robust to noise in the observation data. Moreover, the proposed method is also effective for the network which is not exactly sparse. Accordingly, the proposed method can extend to a wide scope of network reconstruction task in practice.

Huang Keke, Li Shuo, Dai Penglin, Wang Zhen, Yu Zhaofei

2020-Mar-14

Complex network, Compressive sensing, Deep learning, Network structure reconstruction, Stacked denoising autoencoder

General General

Assessing the biophysical and social drivers of burned area distribution at the local scale.

In Journal of environmental management

Understanding the characteristics of wildfire-affected communities and the importance of particular factors of different dimensions, is paramount to improve prevention and mitigation strategies, tailored to people's needs and abilities. In this study, we explored different combinations of biophysical and social factors to characterize wildfire-affected areas in Portugal. By means of machine-learning methods based on classification trees, we assessed the predictive ability of various models to discriminate different levels of wildfire incidence at the local scale. The model with the best performance included a reduced set of both biophysical and social variables and we found that, oveall, the exclusion of specific variables improved prediction rates of group classification. The most important variables were related to landcover; the civil parishes covered by more than 20% of shrublands were more fire-prone, whereas those parishes with at least 40% of agricultural land were less affected by wildfires. Regarding social variables, the most-affected parishes showed a lower proportion of foreign residents and lower purchasing power, conditions likely associated with the socioeconomic context of inland low-density rural areas, where rural abandonment, depopulation and ageing trends have been observed in the last decades. Further research is needed to investigate how other particular parameters representing the social context, and its evolution, can be integrated in wildfire occurrence modelling, and how these interact with the biophysical conditions over time.

Oliveira Sandra, Zêzere José Luís

2020-Mar-26

Biophysical factors, Local communities, Portugal, Risk mitigation, Sociodemographic variables, Wildfire incidence

General General

A diffeomorphic unsupervised method for deformable soft tissue image registration.

In Computers in biology and medicine

BACKGROUND AND OBJECTIVES : The image registration methods for deformable soft tissues utilize nonlinear transformations to align a pair of images precisely. In some situations, when there is huge gray scale difference or large deformation between the images to be registered, the deformation field tends to fold at some local voxels, which will result in the breakdown of the one-to-one mapping between images and the reduction of invertibility of the deformation field. In order to address this issue, a novel registration approach based on unsupervised learning is presented for deformable soft tissue image registration.

METHODS : A novel unsupervised learning based registration approach, which consists of a registration network, a velocity field integration module and a grid sampling module, is presented for deformable soft tissue image registration. The main contributions are: (1) A novel encoder-decoder network is presented for the evaluation of stationary velocity field. (2) A Jacobian determinant based penalty term (Jacobian loss) is developed to reduce the folding voxels and to improve the invertibility of the deformation field.

RESULTS AND CONCLUSIONS : The experimental results show that a new pair of images can be accurately registered using the trained registration model. In comparison with the conventional state-of-the-art method, SyN, the invertibility of the deformation field, accuracy and speed are all improved. Compared with the deep learning based method, VoxelMorph, the proposed method improves the invertibility of the deformation field.

Zhang Shuo, Liu Peter Xiaoping, Zheng Minhua, Shi Wen

2020-Mar-20

Deformable soft tissue image registration, Encoder–decoder network, Invertibility, Jacobian loss, Unsupervised learning

Radiology Radiology

Impact of hybrid supervision approaches on the performance of artificial intelligence for the classification of chest radiographs.

In Computers in biology and medicine

PURPOSE : To evaluate the impact of different supervision regimens on the training of artificial intelligence (AI) in the classification of chest radiographs as normal or abnormal in a moderately sized cohort of individuals more likely to be outpatients.

MATERIALS AND METHODS : In a retrospective study, 7000 consecutive two-view chest radiographs obtained from 2012 to 2015 were labeled as normal or abnormal based on clinical reports. A convolutional neural network (CNN) was trained on this dataset and then evaluated with an unseen subset of 500 radiographs. Five different training approaches were tested: (1) weak supervision and four hybrid approaches combining weak supervision and extra supervision with annotation in (2) an unbalanced set of normal and abnormal cases, (3) a set of only abnormal cases, (4) a set of only normal cases, and (5) a balanced set of normal and abnormal cases. Standard binary classification metrics were assessed.

RESULTS : The weakly supervised model achieved an accuracy of 82%, but yielded 75 false negative cases, at a sensitivity of 70.0% and a negative predictive value (NPV) of 75.5%. Extra supervision increased NPV at the expense of the false positive rate and overall accuracy. Extra supervision with training using a balance of abnormal and normal radiographs resulted in the greatest increase in NPV (87.2%), improved sensitivity (92.8%), and reduced the number of false negatives by more than fourfold (18 compared to 75 cases).

CONCLUSION : Extra supervision using a balance of annotated normal and abnormal cases applied to a weakly supervised model can minimize the number of false negative cases when classifying two-view chest radiographs. Further refinement of such hybrid training approaches for AI is warranted to refine models for practical clinical applications.

Ellis Ryan, Ellestad Erik, Elicker Brett, Hope Michael D, Tosun Duygu

2020-Mar-17

Attention mining, Chest radiographs, Extra supervision, Hybrid supervision, Outpatient, Two-view radiographs, Weak supervision

General General

Machine learning for microbial identification and antimicrobial susceptibility testing on MALDI-TOF mass spectra: a systematic review.

In Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases

BACKGROUND : The matrix assisted laser desorption/ionization and time-of-flight mass spectrometry (MALDI-TOF MS) technology has revolutionized the field of microbiology by facilitating precise and rapid species identification. Recently, machine learning techniques have been leveraged to maximally exploit the information contained in MALDI-TOF M with the ultimate goal to refine species identification and streamline antimicrobial resistance determination.

OBJECTIVES : To systematically review and evaluate studies employing machine learning for the analysis of MALDI-TOF mass spectra.

DATA SOURCES : Using PubMed/Medline, Scopus, and Web of Science, we searched the existing literature for machine learning supported applications of MALDI-TOF mass spectra for microbial species and antimicrobial susceptibility identification.

STUDY ELIGIBILITY CRITERIA : Original research studies using machine learning to exploit MALDI-TOF mass spectra for microbial species and antimicrobial susceptibility identification were included. Studies focusing on single proteins and peptides, case studies, and review articles were excluded.

METHODS : A systematic review according to the PRISMA guidelines was performed and a quality assessment of the machine learning models conducted.

RESULTS : From the 36 studies that met our inclusion criteria, 27 employed machine learning for species identification and 9 for antimicrobial susceptibility testing. Support Vector Machines, Genetic Algorithms, Artificial Neural Networks, and Quick Classifiers were the most frequently used machine learning algorithms. The quality of the studies ranged between poor and very good. While, the majority of the studies reported how to interpret the predictors (88.89%) and suggested possible clinical applications of the developed algorithm (100%), only four studies (11.11%) validated machine learning algorithms on external datasets.

CONCLUSIONS : A growing number of studies utilizes machine learning to optimize the analysis of MALDI-TOF mass spectra. This review, however, demonstrates that there are certain shortcomings of current machine learning supported approaches that have to be addressed to make them widely available and incorporated them in the clinical routine.

Cv Weis, Cr Jutzeler, K Borgwardt

2020-Mar-23

MALDI-TOF MS, antimicrobial resistance, antimicrobial susceptibility testing, antimicrobial treatment, machine learning, microbial identification

General General

DeepMILO: a deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure.

In Genome biology ; h5-index 114.0

Non-coding variants have been shown to be related to disease by alteration of 3D genome structures. We propose a deep learning method, DeepMILO, to predict the effects of variants on CTCF/cohesin-mediated insulator loops. Application of DeepMILO on variants from whole-genome sequences of 1834 patients of twelve cancer types revealed 672 insulator loops disrupted in at least 10% of patients. Our results show mutations at loop anchors are associated with upregulation of the cancer driver genes BCL2 and MYC in malignant lymphoma thus pointing to a possible new mechanism for their dysregulation via alteration of insulator loops.

Trieu Tuan, Martinez-Fundichely Alexander, Khurana Ekta

2020-Mar-26

3D genome, BCL2, Cancer, Deep learning, MYC, Non-coding mutation

Radiology Radiology

High-dose 131I-metaiodobenzylguanidine therapy in patients with high-risk neuroblastoma in Japan.

In Annals of nuclear medicine

OBJECTIVE : The aim of the study was to investigate the outcomes and prognostic factors of high-dose 131I-metaiodobenzylguanidine (131I-MIBG) therapy in patients with refractory or relapsed neuroblastoma (NBL) in Japan.

METHODS : We retrospectively analyzed 20 patients with refractory or relapsed high-risk NBL who underwent 131I-MIBG therapy with an administration dose ranging from 444 to 666 MBq/kg at Kanazawa University Hospital, Japan, between September 2008 and September 2013. We focused on measurements regarding their initial responses, prognostic factors, survivals, and toxicities following 131I-MIBG therapy using our hospital data and questionnaires from the hospitals that these patients were initially referred from. Furthermore, we performed Kaplan-Meier survival analysis to evaluate event-free survival (EFS) and overall survival (OS).

RESULTS : In 19 patients with complete follow-up data, the median age at first 131I-MIBG treatment was 7.9 years (range 2.5-17.7 years). Following 131I-MIBG therapy, 17 of the 19 patients underwent stem-cell transplantations, and their treatment response was either complete (CR) or partial (PR) in three and two cases, respectively. The EFS and OS rates at 1 year following 131I-MIBG therapy were 42% and 58%, respectively, and those at 5 years following 131I-MIBG therapy were 16% and 42%, respectively. Using the two-sample log-rank test, the OS time following 131I-MIBG therapy was significantly longer for < 3-year time interval between the initial diagnosis and 131I-MIBG therapy (p = 0.017), Curie score < 16 just before 131I-MIBG therapy (p = 0.002), without pain (p = 0.002), without both vanillylmandelic acid (VMA) and homovanillic acid (HVA) elevation (p = 0.037) at 131I-MIBG therapy, and with CR or PR following 131I-MIBG therapy (p = 0.015). Although severe hematological toxicities were identified in all 19 patients, severe nonhematological toxicity was not recorded in any patient, except for one patient with grade 3 anorexia and nausea.

CONCLUSIONS : High-dose 131I-MIBG therapy in patients with refractory or relapsed high-risk NBL can provide a favorable prognosis without severe nonhematological toxicities. Better prognosis may be anticipated in patients with the initial good response, no pain at 131I-MIBG therapy, no VMA and HVA elevation at 131I-MIBG therapy, low Curie score (< 16) just before 131I-MIBG therapy, and short time interval (< 3 years) between the initial diagnosis and 131I-MIBG therapy.

Kayano Daiki, Wakabayashi Hiroshi, Nakajima Kenichi, Kuroda Rie, Watanabe Satoru, Inaki Anri, Toratani Ayane, Akatani Norihito, Yamase Takafumi, Kunita Yuji, Hiromasa Tomo, Takata Aki, Mori Hiroshi, Saito Shintaro, Araki Raita, Taki Junichi, Kinuya Seigo

2020-Mar-26

131I-metaiodobenzylguanidine, Internal radiation therapy, Radionuclide therapy, Refractory neuroblastoma, Relapsed neuroblastoma

General General

Detecting and Classifying Self-injurious Behavior in Autism Spectrum Disorder Using Machine Learning Techniques.

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

Traditional self-injurious behavior (SIB) management can place compliance demands on the caregiver and have low ecological validity and accuracy. To support an SIB monitoring system for autism spectrum disorder (ASD), we evaluated machine learning methods for detecting and distinguishing diverse SIB types. SIB episodes were captured with body-worn accelerometers from children with ASD and SIB. The highest detection accuracy was found with k-nearest neighbors and support vector machines (up to 99.1% for individuals and 94.6% for grouped participants), and classification efficiency was quite high (offline processing at ~ 0.1 ms/observation). Our results provide an initial step toward creating a continuous and objective smart SIB monitoring system, which could in turn facilitate the future care of a pervasive concern in ASD.

Cantin-Garside Kristine D, Kong Zhenyu, White Susan W, Antezana Ligia, Kim Sunwook, Nussbaum Maury A

2020-Mar-26

Activity recognition, Autism, Machine learning, Wearable sensors

Radiology Radiology

Deep learning-based attenuation map generation for myocardial perfusion SPECT.

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

PURPOSE : Attenuation correction using CT transmission scanning increases the accuracy of single-photon emission computed tomography (SPECT) and enables quantitative analysis. Current existing SPECT-only systems normally do not support transmission scanning and therefore scans on these systems are susceptible to attenuation artifacts. Moreover, the use of CT scans also increases radiation dose to patients and significant artifacts can occur due to the misregistration between the SPECT and CT scans as a result of patient motion. The purpose of this study is to develop an approach to estimate attenuation maps directly from SPECT emission data using deep learning methods.

METHODS : Both photopeak window and scatter window SPECT images were used as inputs to better utilize the underlying attenuation information embedded in the emission data. The CT-based attenuation maps were used as labels with which cardiac SPECT/CT images of 65 patients were included for training and testing. We implemented and evaluated deep fully convolutional neural networks using both standard training and training using an adversarial strategy.

RESULTS : The synthetic attenuation maps were qualitatively and quantitatively consistent with the CT-based attenuation map. The globally normalized mean absolute error (NMAE) between the synthetic and CT-based attenuation maps were 3.60% ± 0.85% among the 25 testing subjects. The SPECT reconstructed images corrected using the CT-based attenuation map and synthetic attenuation map are highly consistent. The NMAE between the reconstructed SPECT images that were corrected using the synthetic and CT-based attenuation maps was 0.26% ± 0.15%, whereas the localized absolute percentage error was 1.33% ± 3.80% in the left ventricle (LV) myocardium and 1.07% ± 2.58% in the LV blood pool.

CONCLUSION : We developed a deep convolutional neural network to estimate attenuation maps for SPECT directly from the emission data. The proposed method is capable of generating highly reliable attenuation maps to facilitate attenuation correction for SPECT-only scanners for myocardial perfusion imaging.

Shi Luyao, Onofrey John A, Liu Hui, Liu Yi-Hwa, Liu Chi

2020-Mar-26

Deep learning, Myocardial perfusion imaging, SPECT, Synthetic attenuation map

General General

Making Sense of Computational Psychiatry.

In The international journal of neuropsychopharmacology

In psychiatry we often speak of constructing "models." Here we try to make sense of what such a claim might mean, starting with the most fundamental question: "What is (and isn't) a model?" We then discuss, in a concrete measurable sense, what it means for a model to be useful. In so doing, we first identify the added value that a computational model can provide in the context of accuracy and power. We then present limitations of standard statistical methods and provide suggestions for how we can expand the explanatory power of our analyses by reconceptualizing statistical models as dynamical systems. Finally, we address the problem of model building-suggesting ways in which computational psychiatry can escape the potential for cognitive biases imposed by classical hypothesis-driven research, exploiting deep systems-level information contained within neuroimaging data to advance our understanding of psychiatric neuroscience.

Mujica-Parodi Lilianne R, Strey Helmut H

2020-Mar-27

** control systems, RDoC, circuit, fMRI, generative models, machine learning, neuroimaging, neuroscience, psychiatry, system identification**

Public Health Public Health

Mediation of firearm violence and preterm birth by pregnancy complications and health behaviors: Addressing structural and post-exposure confounding.

In American journal of epidemiology ; h5-index 65.0

Firearm violence may indirectly affect health among pregnant women living in neighborhoods where it is endemic. We used birth, death, emergency department, and hospitalization data from California from 2007-2011 to estimate the association between living in a neighborhood with high firearm violence and preterm delivery, and assessed whether there was mediation by diagnoses of pregnancy complications and health behaviors during pregnancy. We used an ensemble machine learning algorithm to predict the propensity for neighborhoods to be classified as high firearm violence. Risk differences (RD) for the total effect and stochastic direct and indirect effects were estimated using targeted maximum likelihood. Residence in high violence neighborhoods was associated with higher prevalence of preterm birth [RD = 0.46 (95% CI: 0.13, 0.80)], infections [RD = 1.34 (95% CI: -0.17, 2.86)], asthma [RD = 0.76 (95% CI: 0.03, 1.48)], and substance use [RD=0.74 (95% CI: 0.00, 1.47)]. The largest indirect effects between violence and preterm birth were observed for infection [0.04 (95% CI: 0.00, 0.08)] and substance use [0.04 (95% CI: 0.01, 0.06)]. Firearm violence was associated with risk of preterm delivery, and this association was partially mediated by infection and substance use.

Goin Dana E, Rudolph Kara E, Gomez Anu Manchikanti, Ahern Jennifer

2020-Mar-27

causal mediation analysis, firearm violence, pregnancy complications, preterm birth

General General

Molecular Signatures of Fusion Proteins in Cancer.

In ACS pharmacology & translational science

Although gene fusions are recognized as driver mutations in a wide variety of cancers, the general molecular mechanisms underlying oncogenic fusion proteins are insufficiently understood. Here, we employ large-scale data integration and machine learning and (1) identify three functionally distinct subgroups of gene fusions and their molecular signatures; (2) characterize the cellular pathways rewired by fusion events across different cancers; and (3) analyze the relative importance of over 100 structural, functional, and regulatory features of ∼2200 gene fusions. We report subgroups of fusions that likely act as driver mutations and find that gene fusions disproportionately affect pathways regulating cellular shape and movement. Although fusion proteins are similar across different cancer types, they affect cancer type-specific pathways. Key indicators of fusion-forming proteins include high and nontissue specific expression, numerous splice sites, and higher centrality in protein-interaction networks. Together, these findings provide unifying and cancer type-specific trends across diverse oncogenic fusion proteins.

Latysheva Natasha S, Babu M Madan

2019-Apr-12

General General

The future of sleep health: a data-driven revolution in sleep science and medicine.

In NPJ digital medicine

In recent years, there has been a significant expansion in the development and use of multi-modal sensors and technologies to monitor physical activity, sleep and circadian rhythms. These developments make accurate sleep monitoring at scale a possibility for the first time. Vast amounts of multi-sensor data are being generated with potential applications ranging from large-scale epidemiological research linking sleep patterns to disease, to wellness applications, including the sleep coaching of individuals with chronic conditions. However, in order to realise the full potential of these technologies for individuals, medicine and research, several significant challenges must be overcome. There are important outstanding questions regarding performance evaluation, as well as data storage, curation, processing, integration, modelling and interpretation. Here, we leverage expertise across neuroscience, clinical medicine, bioengineering, electrical engineering, epidemiology, computer science, mHealth and human-computer interaction to discuss the digitisation of sleep from a inter-disciplinary perspective. We introduce the state-of-the-art in sleep-monitoring technologies, and discuss the opportunities and challenges from data acquisition to the eventual application of insights in clinical and consumer settings. Further, we explore the strengths and limitations of current and emerging sensing methods with a particular focus on novel data-driven technologies, such as Artificial Intelligence.

Perez-Pozuelo Ignacio, Zhai Bing, Palotti Joao, Mall Raghvendra, Aupetit Michaël, Garcia-Gomez Juan M, Taheri Shahrad, Guan Yu, Fernandez-Luque Luis

2020

Biomedical engineering, Diagnostic markers, Predictive markers, Preventive medicine, Sleep

General General

Presenting machine learning model information to clinical end users with model facts labels.

In NPJ digital medicine

There is tremendous enthusiasm surrounding the potential for machine learning to improve medical prognosis and diagnosis. However, there are risks to translating a machine learning model into clinical care and clinical end users are often unaware of the potential harm to patients. This perspective presents the "Model Facts" label, a systematic effort to ensure that front-line clinicians actually know how, when, how not, and when not to incorporate model output into clinical decisions. The "Model Facts" label was designed for clinicians who make decisions supported by a machine learning model and its purpose is to collate relevant, actionable information in 1-page. Practitioners and regulators must work together to standardize presentation of machine learning model information to clinical end users in order to prevent harm to patients. Efforts to integrate a model into clinical practice should be accompanied by an effort to clearly communicate information about a machine learning model with a "Model Facts" label.

Sendak Mark P, Gao Michael, Brajer Nathan, Balu Suresh

2020

Health policy, Health services, Translational research

Ophthalmology Ophthalmology

Technical and imaging factors influencing performance of deep learning systems for diabetic retinopathy.

In NPJ digital medicine

Deep learning (DL) has been shown to be effective in developing diabetic retinopathy (DR) algorithms, possibly tackling financial and manpower challenges hindering implementation of DR screening. However, our systematic review of the literature reveals few studies studied the impact of different factors on these DL algorithms, that are important for clinical deployment in real-world settings. Using 455,491 retinal images, we evaluated two technical and three image-related factors in detection of referable DR. For technical factors, the performances of four DL models (VGGNet, ResNet, DenseNet, Ensemble) and two computational frameworks (Caffe, TensorFlow) were evaluated while for image-related factors, we evaluated image compression levels (reducing image size, 350, 300, 250, 200, 150 KB), number of fields (7-field, 2-field, 1-field) and media clarity (pseudophakic vs phakic). In detection of referable DR, four DL models showed comparable diagnostic performance (AUC 0.936-0.944). To develop the VGGNet model, two computational frameworks had similar AUC (0.936). The DL performance dropped when image size decreased below 250 KB (AUC 0.936, 0.900, p < 0.001). The DL performance performed better when there were increased number of fields (dataset 1: 2-field vs 1-field-AUC 0.936 vs 0.908, p < 0.001; dataset 2: 7-field vs 2-field vs 1-field, AUC 0.949 vs 0.911 vs 0.895). DL performed better in the pseudophakic than phakic eyes (AUC 0.918 vs 0.833, p < 0.001). Various image-related factors play more significant roles than technical factors in determining the diagnostic performance, suggesting the importance of having robust training and testing datasets for DL training and deployment in the real-world settings.

Yip Michelle Y T, Lim Gilbert, Lim Zhan Wei, Nguyen Quang D, Chong Crystal C Y, Yu Marco, Bellemo Valentina, Xie Yuchen, Lee Xin Qi, Hamzah Haslina, Ho Jinyi, Tan Tien-En, Sabanayagam Charumathi, Grzybowski Andrzej, Tan Gavin S W, Hsu Wynne, Lee Mong Li, Wong Tien Yin, Ting Daniel S W

2020

Population screening, Translational research

General General

HeMoQuest: a webserver for qualitative prediction of transient heme binding to protein motifs.

In BMC bioinformatics

BACKGROUND : The notion of heme as a regulator of many physiological processes via transient binding to proteins is one that is recently being acknowledged. The broad spectrum of the effects of heme makes it important to identify further heme-regulated proteins to understand physiological and pathological processes. Moreover, several proteins were shown to be functionally regulated by interaction with heme, yet, for some of them the heme-binding site(s) remain unknown. The presented application HeMoQuest enables identification and qualitative evaluation of such heme-binding motifs from protein sequences.

RESULTS : We present HeMoQuest, an online interface (http://bit.ly/hemoquest) to algorithms that provide the user with two distinct qualitative benefits. First, our implementation rapidly detects transient heme binding to nonapeptide motifs from protein sequences provided as input. Additionally, the potential of each predicted motif to bind heme is qualitatively gauged by assigning binding affinities predicted by an ensemble learning implementation, trained on experimentally determined binding affinity data. Extensive testing of our implementation on both existing and new manually curated datasets reveal that our method produces an unprecedented level of accuracy (92%) in identifying those residues assigned "heme binding" in all of the datasets used. Next, the machine learning implementation for the prediction and qualitative assignment of binding affinities to the predicted motifs achieved 71% accuracy on our data.

CONCLUSIONS : Heme plays a crucial role as a regulatory molecule exerting functional consequences via transient binding to surfaces of target proteins. HeMoQuest is designed to address this imperative need for a computational approach that enables rapid detection of heme-binding motifs from protein datasets. While most existing implementations attempt to predict sites of permanent heme binding, this application is to the best of our knowledge, the first of its kind to address the significance of predicting transient heme binding to proteins.

Paul George Ajay Abisheck, Lacerda Mauricio, Syllwasschy Benjamin Franz, Hopp Marie-Thérèse, Wißbrock Amelie, Imhof Diana

2020-Mar-27

Heme, Heme-binding site prediction, Heme-regulated protein, Machine learning, Transient heme binding, Web application

General General

High throughput image labeling on chest computed tomography by deep learning.

In Journal of medical imaging (Bellingham, Wash.)

When mining image data from PACs or clinical trials or processing large volumes of data without curation, the relevant scans must be identified among irrelevant or redundant data. Only images acquired with appropriate technical factors, patient positioning, and physiological conditions may be applicable to a particular image processing or machine learning task. Automatic labeling is important to make big data mining practical by replacing conventional manual review of every single-image series. Digital imaging and communications in medicine headers usually do not provide all the necessary labels and are sometimes incorrect. We propose an image-based high throughput labeling pipeline using deep learning, aimed at identifying scan direction, scan posture, lung coverage, contrast usage, and breath-hold types. They were posed as different classification problems and some of them involved further segmentation and identification of anatomic landmarks. Images of different view planes were used depending on the specific classification problem. All of our models achieved accuracy > 99 % on test set across different tasks using a research database from multicenter clinical trials.

Wang Xiaoyong, Teng Pangyu, Ontiveros Ashley, Goldin Jonathan G, Brown Matthew S

2020-Mar

clinical trials, computed tomography, convolutional neural network, image labeling

General General

Exploring the Consistency of the Quality Scores with Machine Learning for Next-Generation Sequencing Experiments.

In BioMed research international ; h5-index 102.0

Background : Next-generation sequencing enables massively parallel processing, allowing lower cost than the other sequencing technologies. In the subsequent analysis with the NGS data, one of the major concerns is the reliability of variant calls. Although researchers can utilize raw quality scores of variant calling, they are forced to start the further analysis without any preevaluation of the quality scores.

Method : We presented a machine learning approach for estimating quality scores of variant calls derived from BWA+GATK. We analyzed correlations between the quality score and these annotations, specifying informative annotations which were used as features to predict variant quality scores. To test the predictive models, we simulated 24 paired-end Illumina sequencing reads with 30x coverage base. Also, twenty-four human genome sequencing reads resulting from Illumina paired-end sequencing with at least 30x coverage were secured from the Sequence Read Archive.

Results : Using BWA+GATK, VCFs were derived from simulated and real sequencing reads. We observed that the prediction models learned by RFR outperformed other algorithms in both simulated and real data. The quality scores of variant calls were highly predictable from informative features of GATK Annotation Modules in the simulated human genome VCF data (R2: 96.7%, 94.4%, and 89.8% for RFR, MLR, and NNR, respectively). The robustness of the proposed data-driven models was consistently maintained in the real human genome VCF data (R2: 97.8% and 96.5% for RFR and MLR, respectively).

Cosgun Erdal, Oh Min

2020

General General

Immune predictors of oral poliovirus vaccine immunogenicity among infants in South India.

In NPJ vaccines

Identification of the causes of poor oral vaccine immunogenicity in low-income countries might lead to more effective vaccines. We measured mucosal and systemic immune parameters at the time of vaccination with oral poliovirus vaccine (OPV) in 292 Indian infants aged 6-11 months, including plasma cytokines, leukocyte counts, fecal biomarkers of environmental enteropathy and peripheral blood T-cell phenotype, focused on gut-homing regulatory CD4+ populations. We did not find a distinct immune phenotype associated with OPV immunogenicity, although viral pathogens were more prevalent in stool at the time of immunization among infants who failed to seroconvert (63.9% vs. 45.6%, p = 0.002). Using a machine-learning approach, we could predict seroconversion a priori using immune parameters and infection status with a median 58% accuracy (cross-validation IQR: 50-69%) compared with 50% expected by chance. Better identification of immune predictors of OPV immunogenicity is likely to require sampling of mucosal tissue and improved oral poliovirus infection models.

Babji Sudhir, Manickavasagam Punithavathy, Chen Yin-Huai, Jeyavelu Nithya, Jose Nisha Vincy, Praharaj Ira, Syed Chanduni, Kaliappan Saravanakumar Puthupalayam, John Jacob, Giri Sidhartha, Venugopal Srinivasan, Kampmann Beate, Parker Edward P K, Iturriza-Gómara Miren, Kang Gagandeep, Grassly Nicholas C, Uhlig Holm H

2020

Live attenuated vaccines, Paediatric research

General General

The aging human body shape.

In NPJ aging and mechanisms of disease

Body shape and composition are heterogeneous among humans with possible impact for health. Anthropometric methods and data are needed to better describe the diversity of the human body in human populations, its age dependence, and associations with health risk. We applied whole-body laser scanning to a cohort of 8499 women and men of age 40-80 years within the frame of the LIFE (Leipzig Research Center for Civilization Diseases) study aimed at discovering health risk in a middle European urban population. Body scanning delivers multidimensional anthropometric data, which were further processed by machine learning to stratify the participants into body types. We here applied this body typing concept to describe the diversity of body shapes in an aging population and its association with physical activity and selected health and lifestyle factors. We find that aging results in similar reshaping of female and male bodies despite the large diversity of body types observed in the study. Slim body shapes remain slim and partly tend to become even more lean and fragile, while obese body shapes remain obese. Female body shapes change more strongly than male ones. The incidence of the different body types changes with characteristic Life Course trajectories. Physical activity is inversely related to the body mass index and decreases with age, while self-reported incidence for myocardial infarction shows overall the inverse trend. We discuss health risks factors in the context of body shape and its relation to obesity. Body typing opens options for personalized anthropometry to better estimate health risk in epidemiological research and future clinical applications.

Frenzel Alexander, Binder Hans, Walter Nadja, Wirkner Kerstin, Loeffler Markus, Loeffler-Wirth Henry

2020

Ageing, Health care

General General

FaceLift: a transparent deep learning framework to beautify urban scenes.

In Royal Society open science

In the area of computer vision, deep learning techniques have recently been used to predict whether urban scenes are likely to be considered beautiful: it turns out that these techniques are able to make accurate predictions. Yet they fall short when it comes to generating actionable insights for urban design. To support urban interventions, one needs to go beyond predicting beauty, and tackle the challenge of recreating beauty. Unfortunately, deep learning techniques have not been designed with that challenge in mind. Given their 'black-box nature', these models cannot be directly used to explain why a particular urban scene is deemed to be beautiful. To partly fix that, we propose a deep learning framework (which we name FaceLift) that is able to both beautify existing urban scenes (Google Street Views) and explain which urban elements make those transformed scenes beautiful. To quantitatively evaluate our framework, we cannot resort to any existing metric (as the research problem at hand has never been tackled before) and need to formulate new ones. These new metrics should ideally capture the presence (or absence) of elements that make urban spaces great. Upon a review of the urban planning literature, we identify five main metrics: walkability, green spaces, openness, landmarks and visual complexity. We find that, across all the five metrics, the beautified scenes meet the expectations set by the literature on what great spaces tend to be made of. This result is further confirmed by a 20-participant expert survey in which FaceLift has been found to be effective in promoting citizen participation. All this suggests that, in the future, as our framework's components are further researched and become better and more sophisticated, it is not hard to imagine technologies that will be able to accurately and efficiently support architects and planners in the design of the spaces we intuitively love.

Joglekar Sagar, Quercia Daniele, Redi Miriam, Aiello Luca Maria, Kauer Tobias, Sastry Nishanth

2020-Jan

deep learning, explainable models, generative models, urban beauty, urban design

General General

Automated detection of lameness in sheep using machine learning approaches: novel insights into behavioural differences among lame and non-lame sheep.

In Royal Society open science

Lameness in sheep is the biggest cause of concern regarding poor health and welfare among sheep-producing countries. Best practice for lameness relies on rapid treatment, yet there are no objective measures of lameness detection. Accelerometers and gyroscopes have been widely used in human activity studies and their use is becoming increasingly common in livestock. In this study, we used 23 datasets (10 non-lame and 13 lame sheep) from an accelerometer- and gyroscope-based ear sensor with a sampling frequency of 16 Hz to develop and compare algorithms that can differentiate lameness within three different activities (walking, standing and lying). We show for the first time that features extracted from accelerometer and gyroscope signals can differentiate between lame and non-lame sheep while standing, walking and lying. The random forest algorithm performed best for classifying lameness with an accuracy of 84.91% within lying, 81.15% within standing and 76.83% within walking and overall correctly classified over 80% sheep within activities. Both accelerometer- and gyroscope-based features ranked among the top 10 features for classification. Our results suggest that novel behavioural differences between lame and non-lame sheep across all three activities could be used to develop an automated system for lameness detection.

Kaler Jasmeet, Mitsch Jurgen, Vázquez-Diosdado Jorge A, Bollard Nicola, Dottorini Tania, Ellis Keith A

2020-Jan

behaviour, lameness, machine learning, precision livestock farming, sensor, signal processing

General General

Gene expression profiling identified TP53MutPIK3CAWild as a potential biomarker for patients with triple-negative breast cancer treated with immune checkpoint inhibitors.

In Oncology letters

Triple-negative breast cancer (TNBC) accounts for 15-30% of all breast cancer cases and is clinically difficult to treat due to the lack of hormone or human epidermal growth factor receptor 2 receptors, which are usually targeted by the most successful therapeutic approaches. Immune checkpoint inhibitors (ICIs) have offered long-term survival benefits in several types of solid tumors, however with low response rates. Thus, there is an urgent need to develop feasible biomarkers for identifying patients with TNBC, who are responsive. The present study demonstrated that the immune microenvironment of TNBC has the highest expression of immunoregulatory molecules among all pathologic types. The tumor mutation burden (TMB) of TNBC was not strongly correlated with cytolytic activity and showed no significant associations with different degrees of immune cell infiltration and TMB. The machine learning method divided patients with TNBC into two groups characterized by 'hot' and 'cold' tumors, according to whether immune-associated genes were highly expressed, and different responses to immunotherapy were seen between these two groups. Furthermore, patients with a TP53MutPIK3CAWild genotype demonstrated favorable immunotherapy-responsive signatures and may have improved outcomes with ICIs. In conclusion, the present study revealed that TP53 and PIK3CA may be appropriate biomarkers to screen for patients who would benefit most from ICIs, which could guide precise immunotherapy for patients with TNBC.

Cheng Jia’Nan, Ding Xiaofang, Xu Shouxia, Zhu Bo, Jia Qingzhu

2020-Apr

TP53MutPIK3CAWild, biomarker, immune checkpoint inhibitors, triple-negative breast cancer

General General

Using Artificial Intelligence in Infection Prevention.

In Current treatment options in infectious diseases

Purpose of Review : Artificial intelligence (AI) offers huge potential in infection prevention and control (IPC). We explore its potential IPC benefits in epidemiology, laboratory infection diagnosis, and hand hygiene.

Recent Findings : AI has the potential to detect transmission events during outbreaks or predict high-risk patients, enabling development of tailored IPC interventions. AI offers opportunities to enhance diagnostics with objective pattern recognition, standardize the diagnosis of infections with IPC implications, and facilitate the dissemination of IPC expertise. AI hand hygiene applications can deliver behavior change, though it requires further evaluation in different clinical settings. However, staff can become dependent on automatic reminders, and performance returns to baseline if feedback is removed.

Summary : Advantages for IPC include speed, consistency, and capability of handling infinitely large datasets. However, many challenges remain; improving the availability of high-quality representative datasets and consideration of biases within preexisting databases are important challenges for future developments. AI in itself will not improve IPC; this requires culture and behavior change. Most studies to date assess performance retrospectively so there is a need for prospective evaluation in the real-life, often chaotic, clinical setting. Close collaboration with IPC experts to interpret outputs and ensure clinical relevance is essential.

Fitzpatrick Fidelma, Doherty Aaron, Lacey Gerard

2020-Mar-19

Artificial intelligence, Epidemiology, Hand hygiene, Infection diagnosis, Infection prevention and control, Machine learning

General General

Big tech and societal sustainability: an ethical framework.

In AI & society

Sustainability is typically viewed as consisting of three forces, economic, social, and ecological, in tension with one another. In this paper, we address the dangers posed to societal sustainability. The concern being addressed is the very survival of societies where the rights of individuals, personal and collective freedoms, an independent judiciary and media, and democracy, despite its messiness, are highly valued. We argue that, as a result of various technological innovations, a range of dysfunctional impacts are threatening social and political stability. For instance, robotics and automation are replacing human labor and decision-making in a range of industries; search engines, monetized through advertising, have access to, and track, our interests and preferences; social media, in connecting us to one another often know more about us than we ourselves do, enabling them to profit in ways which may not coincide with our well-being; online retailers have not only acquired the ability to track and predict our buying choices, but also they can squeeze vendors based on their outsize bargaining power; and, in general, virtual technologies have changed both the way we think and our sense of self. With the rising deployment of the Internet of Things, and developments in machine learning and artificial intelligence, the threats to individual freedoms and rights, societal cohesion and harmony, employment and economic well-being, and trust in democracy are being ratcheted up. This paper lauds the benefits and addresses the harm wrought by the high tech giants in Information and Communication Technologies (ICTs). The search for rapidly growing revenues (and shareholder returns and stock prices) drives firms to accelerate product innovation without fully investigating the entire gamut of their impacts. As greater wealth accrues to the leaders of tech firms, inequalities within firms and societies are widening, creating social tensions and political ferment. We explore the ethical nature of the challenge employing a simple utilitarian calculus, complemented by approaches rooted in rights, justice, and the common good. Various options to address the challenges posed by ICTs are considered and evaluated. We argue that regulation may do little more than slow down the damage to society, particularly since societal values and political preferences vary internationally. Firms need to establish ethical standards, imbuing the upholders of these standards with sufficient authority, while creating a culture of morality. User involvement and activism, and shareholders' concerns for the sustainability of societies on whose continued prosperity they depend, are imperative to humanity's ability to decide the future direction of technology.

Arogyaswamy Bernard

2020-Mar-19

Big tech, Centralized power, Cognition, Ethical criteria, Social impacts, User activism

Pathology Pathology

Automated detection algorithm for C4d immunostaining showed comparable diagnostic performance to pathologists in renal allograft biopsy.

In Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc

A deep learning-based image analysis could improve diagnostic accuracy and efficiency in pathology work. Recently, we proposed a deep learning-based detection algorithm for C4d immunostaining in renal allografts. The objective of this study is to assess the diagnostic performance of the algorithm by comparing pathologists' diagnoses and analyzing the associations of the algorithm with clinical data. C4d immunostaining slides of renal allografts were obtained from two different institutions (100 slides from the Asan Medical Center and 86 slides from the Seoul National University Hospital) and scanned using two different slide scanners. Three pathologists and the algorithm independently evaluated each slide according to the Banff 2017 criteria. Subsequently, they jointly reviewed the results for consensus scoring. The result of the algorithm was compared with that of each pathologist and the consensus diagnosis. Clinicopathological associations of the results of the algorithm with allograft survival, histologic evidence of microvascular inflammation, and serologic results for donor-specific antibodies were also analyzed. As a result, the reproducibility between the pathologists was fair to moderate (kappa 0.36-0.54), which is comparable to that between the algorithm and each pathologist (kappa 0.34-0.51). The C4d scores predicted by the algorithm achieved substantial concordance with the consensus diagnosis (kappa = 0.61), and they were significantly associated with remarkable microvascular inflammation (P = 0.001), higher detection rate of donor-specific antibody (P = 0.003), and shorter graft survival (P < 0.001). In conclusion, the deep learning-based C4d detection algorithm showed a diagnostic performance similar to that of the pathologists.

Choi Gyuheon, Kim Young-Gon, Cho Haeyon, Kim Namkug, Lee Hyunna, Moon Kyung Chul, Go Heounjeong

2020-Mar-26

Pathology Pathology

MLCD: A Unified Software Package for Cancer Diagnosis.

In JCO clinical cancer informatics

PURPOSE : Machine Learning Package for Cancer Diagnosis (MLCD) is the result of a National Institutes of Health/National Cancer Institute (NIH/NCI)-sponsored project for developing a unified software package from state-of-the-art breast cancer biopsy diagnosis and machine learning algorithms that can improve the quality of both clinical practice and ongoing research.

METHODS : Whole-slide images of 240 well-characterized breast biopsy cases, initially assembled under R01 CA140560, were used for developing the algorithms and training the machine learning models. This software package is based on the methodology developed and published under our recent NIH/NCI-sponsored research grant (R01 CA172343) for finding regions of interest (ROIs) in whole-slide breast biopsy images, for segmenting ROIs into histopathologic tissue types and for using this segmentation in classifiers that can suggest final diagnoses.

RESULT : The package provides an ROI detector for whole-slide images and modules for semantic segmentation into tissue classes and diagnostic classification into 4 classes (benign, atypia, ductal carcinoma in situ, invasive cancer) of the ROIs. It is available through the GitHub repository under the Massachusetts Institute of Technology license and will later be distributed with the Pathology Image Informatics Platform system. A Web page provides instructions for use.

CONCLUSION : Our tools have the potential to provide help to other cancer researchers and, ultimately, to practicing physicians and will motivate future research in this field. This article describes the methodology behind the software development and gives sample outputs to guide those interested in using this package.

Wu Wenjun, Li Beibin, Mercan Ezgi, Mehta Sachin, Bartlett Jamen, Weaver Donald L, Elmore Joann G, Shapiro Linda G

2020-Mar

General General

Relative posture between head and finger determines perceived tactile direction of motion.

In Scientific reports ; h5-index 158.0

The hand explores the environment for obtaining tactile information that can be fruitfully integrated with other functions, such as vision, audition, and movement. In theory, somatosensory signals gathered by the hand are accurately mapped in the world-centered (allocentric) reference frame such that the multi-modal information signals, whether visual-tactile or motor-tactile, are perfectly aligned. However, an accumulating body of evidence indicates that the perceived tactile orientation or direction is inaccurate; yielding a surprisingly large perceptual bias. To investigate such perceptual bias, this study presented tactile motion stimuli to healthy adult participants in a variety of finger and head postures, and requested the participants to report the perceived direction of motion mapped on a video screen placed on the frontoparallel plane in front of the eyes. Experimental results showed that the perceptual bias could be divided into systematic and nonsystematic biases. Systematic bias, defined as the mean difference between the perceived and veridical directions, correlated linearly with the relative posture between the finger and the head. By contrast, nonsystematic bias, defined as minor difference in bias for different stimulus directions, was highly individualized, phase-locked to stimulus orientation presented on the skin. Overall, the present findings on systematic bias indicate that the transformation bias among the reference frames is dominated by the finger-to-head posture. Moreover, the highly individualized nature of nonsystematic bias reflects how information is obtained by the orientation-selective units in the S1 cortex.

Chen Yueh-Peng, Yeh Chun-I, Lee Tsung-Chi, Huang Jian-Jia, Pei Yu-Cheng

2020-Mar-26

General General

Artificial Intelligence and Machine learning based prediction of resistant and susceptible mutations in Mycobacterium tuberculosis.

In Scientific reports ; h5-index 158.0

Tuberculosis (TB), an infectious disease caused by Mycobacterium tuberculosis (M.tb), causes highest number of deaths globally for any bacterial disease necessitating novel diagnosis and treatment strategies. High-throughput sequencing methods generate a large amount of data which could be exploited in determining multi-drug resistant (MDR-TB) associated mutations. The present work is a computational framework that uses artificial intelligence (AI) based machine learning (ML) approaches for predicting resistance in the genes rpoB, inhA, katG, pncA, gyrA and gyrB for the drugs rifampicin, isoniazid, pyrazinamide and fluoroquinolones. The single nucleotide variations were represented by several sequence and structural features that indicate the influence of mutations on the target protein coded by each gene. We used ML algorithms - naïve bayes, k nearest neighbor, support vector machine, and artificial neural network, to build the prediction models. The classification models had an average accuracy of 85% across all examined genes and were evaluated on an external unseen dataset to demonstrate their application. Further, molecular docking and molecular dynamics simulations were performed for wild type and predicted resistance causing mutant protein and anti-TB drug complexes to study their impact on the conformation of proteins to confirm the observed phenotype.

Jamal Salma, Khubaib Mohd, Gangwar Rishabh, Grover Sonam, Grover Abhinav, Hasnain Seyed E

2020-Mar-26

General General

Food Liking-Based Diet Quality Indexes (DQI) Generated by Conceptual and Machine Learning Explained Variability in Cardiometabolic Risk Factors in Young Adults.

In Nutrients ; h5-index 86.0

The overall pattern of a diet (diet quality) is recognized as more important to health and chronic disease risk than single foods or food groups. Indexes of diet quality can be derived theoretically from evidence-based recommendations, empirically from existing datasets, or a combination of the two. We used these methods to derive diet quality indexes (DQI), generated from a novel dietary assessment, and to evaluate relationships with cardiometabolic risk factors in young adults with (n = 106) or without (n = 106) diagnosed depression (62% female, mean age = 21). Participants completed a liking survey (proxy for usual dietary consumption). Principle component analysis of plasma (insulin, glucose, lipids) and adiposity (BMI, Waist-to-Hip ratio) measures formed a continuous cardiometabolic risk factor score (CRFS). DQIs were created: theoretically (food/beverages grouped, weighted conceptually), empirically (grouping by factor analysis, weights empirically-derived by ridge regression analysis of CRFS), and hybrid (food/beverages conceptually-grouped, weights empirically-derived). The out-of-sample CRFS predictability for the DQI was assessed by two-fold and five-fold cross validations. While moderate consistencies between theoretically- and empirically-generated weights existed, the hybrid outperformed theoretical and empirical DQIs in cross validations (five-fold showed DQI explained 2.6% theoretical, 2.7% empirical, and 6.5% hybrid of CRFS variance). These pilot data support a liking survey that can generate reliable/valid DQIs that are significantly associated with cardiometabolic risk factors, especially theoretically- plus empirically-derived DQI.

Xu Ran, Blanchard Bruce E, McCaffrey Jeanne M, Woolley Stephen, Corso Lauren M L, Duffy Valerie B

2020-Mar-25

cardiometabolic health, diet, diet quality, food preference, metabolic syndrome, principal component analysis, ridge regression analysis, sweets, vegetables, young adult

General General

The Study of Multiple Diagnosis Models of Human Prostate Cancer Based on Taylor Database by Artificial Neural Networks.

In Journal of the Chinese Medical Association : JCMA

BACKGROUND : Prostate cancer (PCa) is the most common malignancy seen in men and the second leading cause of cancer-related death in males. The incidence and mortality associated with PCa has been rapidly increasing in China recently.

METHODS : Multiple diagnostic models of human PCa were developed based on Taylor database by combining the artificial neural networks (ANNs) to enhance the ability of PCa diagnosis. Genetic algorithm (GA) is used to select feature genes as numerical encoded parameters that reflect cancer, metastatic or normal samples. Back propagation (BP) neural network and learning vector quantization (LVQ) neural network were used to build different Cancer/Normal, Primary/Metastatic and Gleason Grade diagnostic models.

RESULTS : The performance of these modeling approaches was evaluated by predictive accuracy (ACC) and area under the ROC curve (AUC). By observing the statistically significant parameters of the three training sets, our Cancer/Normal, Primary/Metastatic and Gleason Grade models' with ACC and AUC can be drawn (97.33%, 0.9832), (99.17%, 0.9952), and (90.48%, 0.8742), respectively.

CONCLUSION : These results indicated that our diagnostic models of human PCa based on Taylor database combining the feature gene expression profiling data and artificial intelligence algorithms might act as a powerful tool for diagnosing PCa. Gleason Grade diagnostic models were used as novel prognostic diagnosis models for biochemical recurrence-free survival and overall survival, which might be helpful in the prognostic diagnosis of PCa in patients.

Jiang Fu-Neng, Dai Li-Jun, Wu Yong-Ding, Yang Sheng-Bang, Liang Yu-Xiang, Zhang Xin, Zou Cui-Yun, He Ren-Qiang, Xu Xiao-Ming, Zhong Wei-De

2020-Mar-11

Radiology Radiology

Deep learning in medical image registration: a review.

In Physics in medicine and biology

This paper presents a review of deep learning (DL) based medical image registration methods. We summarized the latest developments and applications of DL-based registration methods in the medical field. These methods were classified into seven categories according to their methods, functions and popularity. A detailed review of each category was presented, highlighting important contributions and identifying specific challenges. A short assessment was presented following the detailed review of each category to summarize its achievements and future potentials. We provided a comprehensive comparison among DL-based methods for lung and brain registration using benchmark datasets. Lastly, we analyzed the statistics of all the cited works from various aspects, revealing the popularity and future trend of DL-based medical image registration.

Fu Yabo, Lei Yang, Wang Tonghe, Curran Walter J, Liu Tian, Yang Xiaofeng

2020-Mar-27

deep learning, medical image registration, review

General General

A Comprehensive Survey on Graph Neural Networks.

In IEEE transactions on neural networks and learning systems

Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications, where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects. The complexity of graph data has imposed significant challenges on the existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. In this article, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. We propose a new taxonomy to divide the state-of-the-art GNNs into four categories, namely, recurrent GNNs, convolutional GNNs, graph autoencoders, and spatial-temporal GNNs. We further discuss the applications of GNNs across various domains and summarize the open-source codes, benchmark data sets, and model evaluation of GNNs. Finally, we propose potential research directions in this rapidly growing field.

Wu Zonghan, Pan Shirui, Chen Fengwen, Long Guodong, Zhang Chengqi, Yu Philip S

2020-Mar-24

General General

Synthetic Elastography using B-mode Ultrasound through a Deep Fully-Convolutional Neural Network.

In IEEE transactions on ultrasonics, ferroelectrics, and frequency control

Shear-wave elastography (SWE) permits local estimation of tissue elasticity, an important imaging marker in biomedicine. This recently-developed, advanced technique assesses the speed of a laterally-travelling shear wave after an acoustic radiation force "push" to estimate local Young's moduli in an operator-independent fashion. In this work, we show how synthetic SWE (sSWE) images can be generated based on conventional B-mode imaging through deep learning. Using sideby-side-view B-mode/SWE images collected in 50 patients with prostate cancer, we show that sSWE images with a pixel-wise mean absolute error of 4.5±0.96 kPa with regard to the original SWE can be generated. Visualization of high-level feature levels through t-Distributed Stochastic Neighbor Embedding reveals substantial overlap between data from two different scanners. Qualitatively, we examined the use of the sSWE methodology for B-mode images obtained with a scanner without SWE functionality. We also examined the use of this type of network in elasticity imaging in the thyroid. Limitations of the technique reside in the fact that networks have to be retrained for different organs, and that the method requires standardization of the imaging settings and procedure. Future research will be aimed at development of sSWE as an elasticity-related tissue typing strategy that is solely based on B-mode ultrasound acquisition, and the examination of its clinical utility.

Wildeboer R R, Van Sloun R J G, Mannaerts C K, Moraes P H, Salomon G, Chammas M C, Wijkstra H, Mischi M

2020-Mar-24

General General

Spatially-Constrained Fisher Representation for Brain Disease Identification with Incomplete Multi-Modal Neuroimages.

In IEEE transactions on medical imaging ; h5-index 74.0

Multi-modal neuroimages, such as magnetic resonance imaging (MRI) and positron emission tomography (PET), can provide complementary structural and functional information of the brain, thus facilitating automated brain disease identification. Incomplete data problem is unavoidable in multi-modal neuroimage studies due to patient dropouts and/or poor data quality. Conventional methods usually discard data-missing subjects, thus significantly reducing the number of training samples. Even though several deep learning methods have been proposed, they usually rely on pre-defined regions-of-interest in neuroimages, requiring disease-specific expert knowledge. To this end, we propose a spatially-constrained Fisher representation framework for brain disease diagnosis with incomplete multi-modal neuroimages. We first impute missing PET images based on their corresponding MRI scans using a hybrid generative adversarial network. With the complete (after imputation) MRI and PET data, we then develop a spatially-constrained Fisher representation network to extract statistical descriptors of neuroimages for disease diagnosis, assuming that these descriptors follow a Gaussian mixture model with a strong spatial constraint (i.e., images from different subjects have similar anatomical structures). Experimental results on three databases suggest that our method can synthesize reasonable neuroimages and achieve promising results in brain disease identification, compared with several state-of-the-art methods.

Pan Yongsheng, Liu Mingxia, Lian Chunfeng, Xia Yong, Shen Dinggang

2020-Mar-24

General General

Deep Learning for Image Super-resolution: A Survey.

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

Image Super-Resolution (SR) is an important class of image processing techniques to enhance the resolution of images and videos in computer vision. Recent years have witnessed remarkable progress of image super-resolution using deep learning techniques. In this survey, we aim to give a survey on recent advances of image super-resolution techniques using deep learning approaches in a systematic way. In general, we can roughly group the existing studies of SR techniques into three major categories: supervised SR, unsupervised SR, and domain-specific SR. In addition, we also cover some other important issues, such as publicly available benchmark datasets and performance evaluation metrics. Finally, we conclude this survey by highlighting several future directions and open issues which should be further addressed by the community in the future.

Wang Zhihao, Chen Jian, Hoi Steven C H

2020-Mar-23

General General

Heterogeneity of executive function revealed by a functional random forest approach across ADHD and ASD.

In NeuroImage. Clinical

BACKGROUND : Those with autism spectrum disorder (ASD) and/or attention-deficit-hyperactivity disorder (ADHD) exhibit symptoms of hyperactivity and inattention, causing significant hardships for families and society. A potential mechanism involved in these conditions is atypical executive function (EF). Inconsistent findings highlight that EF features may be shared or distinct across ADHD and ASD. With ADHD and ASD each also being heterogeneous, we hypothesized that there may be nested subgroups across disorders with shared or unique underlying mechanisms.

METHODS : Participants (N = 130) included adolescents aged 7-16 with ASD (n = 64) and ADHD (n = 66). Typically developing (TD) participants (n = 28) were included for a comparative secondary sub-group analysis. Parents completed the K-SADS and youth completed an extended battery of executive and other cognitive measures. A two stage hybrid machine learning tool called functional random forest (FRF) was applied as a classification approach and then subsequently to subgroup identification. We input 43 EF variables to the classification step, a supervised random forest procedure in which the features estimated either hyperactive or inattentive ADHD symptoms per model. The FRF then produced proximity matrices and identified optimal subgroups via the infomap algorithm (a type of community detection derived from graph theory). Resting state functional connectivity MRI (rs-fMRI) was used to evaluate the neurobiological validity of the resulting subgroups.

RESULTS : Both hyperactive (Mean absolute error (MAE) = 0.72, Null model MAE = 0.8826, (t(58) = -4.9, p < .001) and inattentive (MAE = 0.7, Null model MAE = 0.85, t(58) = -4.4, p < .001) symptoms were predicted better than chance by the EF features selected. Subgroup identification was robust (Hyperactive: Q = 0.2356, p < .001; Inattentive: Q = 0.2350, p < .001). Two subgroups representing severe and mild symptomology were identified for each symptom domain. Neuroimaging data revealed that the subgroups and TD participants significantly differed within and between multiple functional brain networks, but no consistent "severity" patterns of over or under connectivity were observed between subgroups and TD.

CONCLUSION : The FRF estimated hyperactive/inattentive symptoms and identified 2 distinct subgroups per model, revealing distinct neurocognitive profiles of Severe and Mild EF performance per model. Differences in functional connectivity between subgroups did not appear to follow a severity pattern based on symptom expression, suggesting a more complex mechanistic interaction that cannot be attributed to symptom presentation alone.

Cordova Michaela, Shada Kiryl, Demeter Damion V, Doyle Olivia, Miranda-Dominguez Oscar, Perrone Anders, Schifsky Emma, Graham Alice, Fombonne Eric, Langhorst Beth, Nigg Joel, Fair Damien A, Feczko Eric

2020-Mar-16

ADHD, ASD, Executive function, Machine learning, rs-fMRI

General General

Predicting Adverse Outcomes for Febrile Patients in the Emergency Department Using Sparse Laboratory Data: Development of a Time Adaptive Model.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : A timely decision in the initial stages for patients with an acute illness is important. However, only a few studies have determined the prognosis of patients based on insufficient laboratory data during the initial stages of treatment.

OBJECTIVE : This study aimed to develop and validate time adaptive prediction models to predict the severity of illness in the emergency department (ED) using highly sparse laboratory test data (test order status and test results) and a machine learning approach.

METHODS : This retrospective study used ED data from a tertiary academic hospital in Seoul, Korea. Two different models were developed based on laboratory test data: order status only (OSO) and order status and results (OSR) models. A binary composite adverse outcome was used, including mortality or hospitalization in the intensive care unit. Both models were evaluated using various performance criteria, including the area under the receiver operating characteristic curve (AUC) and balanced accuracy (BA). Clinical usefulness was examined by determining the positive likelihood ratio (PLR) and negative likelihood ratio (NLR).

RESULTS : Of 9491 eligible patients in the ED (mean age, 55.2 years, SD 17.7 years; 4839/9491, 51.0% women), the model development cohort and validation cohort included 6645 and 2846 patients, respectively. The OSR model generally exhibited better performance (AUC=0.88, BA=0.81) than the OSO model (AUC=0.80, BA=0.74). The OSR model was more informative than the OSO model to predict patients at low or high risk of adverse outcomes (P<.001 for differences in both PLR and NLR).

CONCLUSIONS : Early-stage adverse outcomes for febrile patients could be predicted using machine learning models of highly sparse data including test order status and laboratory test results. This prediction tool could help medical professionals who are simultaneously treating the same patient share information, lead dynamic communication, and consequently prevent medical errors.

Lee Sungjoo, Hong Sungjun, Cha Won Chul, Kim Kyunga

2020-Mar-26

adverse outcome, emergency department, imbalanced data, machine learning, order status, sparse laboratory data, time adaptive model

General General

Accurate Measurement of Handwash Quality Using Sensor Armbands: Instrument Validation Study.

In JMIR mHealth and uHealth

BACKGROUND : Hand hygiene is a crucial and cost-effective method to prevent health care-associated infections, and in 2009, the World Health Organization (WHO) issued guidelines to encourage and standardize hand hygiene procedures. However, a common challenge in health care settings is low adherence, leading to low handwashing quality. Recent advances in machine learning and wearable sensing have made it possible to accurately measure handwashing quality for the purposes of training, feedback, or accreditation.

OBJECTIVE : We measured the accuracy of a sensor armband (Myo armband) in detecting the steps and duration of the WHO procedures for handwashing and handrubbing.

METHODS : We recruited 20 participants (10 females; mean age 26.5 years, SD 3.3). In a semistructured environment, we collected armband data (acceleration, gyroscope, orientation, and surface electromyography data) and video data from each participant during 15 handrub and 15 handwash sessions. We evaluated the detection accuracy for different armband placements, sensor configurations, user-dependent vs user-independent models, and the use of bootstrapping.

RESULTS : Using a single armband, the accuracy was 96% (SD 0.01) for the user-dependent model and 82% (SD 0.08) for the user-independent model. This increased when using two armbands to 97% (SD 0.01) and 91% (SD 0.04), respectively. Performance increased when the armband was placed on the forearm (user dependent: 97%, SD 0.01; and user independent: 91%, SD 0.04) and decreased when placed on the arm (user dependent: 96%, SD 0.01; and user independent: 80%, SD 0.06). In terms of bootstrapping, user-dependent models can achieve more than 80% accuracy after six training sessions and 90% with 16 sessions. Finally, we found that the combination of accelerometer and gyroscope minimizes power consumption and cost while maximizing performance.

CONCLUSIONS : A sensor armband can be used to measure hand hygiene quality relatively accurately, in terms of both handwashing and handrubbing. The performance is acceptable using a single armband worn in the upper arm but can substantially improve by placing the armband on the forearm or by using two armbands.

Wang Chaofan, Sarsenbayeva Zhanna, Chen Xiuge, Dingler Tilman, Goncalves Jorge, Kostakos Vassilis

2020-Mar-26

hand hygiene, machine learning, wearable devices

Pathology Pathology

Image analysis and artificial intelligence in infectious disease diagnostics.

In Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases

BACKGROUND : Microbiologists are valued for their time-honed skills in image analysis including identification of pathogens and inflammatory context in Gram stains, ova and parasite preparations, blood smears, and histopathological slides. They also must classify colonial growth on a variety of agar plates for triage and workup. Recent advances in image analysis, in particular application of artificial intelligence (AI), have the potential to automate these processes and support more timely and accurate diagnoses.

OBJECTIVES : To review current artificial intelligence-based image analysis as applied to clinical microbiology and discuss future trends in the field.

SOURCES : Material sourced for this review included peer-reviewed literature annotated in the PubMed or Google Scholar databases and preprint articles from bioRxiv. Articles describing use of AI for analysis of images used in infectious disease diagnostics were reviewed.

CONTENT : We describe application of machine learning towards analysis of different types of microbiological image data. Specifically, we outline progress in smear and plate interpretation and potential for AI diagnostic applications in the clinical microbiology laboratory.

IMPLICATIONS : Combined with automation, we predict that AI algorithms will be used in the future to pre-screen and pre-classify image data, thereby increasing productivity and enabling more accurate diagnoses through collaboration between the AI and microbiologist. Once developed, image-based AI analysis is inexpensive and amenable to local and remote diagnostic use.

Smith Kenneth P, Kirby James E

2020-Mar-22

Radiology Radiology

Automated voxel- and region-based analysis of gray matter and cerebrospinal fluid space in primary dementia disorders.

In Brain research

PURPOSE : Previous studies showed voxel-based volumetry as a helpful tool in detecting pathologic brain atrophy. Aim of this study was to investigate whether the inclusion of CSF volume improves the imaging based diagnostic accuracy by using combined automated voxel- and region-based volumetry.

METHODS : In total, 120 individuals (30 healthy elderly, 30 frontotemporal dementia (FTD), 30 Alzheimer's dementia (AD) and 30 Lewy body dementia (LBD) patients) were analyzed with voxel-based morphometry and compared to a reference group of 360 healthy elderly controls. Abnormal GM and CSF volumes were visualized via z-scores. Volumetric results were finally evaluated by ROC analyses.

RESULTS : Based on the volume of abnormal GM and CSF voxels high accuracy was shown in separating dementia from normal ageing (AUC 0.93 and 0.91, respectively) within 5 different brain regions per hemisphere (frontal, medial temporal, temporal, parietal, occipital). Accuracy for separating FTD and AD was higher based on CSF volume (FTD: AUC 0.80 vs. 0.75 in frontal regions; AD: AUC 0.78 vs. 0.68 in parietal regions based on CSF and GM respectively).

CONCLUSIONS : Differentiation of dementia patients from normal ageing persons shows high accuracy when based on automatic volumetry alone. Using volumes of abnormal CSF performed better than volumes of abnormal GM, especially in AD and FTD patients.

Egger Karl, Rau Alexander, Yang Shan, Klöppel Stefan, Abdulkadir Ahmed, Kellner Elias, Frings Lars, Hellwig Sabine, Urbach Horst

2020-Mar-22

Alzheimeŕs disease, Dementia, Lewy body dementia, frontotemporal dementia, machine learning, volumetry

General General

Adversarial autoencoder for visualization and classification of human activity: Application to a low-cost commercial force plate.

In Journal of biomechanics

The ability to visualize and interpret high dimensional time-series data will be critical as wearable and other sensors are adopted in rehabilitation protocols. This study proposes a latent space representation of high dimensional time-series data for data visualization. For that purpose, a deep learning model called Adversarial AutoEncoder (AAE) is proposed to perform efficient data dimensionality reduction by considering unsupervised and semi-supervised adversarial training. Eighteen subjects were recruited for the experiment and performed two sets of exercises (upper and lower body) on the Wii Balance Board. Then, the accuracy of the latent space representation is evaluated on both sets of exercises separately. Data dimensionality reduction with conventional Machine Learning (ML) and supervised Deep Learning (DL) classification are also performed to compare the efficiency of AAE approaches. The results showed that AAE can outperform conventional ML approaches while providing close results to DL supervised classification. AAE approaches for data visualization are a promising approach to monitor the subject's movements and detect adverse events or similarity with previous data, providing an intuitive way to monitor the patient's progress and provide potential information for rehabilitation tracking.

Hernandez Vincent, Kulić Dana, Venture Gentiane

2020-Feb-26

Adversarial autoencoder, Deep learning, Ground reaction force, Human activity recognition, Machine learning

oncology Oncology

Machine Learning and Mechanistic Modeling for Prediction of Metastatic Relapse in Early-Stage Breast Cancer.

In JCO clinical cancer informatics

PURPOSE : For patients with early-stage breast cancer, predicting the risk of metastatic relapse is of crucial importance. Existing predictive models rely on agnostic survival analysis statistical tools (eg, Cox regression). Here we define and evaluate the predictive ability of a mechanistic model for time to distant metastatic relapse.

METHODS : The data we used for our model consisted of 642 patients with 21 clinicopathologic variables. A mechanistic model was developed on the basis of two intrinsic mechanisms of metastatic progression: growth (parameter α) and dissemination (parameter μ). Population statistical distributions of the parameters were inferred using mixed-effects modeling. A random survival forest analysis was used to select a minimal set of five covariates with the best predictive power. These were further considered to individually predict the model parameters by using a backward selection approach. Predictive performances were compared with classic Cox regression and machine learning algorithms.

RESULTS : The mechanistic model was able to accurately fit the data. Covariate analysis revealed statistically significant association of Ki67 expression with α (P = .001) and EGFR expression with μ (P = .009). The model achieved a c-index of 0.65 (95% CI, 0.60 to 0.71) in cross-validation and had predictive performance similar to that of random survival forest (95% CI, 0.66 to 0.69) and Cox regression (95% CI, 0.62 to 0.67) as well as machine learning classification algorithms.

CONCLUSION : By providing informative estimates of the invisible metastatic burden at the time of diagnosis and forward simulations of metastatic growth, the proposed model could be used as a personalized prediction tool for routine management of patients with breast cancer.

Nicolò Chiara, Périer Cynthia, Prague Melanie, Bellera Carine, MacGrogan Gaëtan, Saut Olivier, Benzekry Sébastien

2020-Mar

General General

Who Benefits Most from Adding Technology to Depression Treatment and How? An Analysis of Engagement with a Texting Adjunct for Psychotherapy.

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

Introduction: Cognitive behavioral therapy (CBT) is an established treatment for depression, but its success is often impeded by low attendance. Supportive text messages assessing participants' mood in between sessions might increase attendance to in-clinic CBT, although it is not fully understood who benefits most from these interventions and how. This study examined (1) user groups showing different profiles of study engagement and (2) associations between increased response rates to mood texts and psychotherapy attendance. Methods: We included 73 participants who attended Group CBT (GCBT) in a primary care clinic and participated in a supportive automated text-messaging intervention. Using unsupervised machine learning, we identified and characterized subgroups with similar combinations of total texting responsiveness and total GCBT attendance. We used mixed-effects models to explore the association between increased previous week response rate and subsequent week in-clinic GCBT attendance and, conversely, response rate following attendance. Results: Participants could be divided into four clusters of overall study engagement, showing distinct profiles in age and prior texting knowledge. The response rate to texts in the week before GCBT was not associated with GCBT attendance, although the relationship was moderated by age; there was a positive relationship for younger, but not older, participants. Attending GCBT was, however, associated with higher response rate the week after an attended session. Conclusion: User groups of study engagement differ in texting knowledge and age. Younger participants might benefit more from supportive texting interventions when their purpose is to increase psychotherapy attendance. Our results have implications for tailoring digital interventions to user groups and for understanding therapeutic effects of these interventions.

Figueroa Caroline A, DeMasi Orianna, Hernandez-Ramos Rosa, Aguilera Adrian

2020-Mar-26

cognitive behavioral therapy, digital literacy, engagement, short messaging service, telehealth

Surgery Surgery

Treatment of UGI bleeding in 2020: new techniques and outcomes.

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

Clinical outcome of upper gastrointestinal bleeding has improved due to advances in endoscopic therapy and standardized peri-endoscopy care. Apart from validated clinical scores, artificial intelligence-assisted machine learning model may play an important role in risk stratification. While standard endoscopic treatments remain irreplaceable, novel endoscopic modalities have changed the landscape of management. Over-the-scope clips have high success rate as rescue or even first line treatment in difficult-to-treat cases. Hemostatic powder is safe and easy-to-use, which can be useful as temporary control with its high immediate hemostatic ability. After endoscopic hemostasis, Doppler endoscopic probe can offer an objective measure to guide treatment endpoint. In refractory bleeding, angiographic embolization should be considered before salvage surgery. In variceal hemorrhage, banding ligation and glue injection are first line treatment options. Endoscopic ultrasound-guided therapy is gaining popularity due to its capability of precise localization for treatment targets. Self-expandable metal stent may be considered as an alternative option of balloon tamponade in refractory bleeding. Transjugular intrahepatic portosystemic shunting should be reserved as salvage therapy. In this article, we aim to provide an evidence-based comprehensive review of the major advancements in endoscopic hemostatic techniques and clinical outcomes.

Louis Ho Shing Lau, Sung Joseph Jy

2020-Mar-26

Radiology Radiology

Radiomic Features of Primary Rectal Cancers on Baseline T2 -Weighted MRI Are Associated With Pathologic Complete Response to Neoadjuvant Chemoradiation: A Multisite Study.

In Journal of magnetic resonance imaging : JMRI

BACKGROUND : Twenty-five percent of rectal adenocarcinoma patients achieve pathologic complete response (pCR) to neoadjuvant chemoradiation and could avoid proctectomy. However, pretreatment clinical or imaging markers are lacking in predicting response to chemoradiation. Radiomic texture features from MRI have recently been associated with therapeutic response in other cancers.

PURPOSE : To construct a radiomics texture model based on pretreatment MRI for identifying patients who will achieve pCR to neoadjuvant chemoradiation in rectal cancer, including validation across multiple scanners and sites.

STUDY TYPE : Retrospective.

SUBJECTS : In all, 104 rectal cancer patients staged with MRI prior to long-course chemoradiation followed by proctectomy; curated from three institutions.

FIELD STRENGTH/SEQUENCE : 1.5T-3.0T, axial higher resolution T2 -weighted turbo spin echo sequence.

ASSESSMENT : Pathologic response was graded on postsurgical specimens. In total, 764 radiomic features were extracted from single-slice sections of rectal tumors on processed pretreatment T2 -weighted MRI.

STATISTICAL TESTS : Three feature selection schemes were compared for identifying radiomic texture descriptors associated with pCR via a discovery cohort (one site, N = 60, cross-validation). The top-selected radiomic texture features were used to train and validate a random forest classifier model for pretreatment identification of pCR (two external sites, N = 44). Model performance was evaluated via area under the curve (AUC), accuracy, sensitivity, and specificity.

RESULTS : Laws kernel responses and gradient organization features were most associated with pCR (P ≤ 0.01); as well as being commonly identified across all feature selection schemes. The radiomics model yielded a discovery AUC of 0.699 ± 0.076 and a hold-out validation AUC of 0.712 with 70.5% accuracy (70.0% sensitivity, 70.6% specificity) in identifying pCR. Radiomic texture features were resilient to variations in magnetic field strength as well as being consistent between two different expert annotations. Univariate analysis revealed no significant associations of baseline clinicopathologic or MRI findings with pCR (P = 0.07-0.96).

DATA CONCLUSION : Radiomic texture features from pretreatment MRIs may enable early identification of potential pCR to neoadjuvant chemoradiation, as well as generalize across sites.

LEVEL OF EVIDENCE : 3 TECHNICAL EFFICACY STAGE: 2.

Antunes Jacob T, Ofshteyn Asya, Bera Kaustav, Wang Erik Y, Brady Justin T, Willis Joseph E, Friedman Kenneth A, Marderstein Eric L, Kalady Matthew F, Stein Sharon L, Purysko Andrei S, Paspulati Rajmohan, Gollamudi Jayakrishna, Madabhushi Anant, Viswanath Satish E

2020-Mar-26

machine learning, pathologic complete response, radiomics, rectal cancer

oncology Oncology

Dosimetric evaluation of synthetic CT generated with GANs for MRI-only proton therapy treatment planning of brain tumors.

In Journal of applied clinical medical physics ; h5-index 28.0

PURPOSE : The purpose of this study was to address the dosimetric accuracy of synthetic computed tomography (sCT) images of patients with brain tumor generated using a modified generative adversarial network (GAN) method, for their use in magnetic resonance imaging (MRI)-only treatment planning for proton therapy.

METHODS : Dose volume histogram (DVH) analysis was performed on CT and sCT images of patients with brain tumor for plans generated for intensity-modulated proton therapy (IMPT). All plans were robustly optimized using a commercially available treatment planning system (RayStation, from RaySearch Laboratories) and standard robust parameters reported in the literature. The IMPT plan was then used to compute the dose on CT and sCT images for dosimetric comparison, using RayStation analytical (pencil beam) dose algorithm. We used a second, independent Monte Carlo dose calculation engine to recompute the dose on both CT and sCT images to ensure a proper analysis of the dosimetric accuracy of the sCT images.

RESULTS : The results extracted from RayStation showed excellent agreement for most DVH metrics computed on the CT and sCT for the nominal case, with a mean absolute difference below 0.5% (0.3 Gy) of the prescription dose for the clinical target volume (CTV) and below 2% (1.2 Gy) for the organs at risk (OARs) considered. This demonstrates a high dosimetric accuracy for the generated sCT images, especially in the target volume. The metrics obtained from the Monte Carlo doses mostly agreed with the values extracted from RayStation for the nominal and worst-case scenarios (mean difference below 3%).

CONCLUSIONS : This work demonstrated the feasibility of using sCT generated with a GAN-based deep learning method for MRI-only treatment planning of patients with brain tumor in intensity-modulated proton therapy.

Kazemifar Samaneh, Barragán Montero Ana M, Souris Kevin, Rivas Sara T, Timmerman Robert, Park Yang K, Jiang Steve, Geets Xavier, Sterpin Edmond, Owrangi Amir

2020-Mar-26

GAN structure, artificial intelligence, brain MRI, deep learning, proton therapy, synthetic CT

General General

A concept for a Japanese regulatory framework for emerging medical devices with frequently modified behavior.

In Clinical and translational science

Recent progress in the Internet of Things and artificial intelligence has made it possible to utilize the vast quantity of personal health records, clinical data, and scientific findings for prognosis, diagnosis, and therapy. These innovative technologies provide new possibilities with the development of medical devices (MDs), whose behaviors can be continuously modified. A novel regulatory framework covering these MDs is now under discussion in Japan. In this review, we introduce the regulatory initiative for MDs, and importance of a paradigm shift from regulation to innovation regarding MDs.

Ota Nagomi, Tachibana Keisuke, Kusakabe Tetsuya, Sanada Shoji, Kondoh Masuo

2020-Mar-26

General General

The Role and Promise of Artificial Intelligence in Medical Toxicology.

In Journal of medical toxicology : official journal of the American College of Medical Toxicology

Artificial intelligence (AI) refers to machines or software that process information and interact with the world as understanding beings. Examples of AI in medicine include the automated reading of chest X-rays and the detection of heart dysrhythmias from wearables. A key promise of AI is its potential to apply logical reasoning at the scale of data too vast for the human mind to comprehend. This scaling up of logical reasoning may allow clinicians to bring the entire breadth of current medical knowledge to bear on each patient in real time. It may also unearth otherwise unreachable knowledge in the attempt to integrate knowledge and research across disciplines. In this review, we discuss two complementary aspects of artificial intelligence: deep learning and knowledge representation. Deep learning recognizes and predicts patterns. Knowledge representation structures and interprets those patterns or predictions. We frame this review around how deep learning and knowledge representation might expand the reach of Poison Control Centers and enhance syndromic surveillance from social media.

Chary Michael A, Manini Alex F, Boyer Edward W, Burns Michele

2020-Mar-25

Artificial intelligence, Big data, Knowledge representation, Machine learning

Surgery Surgery

Heart rate variability as a measure of mental stress in surgery: a systematic review.

In International archives of occupational and environmental health

PURPOSE : There is increasing interest in the use of heart rate variability (HRV) as an objective measurement of mental stress in the surgical setting. To identify areas of improvement, the aim of our study was to review current use of HRV measurements in the surgical setting, evaluate the different methods used for the analysis of HRV, and to assess whether HRV is being measured correctly.

METHODS : A systematic review was performed according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA). 17 studies regarding HRV as a measurement of mental stress in the surgical setting were included and analysed.

RESULTS : 24% of the studies performed long-term measurements (24 h and longer) to assess the long-term effects of and recovery from mental stress. In 24% of the studies, artefact correction took place.

CONCLUSIONS : HRV showed to be a good objective assessment method of stress induced in the workplace environment: it was able to pinpoint stressors during operations, determine which operating techniques induced most stress for surgeons, and indicate differences in stress levels between performing and assisting surgery. For future research, this review recommends using singular guidelines to standardize research, and performing artefact correction. This will improve further evaluation of the long-term effects of mental stress and its recovery.

The Anne-Fleur, Reijmerink Iris, van der Laan Maarten, Cnossen Fokie

2020-Mar-25

Heart rate variability, Mental stress, Occupational stress, Surgery

Public Health Public Health

Mapping the Global Potential Transmission Hotspots for Severe Fever with Thrombocytopenia Syndrome by Machine Learning Methods.

In Emerging microbes & infections ; h5-index 36.0

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease with increasing spread. Currently SFTS transmission has expanded beyond Asian countries, however, with definitive global extents and risk patterns remained obscure. Here we established an exhaustive database that included globally reported locations of human SFTS cases and the competent vector, Haemaphysalis longicornis (H. longicornis), as well as the explanatory environmental variables, based on which, the potential geographic range of H. longicornis and risk areas for SFTS were mapped by applying two machine learning methods. Ten predictors were identified contributing to global distribution for H. longicornis with relative contribution ≥1%. Outside contemporary known distribution, we predict high receptivity to H. longicornis across two continents, including northeastern USA, New Zealand, parts of Australia, and several Pacific islands. Eight key drivers of SFTS cases occurrence were identified, including elevation, predicted probability of H. longicornis presence, two temperature-related factors, two precipitation-related factors, the richness of mammals and percentage coverage of water bodies. The globally model-predicted risk map of human SFTS occurrence was created and validated effective for discriminating the actual affected and unaffected areas (median predictive probability 0.74 vs. 0.04, P<0.001) in three countries with reported cases outside China. The high-risk areas (probability ≥50%) were predicted mainly in east-central China, most parts of the Korean peninsula and southern Japan, and northern New Zealand. Our findings highlight areas where an intensive vigilance for potential SFTS spread or invasion events should be advocated, owing to their high receptibility to H. longicornis distribution.

Miao Dong, Dai Ke, Zhao Guo-Ping, Li Xin-Lou, Shi Wen-Qiang, Zhang Jiu Song, Yang Yang, Liu Wei, Fang Li-Qun

2020-Mar-26

Haemaphysalis longicornis, Severe fever with thrombocytopenia syndrome, distribution, machine learning, modeling, risk assessment, world

Ophthalmology Ophthalmology

Screening Candidates for Refractive Surgery With Corneal Tomographic-Based Deep Learning.

In JAMA ophthalmology ; h5-index 58.0

Importance : Evaluating corneal morphologic characteristics with corneal tomographic scans before refractive surgery is necessary to exclude patients with at-risk corneas and keratoconus. In previous studies, researchers performed screening with machine learning methods based on specific corneal parameters. To date, a deep learning algorithm has not been used in combination with corneal tomographic scans.

Objective : To examine the use of a deep learning model in the screening of candidates for refractive surgery.

Design, Setting, and Participants : A diagnostic, cross-sectional study was conducted at the Zhongshan Ophthalmic Center, Guangzhou, China, with examination dates extending from July 18, 2016, to March 29, 2019. The investigation was performed from July 2, 2018, to June 28, 2019. Participants included 1385 patients; 6465 corneal tomographic images were used to generate the artificial intelligence (AI) model. The Pentacam HR system was used for data collection.

Interventions : The deidentified images were analyzed by ophthalmologists and the AI model.

Main Outcomes and Measures : The performance of the AI classification system.

Results : A classification system centered on the AI model Pentacam InceptionResNetV2 Screening System (PIRSS) was developed for screening potential candidates for refractive surgery. The model achieved an overall detection accuracy of 94.7% (95% CI, 93.3%-95.8%) on the validation data set. Moreover, on the independent test data set, the PIRSS model achieved an overall detection accuracy of 95% (95% CI, 88.8%-97.8%), which was comparable with that of senior ophthalmologists who are refractive surgeons (92.8%; 95% CI, 91.2%-94.4%) (P = .72). In distinguishing corneas with contraindications for refractive surgery, the PIRSS model performed better than the classifiers (95% vs 81%; P < .001) in the Pentacam HR system on an Asian patient database.

Conclusions and Relevance : PIRSS appears to be useful in classifying images to provide corneal information and preliminarily identify at-risk corneas. PIRSS may provide guidance to refractive surgeons in screening candidates for refractive surgery as well as for generalized clinical application for Asian patients, but its use needs to be confirmed in other populations.

Xie Yi, Zhao Lanqin, Yang Xiaonan, Wu Xiaohang, Yang Yahan, Huang Xiaoman, Liu Fang, Xu Jiping, Lin Limian, Lin Haiqin, Feng Qiting, Lin Haotian, Liu Quan

2020-Mar-26

General General

Generative and discriminative model-based approaches to microscopic image restoration and segmentation.

In Microscopy (Oxford, England)

Image processing is one of the most important applications of recent machine learning (ML) technologies. Convolutional neural networks (CNNs), a popular deep learning-based ML architecture, have been developed for image processing applications. However, the application of ML to microscopic images is limited as microscopic images are often 3D/4D, that is, the image sizes can be very large, and the images may suffer from serious noise generated due to optics. In this review, three types of feature reconstruction applications to microscopic images are discussed, which fully utilize the recent advancements in ML technologies. First, multi-frame super-resolution is introduced, based on the formulation of statistical generative model-based techniques such as Bayesian inference. Second, data-driven image restoration is introduced, based on supervised discriminative model-based ML technique. In this application, CNNs are demonstrated to exhibit preferable restoration performance. Third, image segmentation based on data-driven CNNs is introduced. Image segmentation has become immensely popular in object segmentation based on electron microscopy (EM); therefore, we focus on EM image processing.

Ishii Shin, Lee Sehyung, Urakubo Hidetoshi, Kume Hideaki, Kasai Haruo

2020-Mar-26

Bayesian estimation, deep learning, image processing, image segmentation, image super-resolution, maximum likelihood estimation

General General

Detecting Sleep Using Heart Rate and Motion Data from Multisensor Consumer-Grade Wearables, Relative to Wrist Actigraphy and Polysomnography.

In Sleep

STUDY OBJECTIVES : Multisensor wearable consumer devices allowing collection of multiple data sources, such as heart rate and motion, for the evaluation of sleep in the home environment, are increasingly ubiquitous. However, the validity of such devices for sleep assessment has not been directly compared to alternatives such as wrist actigraphy or PSG.

METHODS : Eight participants each completed four nights in a sleep laboratory, equipped with polysomnography (PSG) and several wearable devices. RPSGT-scored PSG served as ground truth for sleep-wake state. Wearable devices providing sleep-wake classification data were compared to PSG at both an epoch-by-epoch and night level. Data from multisensor wearables (Apple Watch and Oura Ring) were compared to data available from ECG and a tri-axial wrist actigraph to evaluate quality and utility of heart rate and motion data. Machine learning methods were used to train and test sleep-wake classifiers, using data from consumer wearables. Quality of classifications derived from devices were compared.

RESULTS : For epoch-by-epoch sleep-wake performance, research devices ranged in d' between 1.771 and 1.874, with sensitivity between 0.912 and 0.982, and specificity between 0.366 and 0.647. Data from multisensor wearables were strongly correlated at an epoch-by-epoch level with reference data sources. Classifiers developed from the multisensor wearable data ranged in d' between 1.827 and 2.347, with sensitivity between 0.883 and 0.977, and specificity between 0.407 and 0.821.

CONCLUSIONS : Data from multisensor consumer wearables is strongly correlated with reference devices at the epoch level and can be used to develop epoch-by-epoch models of sleep-wake rivaling existing research devices.

Roberts Daniel M, Schade Margeaux M, Mathew Gina M, Gartenberg Daniel, Buxton Orfeu M

2020-Mar-26

Actigraphy, Artificial Intelligence, Big Data, Machine Learning, Polysomnography, Smartphone, Wearable

Public Health Public Health

Advanced machine learning methods in psychiatry: an introduction.

In General psychiatry

Mental health questions can be tackled through machine learning (ML) techniques. Apart from the two ML methods we introduced in our previous paper, we discuss two more advanced ML approaches in this paper: support vector machines and artificial neural networks. To illustrate how these ML methods have been employed in mental health, recent research applications in psychiatry were reported.

Wu Tsung-Chin, Zhou Zhirou, Wang Hongyue, Wang Bokai, Lin Tuo, Feng Changyong, Tu Xin M

2020

mental health

General General

Correction: Machine learning methods in psychiatry: a brief introduction.

In General psychiatry

[This corrects the article DOI: 10.1136/gpsych-2019-100171.].

**

2020

General General

Evolving network representation learning based on random walks.

In Applied network science

Large-scale network mining and analysis is key to revealing the underlying dynamics of networks, not easily observable before. Lately, there is a fast-growing interest in learning low-dimensional continuous representations of networks that can be utilized to perform highly accurate and scalable graph mining tasks. A family of these methods is based on performing random walks on a network to learn its structural features and providing the sequence of random walks as input to a deep learning architecture to learn a network embedding. While these methods perform well, they can only operate on static networks. However, in real-world, networks are evolving, as nodes and edges are continuously added or deleted. As a result, any previously obtained network representation will now be outdated having an adverse effect on the accuracy of the network mining task at stake. The naive approach to address this problem is to re-apply the embedding method of choice every time there is an update to the network. But this approach has serious drawbacks. First, it is inefficient, because the embedding method itself is computationally expensive. Then, the network mining task outcome obtained by the subsequent network representations are not directly comparable to each other, due to the randomness involved in the new set of random walks involved each time. In this paper, we propose EvoNRL, a random-walk based method for learning representations of evolving networks. The key idea of our approach is to first obtain a set of random walks on the current state of network. Then, while changes occur in the evolving network's topology, to dynamically update the random walks in reserve, so they do not introduce any bias. That way we are in position of utilizing the updated set of random walks to continuously learn accurate mappings from the evolving network to a low-dimension network representation. Moreover, we present an analytical method for determining the right time to obtain a new representation of the evolving network that balances accuracy and time performance. A thorough experimental evaluation is performed that demonstrates the effectiveness of our method against sensible baselines and varying conditions.

Heidari Farzaneh, Papagelis Manos

2020

Dynamic graph embedding, Dynamic random walks, Evolving networks, Network representation learning

General General

UMUDGA: A dataset for profiling algorithmically generated domain names in botnet detection.

In Data in brief

In computer security, botnets still represent a significant cyber threat. Concealing techniques such as the dynamic addressing and the domain generation algorithms (DGAs) require an improved and more effective detection process. To this extent, this data descriptor presents a collection of over 30 million manually-labeled algorithmically generated domain names decorated with a feature set ready-to-use for machine learning (ML) analysis. This proposed dataset has been co-submitted with the research article "UMUDGA: a dataset for profiling DGA-based botnet" [1], and it aims to enable researchers to move forward the data collection, organization, and pre-processing phases, eventually enabling them to focus on the analysis and the production of ML-powered solutions for network intrusion detection. In this research, we selected 50 among the most notorious malware variants to be as exhaustive as possible. Inhere, each family is available both as a list of domains (generated by executing the malware DGAs in a controlled environment with fixed parameters) and as a collection of features (generated by extracting a combination of statistical and natural language processing metrics).

Zago Mattia, Gil Pérez Manuel, Martínez Pérez Gregorio

2020-Jun

Data, Domain Generation Algorithm (DGA), Machine learning, Natural Language Processing (NLP), Network security

General General

Effects of intense assessment on statistical power in randomized controlled trials: Simulation study on depression.

In Internet interventions

Smartphone-based devices are increasingly recognized to assess disease symptoms in daily life (e.g. ecological momentary assessment, EMA). Despite this development in digital psychiatry, clinical trials are mainly based on point assessments of psychopathology. This study investigated expectable increases in statistical power by intense assessment in randomized controlled trials (RCTs). A simulation study, based on three scenarios and several empirical data sets, estimated power gains of two- or fivefold pre-post-assessment. For each condition, data sets of various effect sizes were generated, and AN(C)OVAs were applied to the sample of interest (N = 50-N = 200). Power increases ranged from 6% to 92%, with higher gains in more underpowered scenarios and with higher number of repeated assessments. ANCOVA profited from a more precise estimation of the baseline covariate, resulting in additional gains in statistical power. Fivefold pre-post EMA resulted in highest absolute statistical power and clearly outperformed traditional questionnaire assessments. For example, ANCOVA of automatized PHQ-9 questionnaire data resulted in absolute power of 55 (for N = 200 and d = 0.3). Fivefold EMA, however, resulted in power of 88.9. Non-parametric and multi-level analyses resulted in comparable outcomes. Besides providing psychological treatment, digital mental health can help optimizing sensitivity in RCT-based research. Intense assessment appears advisable whenever psychopathology needs to be assessed with high precision at pre- and post-assessment (e.g. small sample sizes, small treatment effects, or when applying optimization problems like machine learning). First empiric studies are promising, but more evidence is needed. Simulations for various effects and a short guide for popular power software are provided for study planning.

Schuster Raphael, Schreyer Manuela Larissa, Kaiser Tim, Berger Thomas, Klein Jan Philipp, Moritz Steffen, Laireiter Anton-Rupert, Trutschnig Wolfgang

2020-Apr

General General

An algorithm to compare two-dimensional footwear outsole images using maximum cliques and speeded-up robust feature.

In Statistical analysis and data mining

Footwear examiners are tasked with comparing an outsole impression (Q) left at a crime scene with an impression (K) from a database or from the suspect's shoe. We propose a method for comparing two shoe outsole impressions that relies on robust features (speeded-up robust feature; SURF) on each impression and aligns them using a maximum clique (MC). After alignment, an algorithm we denote MC-COMP is used to extract additional features that are then combined into a univariate similarity score using a random forest (RF). We use a database of shoe outsole impressions that includes images from two models of athletic shoes that were purchased new and then worn by study participants for about 6 months. The shoes share class characteristics such as outsole pattern and size, and thus the comparison is challenging. We find that the RF implemented on SURF outperforms other methods recently proposed in the literature in terms of classification precision. In more realistic scenarios where crime scene impressions may be degraded and smudged, the algorithm we propose-denoted MC-COMP-SURF-shows the best classification performance by detecting unique features better than other methods. The algorithm can be implemented with the R-package shoeprintr.

Park Soyoung, Carriquiry Alicia

2020-Apr

SURF, forensic science, image comparison, machine learning, maximum clique

oncology Oncology

Concordance of Treatment Recommendations for Metastatic Non-Small-Cell Lung Cancer Between Watson for Oncology System and Medical Team.

In Cancer management and research

Objective : The disease complexity of metastatic non-small-cell lung cancer (mNSCLC) makes it difficult for physicians to make clinical decisions efficiently and accurately. The Watson for Oncology (WFO) system of artificial intelligence might help physicians by providing fast and precise treatment regimens. This study measured the concordance of the medical treatment regimens of the WFO system and actual clinical regimens, with the aim of determining the suitability of WFO recommendations for Chinese patients with mNSCLC.

Methods : Retrospective data of mNSCLC patients were input to the WFO, which generated a treatment regimen (WFO regimen). The actual regimen was made by physicians in a medical team for patients (medical-team regimen). The factors influencing the consistency of the two treatment options were analyzed by univariate and multivariate analyses.

Results : The concordance rate was 85.16% between the WFO and medical-team regimens for mNSCLC patients. Logistic regression showed that the concordance differed significantly for various pathological types and gene mutations in two treatment regimens. Patients with adenocarcinoma had a lower rate of "recommended" regimen than those with squamous cell carcinoma. There was a statistically significant difference in EGFR-mutant patients for "not recommended" regimens with inconsistency rate of 18.75%. In conclusion, the WFO regimen has 85.16% consistency rate with medical-team regimen in our treatment center. The different pathological type and different gene mutation markedly influenced the agreement rate of the two treatment regimens.

Conclusion : WFO recommendations have high applicability to mNSCLC patients in our hospital. This study demonstrates that the valuable WFO system may assist the doctors better to determine the accurate and effective treatment regimens for mNSCLC patients in the Chinese medical setting.

You Hai-Sheng, Gao Chun-Xia, Wang Hai-Bin, Luo Sai-Sai, Chen Si-Ying, Dong Ya-Lin, Lyu Jun, Tian Tao

2020

Watson for Oncology, artificial intelligence, concordance, metastatic non-small-cell lung cancer, treatment recommendations

General General

Molecular Identification with Rotational Spectroscopy and Probabilistic Deep Learning.

In The journal of physical chemistry. A

A proof-of-concept framework for identifying molecules of unknown elemental composition and structure using experimental rotational data and probabilistic deep learning is presented. Using a minimal set of input data determined experimentally, we describe four neural network architectures that yield information to assist in the identification of an unknown molecule. The first architecture translates spectroscopic parameters into Coulomb matrix eigenspectra, as a method of recovering chemical and structural information encoded in the rotational spectrum. The eigenspectrum is subsequently used by three deep learning networks to constrain the range of stoichiometries, generate SMILES strings, and predict the most likely functional groups present in the molecule. In each model, we utilize dropout layers as an approximation to Bayesian sampling, which subsequently generates probabilistic predictions from otherwise deterministic models. These models are trained on a modestly sized theoretical dataset comprising ~83,000 unique organic molecules (between 18 and 180 amu) optimized at the ωB97X-D/6-31+G(d) level of theory where the theoretical uncertainty of the spectoscopic constants are well understood and used to further augment training. Since chemical and structural properties depend highly on molecular composition, we divided the dataset into four groups corresponding to pure hydrocarbons, oxygen-bearing, nitrogen-bearing, and both oxygen- and nitrogen-bearing species, training each type of network with one of these categories thus creating "experts" within each domain of molecules. We demonstrate how these models can then be used for practical inference on four molecules, and discuss both the strengths and shortcomings of our approach, and the future directions these architectures can take.

McCarthy Michael C, Lee Kin Long Kelvin

2020-Mar-26

General General

Snorkel: rapid training data creation with weak supervision.

In The VLDB journal : very large data bases : a publication of the VLDB Endowment

Labeling training data is increasingly the largest bottleneck in deploying machine learning systems. We present Snorkel, a first-of-its-kind system that enables users to train state-of-the-art models without hand labeling any training data. Instead, users write labeling functions that express arbitrary heuristics, which can have unknown accuracies and correlations. Snorkel denoises their outputs without access to ground truth by incorporating the first end-to-end implementation of our recently proposed machine learning paradigm, data programming. We present a flexible interface layer for writing labeling functions based on our experience over the past year collaborating with companies, agencies, and research laboratories. In a user study, subject matter experts build models 2.8 × faster and increase predictive performance an average 45.5 % versus seven hours of hand labeling. We study the modeling trade-offs in this new setting and propose an optimizer for automating trade-off decisions that gives up to 1.8 × speedup per pipeline execution. In two collaborations, with the US Department of Veterans Affairs and the US Food and Drug Administration, and on four open-source text and image data sets representative of other deployments, Snorkel provides 132 % average improvements to predictive performance over prior heuristic approaches and comes within an average 3.60 % of the predictive performance of large hand-curated training sets.

Ratner Alexander, Bach Stephen H, Ehrenberg Henry, Fries Jason, Wu Sen, Ré Christopher

2020

Machine learning, Training data, Weak supervision

General General

A novel intelligent fault identification method based on random forests for HVDC transmission lines.

In PloS one ; h5-index 176.0

In order to remedy the current problem of having been buffeted by competing requirements for both protection sensitivity and quick reaction of High Voltage Direct Current (HVDC) transmission lines simultaneously, a new intelligent fault identification method based on Random Forests (RF) for HVDC transmission lines is proposed. S transform is implemented to extract fault current traveling wave of 8 frequencies and calculate the fluctuation index and energy sum ratio, in which the wave index is used to identify internal and external faults, and energy sum ratio is used to identify the positive and negative pole faults occurred on the transmission line. The intelligent fault identification model of RF is established, and the fault characteristic sample set of HVDC transmission lines is constructed by using multi-scale S transform fluctuation index and multi-scale S-transform energy sum ratio. Training and testing have been carried out to identify HVDC transmission line faults. According to theoretical researches and a large number of results of simulation experiments, the proposed intelligent fault identification method based on RF for HVDC transmission lines can effectively solve the problem of protection failure caused by inaccurate identification of traditional traveling wave wavefront or wavefront data loss. It can accurately and quickly realize the identification of internal and external faults and the selection of fault poles under different fault distances and transitional resistances, and has a strong ability to withstand transitional resistance and a strong ability to resist interference.

Wu Hao, Wang Qiaomei, Yu Kunjian, Hu Xiaotao, Ran Maoxia

2020

oncology Oncology

Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data.

In PloS one ; h5-index 176.0

Precision medicine in oncology aims at obtaining data from heterogeneous sources to have a precise estimation of a given patient's state and prognosis. With the purpose of advancing to personalized medicine framework, accurate diagnoses allow prescription of more effective treatments adapted to the specificities of each individual case. In the last years, next-generation sequencing has impelled cancer research by providing physicians with an overwhelming amount of gene-expression data from RNA-seq high-throughput platforms. In this scenario, data mining and machine learning techniques have widely contribute to gene-expression data analysis by supplying computational models to supporting decision-making on real-world data. Nevertheless, existing public gene-expression databases are characterized by the unfavorable imbalance between the huge number of genes (in the order of tenths of thousands) and the small number of samples (in the order of a few hundreds) available. Despite diverse feature selection and extraction strategies have been traditionally applied to surpass derived over-fitting issues, the efficacy of standard machine learning pipelines is far from being satisfactory for the prediction of relevant clinical outcomes like follow-up end-points or patient's survival. Using the public Pan-Cancer dataset, in this study we pre-train convolutional neural network architectures for survival prediction on a subset composed of thousands of gene-expression samples from thirty-one tumor types. The resulting architectures are subsequently fine-tuned to predict lung cancer progression-free interval. The application of convolutional networks to gene-expression data has many limitations, derived from the unstructured nature of these data. In this work we propose a methodology to rearrange RNA-seq data by transforming RNA-seq samples into gene-expression images, from which convolutional networks can extract high-level features. As an additional objective, we investigate whether leveraging the information extracted from other tumor-type samples contributes to the extraction of high-level features that improve lung cancer progression prediction, compared to other machine learning approaches.

López-García Guillermo, Jerez José M, Franco Leonardo, Veredas Francisco J

2020

General General

Alzheimer's disease, mild cognitive impairment, and normal aging distinguished by multi-modal parcellation and machine learning.

In Scientific reports ; h5-index 158.0

A 360-area surface-based cortical parcellation is extended to study mild cognitive impairment (MCI) and Alzheimer's disease (AD) from healthy control (HC) using the joint human connectome project multi-modal parcellation (JHCPMMP) proposed by us. We propose a novel classification method named as JMMP-LRR to accurately identify different stages toward AD by integrating the JHCPMMP with the logistic regression-recursive feature elimination (LR-RFE). In three-group classification, the average accuracy is 89.0% for HC, MCI, and AD compared to previous studies using other cortical separation with the best classification accuracy of 81.5%. By counting the number of brain regions whose feature is in the feature subset selected with JMMP-LRR, we find that five brain areas often appear in the selected features. The five core brain areas are Fusiform Face Complex (L-FFC), Area 10d (L-10d), Orbital Frontal Complex (R-OFC), Perirhinal Ectorhinal (L-PeEc) and Area TG dorsal (L-TGd, R-TGd). The features corresponding to the five core brain areas are used to form a new feature subset for three classifications with the average accuracy of 80.0%. Results demonstrate the importance of the five core brain regions in identifying different stages toward AD. Experiment results show that the proposed method has better accuracy for the classification of HC, MCI, AD, and it also proves that the division of brain regions using JHCPMMP is more scientific and effective than other methods.

Sheng Jinhua, Shao Meiling, Zhang Qiao, Zhou Rougang, Wang Luyun, Xin Yu

2020-Mar-25

General General

Rapid Learning of Earthquake Felt Area and Intensity Distribution with Real-time Search Engine Queries.

In Scientific reports ; h5-index 158.0

Immediately after a destructive earthquake, the real-time seismological community has a major focus on rapidly estimating the felt area and the extent of ground shaking. This estimate provides critical guidance for government emergency response teams to conduct orderly rescue and recovery operations in the damaged areas. While considerable efforts have been made in this direction, it still remains a realistic challenge for gathering macro-seismic data in a timely, accurate and cost-effective manner. To this end, we introduce a new direction to improve the information acquisition through monitoring the real-time information-seeking behaviors in the search engine queries, which are submitted by tens of millions of users after earthquakes. Specifically, we provide a very efficient, robust and machine-learning-assisted method for mapping the user-reported ground shaking distribution through the large-scale analysis of real-time search queries from a dominant search engine in China. In our approach, each query is regarded as a "crowd sensor" with a certain weight of confidence to proactively report the shaking location and extent. By fitting the epicenters of earthquakes occurred in mainland China from 2014 to 2018 with well-designed machine learning models, we can efficiently learn the realistic weight of confidence for each search query and sketch the felt areas and intensity distributions for most of the earthquakes. Indeed, this approach paves the way for using real-time search engine queries to efficiently map earthquake felt area in the regions with a relatively large population of search engine users.

Zhu Hengshu, Sun Ying, Zhao Wenjia, Zhuang Fuzhen, Wang Baoshan, Xiong Hui

2020-Mar-25

General General

Human activity recognition using magnetic induction-based motion signals and deep recurrent neural networks.

In Nature communications ; h5-index 260.0

Recognizing human physical activities using wireless sensor networks has attracted significant research interest due to its broad range of applications, such as healthcare, rehabilitation, athletics, and senior monitoring. There are critical challenges inherent in designing a sensor-based activity recognition system operating in and around a lossy medium such as the human body to gain a trade-off among power consumption, cost, computational complexity, and accuracy. We introduce an innovative wireless system based on magnetic induction for human activity recognition to tackle these challenges and constraints. The magnetic induction system is integrated with machine learning techniques to detect a wide range of human motions. This approach is successfully evaluated using synthesized datasets, laboratory measurements, and deep recurrent neural networks.

Golestani Negar, Moghaddam Mahta

2020-Mar-25

General General

Deriving Lipid Classification Based on Molecular Formulas.

In Metabolites

Despite instrument and algorithmic improvements, the untargeted and accurate assignment of metabolites remains an unsolved problem in metabolomics. New assignment methods such as our SMIRFE algorithm can assign elemental molecular formulas to observed spectral features in a highly untargeted manner without orthogonal information from tandem MS or chromatography. However, for many lipidomics applications, it is necessary to know at least the lipid category or class that is associated with a detected spectral feature to derive a biochemical interpretation. Our goal is to develop a method for robustly classifying elemental molecular formula assignments into lipid categories for an application to SMIRFE-generated assignments. Using a Random Forest machine learning approach, we developed a method that can predict lipid category and class from SMIRFE non-adducted molecular formula assignments. Our methods achieve high average predictive accuracy (>90%) and precision (>83%) across all eight of the lipid categories in the LIPIDMAPS database. Classification performance was evaluated using sets of theoretical, data-derived, and artifactual molecular formulas. Our methods enable the lipid classification of non-adducted molecular formula assignments generated by SMIRFE without orthogonal information, facilitating the biochemical interpretation of untargeted lipidomics experiments. This lipid classification appears insufficient for validating single-spectrum assignments, but could be useful in cross-spectrum assignment validation.

Mitchell Joshua M, Flight Robert M, Moseley Hunter N B

2020-Mar-24

Random Forest, SMIRFE, lipid category, lipidomics, machine learning, metabolomics

Ophthalmology Ophthalmology

Prediction of Suicidal Ideation among Korean Adults Using Machine Learning: A Cross-Sectional Study.

In Psychiatry investigation

OBJECTIVE : Suicidal ideation (SI) precedes actual suicidal event. Thus, it is important for the prevention of suicide to screen the individuals with SI. This study aimed to identify the factors associated with SI and to build prediction models in Korean adults using machine learning methods.

METHODS : The 2010-2013 dataset of the Korea National Health and Nutritional Examination Survey was used as the training dataset (n=16,437), and the subset collected in 2015 was used as the testing dataset (n=3,788). Various machine learning algorithms were applied and compared to the conventional logistic regression (LR)-based model.

RESULTS : Common risk factors for SI included stress awareness, experience of continuous depressive mood, EQ-5D score, depressive disorder, household income, educational status, alcohol abuse, and unmet medical service needs. The prediction performances of the machine learning models, as measured by the area under receiver-operating curve, ranged from 0.794 to 0.877, some of which were better than that of the conventional LR model (0.867). The Bayesian network, LogitBoost with LR, and ANN models outperformed the conventional LR model.

CONCLUSION : A machine learning-based approach could provide better SI prediction performance compared to a conventional LR-based model. These may help primary care physicians to identify patients at risk of SI and will facilitate the early prevention of suicide.

Oh Bumjo, Yun Je-Yeon, Yeo Eun Chong, Kim Dong-Hoi, Kim Jin, Cho Bum-Joo

2020-Mar-27

Artificial intelligence, Machine learning, Risk factor, Suicidal ideation

General General

DDBJ Data Analysis Challenge: a machine learning competition to predict Arabidopsis chromatin feature annotations from DNA sequences.

In Genes & genetic systems

Recently, the prospect of applying machine learning tools for automating the process of annotation analysis of large-scale sequences from next-generation sequencers has raised the interest of researchers. However, finding research collaborators with knowledge of machine learning techniques is difficult for many experimental life scientists. One solution to this problem is to utilise the power of crowdsourcing. In this report, we describe how we investigated the potential of crowdsourced modelling for a life science task by conducting a machine learning competition, the DNA Data Bank of Japan (DDBJ) Data Analysis Challenge. In the challenge, participants predicted chromatin feature annotations from DNA sequences with competing models. The challenge engaged 38 participants, with a cumulative total of 360 model submissions. The performance of the top model resulted in an area under the curve (AUC) score of 0.95. Over the course of the competition, the overall performance of the submitted models improved by an AUC score of 0.30 from the first submitted model. Furthermore, the 1st- and 2nd-ranking models utilised external data such as genomic location and gene annotation information with specific domain knowledge. The effect of incorporating this domain knowledge led to improvements of approximately 5%-9%, as measured by the AUC scores. This report suggests that machine learning competitions will lead to the development of highly accurate machine learning models for use by experimental scientists unfamiliar with the complexities of data science.

Kaminuma Eli, Baba Yukino, Mochizuki Masahiro, Matsumoto Hirotaka, Ozaki Haruka, Okayama Toshitsugu, Kato Takuya, Oki Shinya, Fujisawa Takatomo, Nakamura Yasukazu, Arita Masanori, Ogasawara Osamu, Kashima Hisashi, Takagi Toshihisa

2020-Mar-26

chromatin features prediction, deep learning, machine learning competition, sequence read archive

Surgery Surgery

Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies in medical imaging.

In BMJ (Clinical research ed.)

OBJECTIVE : To systematically examine the design, reporting standards, risk of bias, and claims of studies comparing the performance of diagnostic deep learning algorithms for medical imaging with that of expert clinicians.

DESIGN : Systematic review.

DATA SOURCES : Medline, Embase, Cochrane Central Register of Controlled Trials, and the World Health Organization trial registry from 2010 to June 2019.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES : Randomised trial registrations and non-randomised studies comparing the performance of a deep learning algorithm in medical imaging with a contemporary group of one or more expert clinicians. Medical imaging has seen a growing interest in deep learning research. The main distinguishing feature of convolutional neural networks (CNNs) in deep learning is that when CNNs are fed with raw data, they develop their own representations needed for pattern recognition. The algorithm learns for itself the features of an image that are important for classification rather than being told by humans which features to use. The selected studies aimed to use medical imaging for predicting absolute risk of existing disease or classification into diagnostic groups (eg, disease or non-disease). For example, raw chest radiographs tagged with a label such as pneumothorax or no pneumothorax and the CNN learning which pixel patterns suggest pneumothorax.

REVIEW METHODS : Adherence to reporting standards was assessed by using CONSORT (consolidated standards of reporting trials) for randomised studies and TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) for non-randomised studies. Risk of bias was assessed by using the Cochrane risk of bias tool for randomised studies and PROBAST (prediction model risk of bias assessment tool) for non-randomised studies.

RESULTS : Only 10 records were found for deep learning randomised clinical trials, two of which have been published (with low risk of bias, except for lack of blinding, and high adherence to reporting standards) and eight are ongoing. Of 81 non-randomised clinical trials identified, only nine were prospective and just six were tested in a real world clinical setting. The median number of experts in the comparator group was only four (interquartile range 2-9). Full access to all datasets and code was severely limited (unavailable in 95% and 93% of studies, respectively). The overall risk of bias was high in 58 of 81 studies and adherence to reporting standards was suboptimal (<50% adherence for 12 of 29 TRIPOD items). 61 of 81 studies stated in their abstract that performance of artificial intelligence was at least comparable to (or better than) that of clinicians. Only 31 of 81 studies (38%) stated that further prospective studies or trials were required.

CONCLUSIONS : Few prospective deep learning studies and randomised trials exist in medical imaging. Most non-randomised trials are not prospective, are at high risk of bias, and deviate from existing reporting standards. Data and code availability are lacking in most studies, and human comparator groups are often small. Future studies should diminish risk of bias, enhance real world clinical relevance, improve reporting and transparency, and appropriately temper conclusions.

STUDY REGISTRATION : PROSPERO CRD42019123605.

Nagendran Myura, Chen Yang, Lovejoy Christopher A, Gordon Anthony C, Komorowski Matthieu, Harvey Hugh, Topol Eric J, Ioannidis John P A, Collins Gary S, Maruthappu Mahiben

2020-Mar-25

General General

On the role of surrogates in the efficient estimation of treatment effects with limited outcome data

ArXiv Preprint

We study the problem of estimating treatment effects when the outcome of primary interest (e.g., long-term health status) is only seldom observed but abundant surrogate observations (e.g., short-term health outcomes) are available. To investigate the role of surrogates in this setting, we derive the semiparametric efficiency lower bounds of average treatment effect (ATE) both with and without presence of surrogates, as well as several intermediary settings. These bounds characterize the best-possible precision of ATE estimation in each case, and their difference quantifies the efficiency gains from optimally leveraging the surrogates in terms of key problem characteristics when only limited outcome data are available. We show these results apply in two important regimes: when the number of surrogate observations is comparable to primary-outcome observations and when the former dominates the latter. Importantly, we take a missing-data approach that circumvents strong surrogate conditions which are commonly assumed in previous literature but almost always fail in practice. To show how to leverage the efficiency gains of surrogate observations, we propose ATE estimators and inferential methods based on flexible machine learning methods to estimate nuisance parameters that appear in the influence functions. We show our estimators enjoy efficiency and robustness guarantees under weak conditions.

Nathan Kallus, Xiaojie Mao

2020-03-27

General General

COVID-19 Screening on Chest X-ray Images Using Deep Learning based Anomaly Detection

ArXiv Preprint

Coronaviruses are important human and animal pathogens. To date the novel COVID-19 coronavirus is rapidly spreading worldwide and subsequently threatening health of billions of humans. Clinical studies have shown that most COVID-19 patients suffer from the lung infection. Although chest CT has been shown to be an effective imaging technique for lung-related disease diagnosis, chest Xray is more widely available due to its faster imaging time and considerably lower cost than CT. Deep learning, one of the most successful AI techniques, is an effective means to assist radiologists to analyze the vast amount of chest X-ray images, which can be critical for efficient and reliable COVID-19 screening. In this work, we aim to develop a new deep anomaly detection model for fast, reliable screening. To evaluate the model performance, we have collected 100 chest X-ray images of 70 patients confirmed with COVID-19 from the Github repository. To facilitate deep learning, more data are needed. Thus, we have also collected 1431 additional chest X-ray images confirmed as other pneumonia of 1008 patients from the public ChestX-ray14 dataset. Our initial experimental results show that the model developed here can reliably detect 96.00% COVID-19 cases (sensitivity being 96.00%) and 70.65% non-COVID-19 cases (specificity being 70.65%) when evaluated on 1531 Xray images with two splits of the dataset.

Jianpeng Zhang, Yutong Xie, Yi Li, Chunhua Shen, Yong Xia

2020-03-27

Public Health Public Health

Using the spike protein feature to predict infection risk and monitor the evolutionary dynamic of coronavirus.

In Infectious diseases of poverty ; h5-index 31.0

BACKGROUND : Coronavirus can cross the species barrier and infect humans with a severe respiratory syndrome. SARS-CoV-2 with potential origin of bat is still circulating in China. In this study, a prediction model is proposed to evaluate the infection risk of non-human-origin coronavirus for early warning.

METHODS : The spike protein sequences of 2666 coronaviruses were collected from 2019 Novel Coronavirus Resource (2019nCoVR) Database of China National Genomics Data Center on Jan 29, 2020. A total of 507 human-origin viruses were regarded as positive samples, whereas 2159 non-human-origin viruses were regarded as negative. To capture the key information of the spike protein, three feature encoding algorithms (amino acid composition, AAC; parallel correlation-based pseudo-amino-acid composition, PC-PseAAC and G-gap dipeptide composition, GGAP) were used to train 41 random forest models. The optimal feature with the best performance was identified by the multidimensional scaling method, which was used to explore the pattern of human coronavirus.

RESULTS : The 10-fold cross-validation results showed that well performance was achieved with the use of the GGAP (g = 3) feature. The predictive model achieved the maximum ACC of 98.18% coupled with the Matthews correlation coefficient (MCC) of 0.9638. Seven clusters for human coronaviruses (229E, NL63, OC43, HKU1, MERS-CoV, SARS-CoV, and SARS-CoV-2) were found. The cluster for SARS-CoV-2 was very close to that for SARS-CoV, which suggests that both of viruses have the same human receptor (angiotensin converting enzyme II). The big gap in the distance curve suggests that the origin of SARS-CoV-2 is not clear and further surveillance in the field should be made continuously. The smooth distance curve for SARS-CoV suggests that its close relatives still exist in nature and public health is challenged as usual.

CONCLUSIONS : The optimal feature (GGAP, g = 3) performed well in terms of predicting infection risk and could be used to explore the evolutionary dynamic in a simple, fast and large-scale manner. The study may be beneficial for the surveillance of the genome mutation of coronavirus in the field.

Qiang Xiao-Li, Xu Peng, Fang Gang, Liu Wen-Bin, Kou Zheng

2020-Mar-25

Coronavirus, Cross-species infection, Machine learning, Spike protein

General General

Accurate Identification of SARS-CoV-2 from Viral Genome Sequences using Deep Learning

bioRxiv Preprint

One of the reasons for the fast spread of SARS-CoV-2 is the lack of accuracy in detection tools in the clinical field. Molecular techniques, such as quantitative real-time RT-PCR and nucleic acid sequencing methods, are widely used to identify pathogens. For this particular virus, however, they have an overall unsatisfying detection rate, due to its relatively recent emergence and still not completely understood features. In addition, SARS-CoV-2 is remarkably similar to other Coronaviruses, and it can present with other respiratory infections, making identification even harder. To tackle this issue, we propose an assisted detection test, combining molecular testing with deep learning. The proposed approach employs a state-of-the-art deep convolutional neural network, able to automatically create features starting from the genome sequence of the virus. Experiments on data from the Novel Coronavirus Resource (2019nCoVR) show that the proposed approach is able to correctly classify SARS-CoV-2, distinguishing it from other coronavirus strains, such as MERS-CoV, HCoV-NL63, HCoV-OC43, HCoV-229E, HCoV-HKU1, and SARS-CoV regardless of missing information and errors in sequencing (noise). From a dataset of 553 complete genome non-repeated sequences that vary from 1,260 to 31,029 bps in length, the proposed approach classifies the different coronaviruses with an average accuracy of 98.75% in a 10-fold cross-validation, identifying SARS-CoV-2 with an AUC of 98%, specificity of 0.9939 and sensitivity of 1.00 in a binary classification. Then, using the same basis, we classify SARS-CoV-2 from 384 complete viral genome sequences with human host, that contain the gene ORF1ab from the NCBI with a 10-fold accuracy of 98.17% , a specificity of 0.9797 and sensitivity of 1.00. These preliminary results seem encouraging enough to identify deep learning as a promising research venue to develop assisted detection tests for SARS-CoV-2. At this end the interaction between viromics and deep learning, will hopefully help to solve global infection problems. In addition, we offer our code and processed data to be used for diagnostic purposes by medical doctors, virologists and scientists involved in solving the SARS-CoV-2 pandemic. As more data become available we will update our system.

Lopez-Rincon, A.; Tonda, A.; Mendoza-Maldonado, L.; Claassen, E.; Garssen, J.; Kraneveld, A. D.

2020-03-27

General General

AirRL: A Reinforcement Learning Approach to Urban Air Quality Inference

ArXiv Preprint

Urban air pollution has become a major environmental problem that threatens public health. It has become increasingly important to infer fine-grained urban air quality based on existing monitoring stations. One of the challenges is how to effectively select some relevant stations for air quality inference. In this paper, we propose a novel model based on reinforcement learning for urban air quality inference. The model consists of two modules: a station selector and an air quality regressor. The station selector dynamically selects the most relevant monitoring stations when inferring air quality. The air quality regressor takes in the selected stations and makes air quality inference with deep neural network. We conduct experiments on a real-world air quality dataset and our approach achieves the highest performance compared with several popular solutions, and the experiments show significant effectiveness of proposed model in tackling problems of air quality inference.

Huiqiang Zhong, Cunxiang Yin, Xiaohui Wu, Jinchang Luo, JiaWei He

2020-03-27

General General

Alzheimer's disease diagnosis from diffusion tensor images using convolutional neural networks.

In PloS one ; h5-index 176.0

Machine learning algorithms are currently being implemented in an escalating manner to classify and/or predict the onset of some neurodegenerative diseases; including Alzheimer's Disease (AD); this could be attributed to the fact of the abundance of data and powerful computers. The objective of this work was to deliver a robust classification system for AD and Mild Cognitive Impairment (MCI) against healthy controls (HC) in a low-cost network in terms of shallow architecture and processing. In this study, the dataset included was downloaded from the Alzheimer's disease neuroimaging initiative (ADNI). The classification methodology implemented was the convolutional neural network (CNN), where the diffusion maps, and gray-matter (GM) volumes were the input images. The number of scans included was 185, 106, and 115 for HC, MCI and AD respectively. Ten-fold cross-validation scheme was adopted and the stacked mean diffusivity (MD) and GM volume produced an AUC of 0.94 and 0.84, an accuracy of 93.5% and 79.6%, a sensitivity of 92.5% and 62.7%, and a specificity of 93.9% and 89% for AD/HC and MCI/HC classification respectively. This work elucidates the impact of incorporating data from different imaging modalities; i.e. structural Magnetic Resonance Imaging (MRI) and Diffusion Tensor Imaging (DTI), where deep learning was employed for the aim of classification. To the best of our knowledge, this is the first study assessing the impact of having more than one scan per subject and propose the proper maneuver to confirm the robustness of the system. The results were competitive among the existing literature, which paves the way for improving medications that could slow down the progress of the AD or prevent it.

Marzban Eman N, Eldeib Ayman M, Yassine Inas A, Kadah Yasser M

2020

Radiology Radiology

Generalization error analysis for deep convolutional neural network with transfer learning in breast cancer diagnosis.

In Physics in medicine and biology

Deep convolutional neural network (DCNN), now popularly called artificial intelligence (AI), has shown the potential to improve over previous computer-assisted tools in medical imaging developed in the past decades. A DCNN has millions of free parameters that need to be trained, but the training sample set is limited in size for most medical imaging tasks so that transfer learning is typically used. Automatic data mining may be an efficient way to enlarge the collected data set but the data can be noisy such as incorrect labels or even a wrong type of images. In this work we studied the generalization error of DCNN with transfer learning in medical imaging for the task of classifying malignant and benign masses on mammograms. With a finite available data set, we simulated a training set containing corrupted data or noisy labels. The balance between learning and memorization of the DCNN was manipulated by varying the proportion of corrupted data in the training set. The generalization error of DCNN was analyzed by the area under the receiver operating characteristic curve for the training and test sets and the weight changes after transfer learning. The study demonstrates that the transfer learning strategy of DCNN for such tasks needs to be designed properly, taking into consideration the constraints of the available training set having limited size and quality for the classification task at hand, to minimize memorization and improve generalizability.

Samala Ravi K, Chan Heang-Ping, Hadjiiski Lubomir M, Helvie Mark A, Richter Caleb

2020-Mar-24

artificial intelligence, breast cancer, deep convolutional neural network, generalization error, mammography

General General

Predicting Escherichia coli loads in cascading dams with machine learning: An integration of hydrometeorology, animal density and grazing pattern.

In The Science of the total environment

Accurate prediction of Escherichia coli contamination in surface waters is challenging due to considerable uncertainty in the physical, chemical and biological variables that control E. coli occurrence and sources in surface waters. This study proposes a novel approach by integrating hydro-climatic variables as well as animal density and grazing pattern in the feature selection modeling phase to increase E. coli prediction accuracy for two cascading dams at the US Meat Animal Research Center (USMARC), Nebraska. Predictive models were developed using regression techniques and an artificial neural network (ANN). Two adaptive neuro-fuzzy inference system (ANFIS) structures including subtractive clustering and fuzzy c-means (FCM) clustering were also used to develop models for predicting E. coli. The performances of the predictive models were evaluated and compared using root mean squared log error (RMSLE). Cross-validation and model performance results indicated that although the majority of models predicted E. coli accurately, ANFIS models resulted in fewer errors compared to the other models. The ANFIS models have the potential to be used to predict E. coli concentration for intervention plans and monitoring programs for cascading dams, and to implement effective best management practices for grazing and irrigation during the growing season.

Abimbola Olufemi P, Mittelstet Aaron R, Messer Tiffany L, Berry Elaine D, Bartelt-Hunt Shannon L, Hansen Samuel P

2020-Mar-12

Animal density estimation, E. coli prediction, Feature selection, Grazing pattern, Machine learning

General General

Large-scale optimization of multi-pollutant control strategies in the Pearl River Delta region of China using a genetic algorithm in machine learning.

In The Science of the total environment

A scientifically sound integrated assessment modeling (IAM) system capable of providing optimized cost-benefit analysis is essential in effective air quality management and control strategy development. Yet scenario optimization for large-scale applications is limited by the computational expense of optimization over many control factors. In this study, a multi-pollutant cost-benefit optimization system based on a genetic algorithm (GA) in machine learning has been developed to provide cost-effective air quality control strategies for large-scale applications (e.g., solution spaces of ~1035). The method was demonstrated by providing optimal cost-benefit control pathways to attain air quality goals for fine particulate matter (PM2.5) and ozone (O3) over the Pearl River Delta (PRD) region of China. The GA was found to be >99% more efficient than the commonly used grid searching method while providing the same combination of optimized multi-pollutant control strategies. The GA method can therefore address air quality management problems that are intractable using the grid searching method. The annual attainment goals for PM2.5 (< 35 μg m-3) and O3 (< 80 ppb) can be achieved simultaneously over the PRD region and surrounding areas by reducing NOx (22%), volatile organic compounds (VOCs, 12%), and primary PM (30%) emissions. However, to attain stricter PM2.5 goals, SO2 reductions (> 9%) are needed as well. The estimated benefit-to-cost ratio of the optimal control strategy reached 17.7 in our application, demonstrating the value of multi-pollutant control for cost-effective air quality management in the PRD region.

Huang Jinying, Zhu Yun, Kelly James T, Jang Carey, Wang Shuxiao, Xing Jia, Chiang Pen-Chi, Fan Shaojia, Zhao Xuetao, Yu Lian

2020-Mar-06

Air pollution control strategies, Cost-benefit analysis, Genetic algorithm, Multi-pollutant optimization, Ozone, PM(2.5)

Radiology Radiology

Ethics of Using and Sharing Clinical Imaging Data for Artificial Intelligence: A Proposed Framework.

In Radiology ; h5-index 91.0

In this article, the authors propose an ethical framework for using and sharing clinical data for the development of artificial intelligence (AI) applications. The philosophical premise is as follows: when clinical data are used to provide care, the primary purpose for acquiring the data is fulfilled. At that point, clinical data should be treated as a form of public good, to be used for the benefit of future patients. In their 2013 article, Faden et al argued that all who participate in the health care system, including patients, have a moral obligation to contribute to improving that system. The authors extend that framework to questions surrounding the secondary use of clinical data for AI applications. Specifically, the authors propose that all individuals and entities with access to clinical data become data stewards, with fiduciary (or trust) responsibilities to patients to carefully safeguard patient privacy, and to the public to ensure that the data are made widely available for the development of knowledge and tools to benefit future patients. According to this framework, the authors maintain that it is unethical for providers to "sell" clinical data to other parties by granting access to clinical data, especially under exclusive arrangements, in exchange for monetary or in-kind payments that exceed costs. The authors also propose that patient consent is not required before the data are used for secondary purposes when obtaining such consent is prohibitively costly or burdensome, as long as mechanisms are in place to ensure that ethical standards are strictly followed. Rather than debate whether patients or provider organizations "own" the data, the authors propose that clinical data are not owned at all in the traditional sense, but rather that all who interact with or control the data have an obligation to ensure that the data are used for the benefit of future patients and society.

Larson David B, Magnus David C, Lungren Matthew P, Shah Nigam H, Langlotz Curtis P

2020-Mar-24

General General

Measuring Implicit Motives with the Picture Story Exercise (PSE): Databases of Expert-Coded German Stories, Pictures, and Updated Picture Norms.

In Journal of personality assessment

We present two openly accessible databases related to the assessment of implicit motives using Picture Story Exercises (PSEs): (a) A database of 183,415 German sentences, nested in 26,389 stories provided by 4,570 participants, which have been coded by experts using Winter's coding system for the implicit affiliation/intimacy, achievement, and power motives, and (b) a database of 54 classic and new pictures which have been used as PSE stimuli. Updated picture norms are provided which can be used to select appropriate pictures for PSE applications. Based on an analysis of the relations between raw motive scores, word count, and sentence count, we give recommendations on how to control motive scores for story length, and validate the recommendation with a meta-analysis on gender differences in the implicit affiliation motive that replicates existing findings. We discuss to what extent the guiding principles of the story length correction can be generalized to other content coding systems for narrative material. Several potential applications of the databases are discussed, including (un)supervised machine learning of text content, psychometrics, and better reproducibility of PSE research.

Schönbrodt Felix D, Hagemeyer Birk, Brandstätter Veronika, Czikmantori Thomas, Gröpel Peter, Hennecke Marie, Israel Laura S F, Janson Kevin T, Kemper Nina, Köllner Martin G, Kopp Philipp M, Mojzisch Andreas, Müller-Hotop Raphael, Prüfer Johanna, Quirin Markus, Scheidemann Bettina, Schiestel Lena, Schulz-Hardt Stefan, Sust Larissa N N, Zygar-Hoffmann Caroline, Schultheiss Oliver C

2020-Mar-24

Surgery Surgery

Coronary Artery Segmentation in Angiographic Videos Using A 3D-2D CE-Net

ArXiv Preprint

Coronary angiography is an indispensable assistive technique for cardiac interventional surgery. Segmentation and extraction of blood vessels from coronary angiography videos are very essential prerequisites for physicians to locate, assess and diagnose the plaques and stenosis in blood vessels. This article proposes a new video segmentation framework that can extract the clearest and most comprehensive coronary angiography images from a video sequence, thereby helping physicians to better observe the condition of blood vessels. This framework combines a 3D convolutional layer to extract spatial--temporal information from a video sequence and a 2D CE--Net to accomplish the segmentation task of an image sequence. The input is a few continuous frames of angiographic video, and the output is a mask of segmentation result. From the results of segmentation and extraction, we can get good segmentation results despite the poor quality of coronary angiography video sequences.

Lu Wang, Dong-xue Liang, Xiao-lei Yin, Jing Qiu, Zhi-yun Yang, Jun-hui Xing, Jian-zeng Dong, Zhao-yuan Ma

2020-03-26

Radiology Radiology

An update on the management of breast atypical ductal hyperplasia.

In The British journal of radiology

Among lesions with uncertain malignant potential found at percutaneous breast biopsy, atypical ductal hyperplasia (ADH) carries both the highest risk of underestimation and the closest and most pathologist-dependent differential diagnosis with ductal carcinoma in situ (DCIS), matching the latter's features save for size only. ADH is therefore routinely surgically excised, but single-centre studies with limited sample size found low rates of upgrade to invasive cancer or DCIS. This suggests the possibility of surveillance over surgery in selected subgroups, considering the 2% threshold allowing for follow-up according to the Breast Imaging Reporting and Data System. A recent meta-analysis on 6458 lesions counters this approach, confirming that, surgically excised or managed with surveillance, ADH carries a 29 and 5% upgrade rate, respectively, invariably higher than 2% even in subgroups considering biopsy guidance and technique, needle calibre, apparent complete lesion removal. The high heterogeneity (I2 = 80%) found in this meta-analysis reaffirmed the need to synthesize evidence from systematic reviews to achieve generalizable results, fit for guidelines development. Limited tissue sampling at percutaneous biopsy intrinsically hampers the prediction of ADH-associated malignancy. This prediction could be improved by using contrast-enhanced breast imaging and applying artificial intelligence on both pathology and imaging results, allowing for overtreatment reduction.

Schiaffino Simone, Cozzi Andrea, Sardanelli Francesco

2020-Mar-24

Surgery Surgery

Weakly-supervised 3D coronary artery reconstruction from two-view angiographic images

ArXiv Preprint

The reconstruction of three-dimensional models of coronary arteries is of great significance for the localization, evaluation and diagnosis of stenosis and plaque in the arteries, as well as for the assisted navigation of interventional surgery. In the clinical practice, physicians use a few angles of coronary angiography to capture arterial images, so it is of great practical value to perform 3D reconstruction directly from coronary angiography images. However, this is a very difficult computer vision task due to the complex shape of coronary blood vessels, as well as the lack of data set and key point labeling. With the rise of deep learning, more and more work is being done to reconstruct 3D models of human organs from medical images using deep neural networks. We propose an adversarial and generative way to reconstruct three dimensional coronary artery models, from two different views of angiographic images of coronary arteries. With 3D fully supervised learning and 2D weakly supervised learning schemes, we obtained reconstruction accuracies that outperform state-of-art techniques.

Lu Wang, Dong-xue Liang, Xiao-lei Yin, Jing Qiu, Zhi-yun Yang, Jun-hui Xing, Jian-zeng Dong, Zhao-yuan Ma

2020-03-26

Radiology Radiology

Deep Convolutional Neural Network-aided Detection of Portal Hypertension in Patients With Cirrhosis.

In Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association

BACKGROUND & AIMS : Non-invasive and accurate methods are needed to identify patients with clinically significant portal hypertension (CSPH). We investigated the ability of deep convolutional neural network (CNN) analysis of computed tomography (CT) or magnetic resonance (MR) to identify patients with CSPH.

METHODS : We collected liver and spleen image from patients who underwent contrast-enhanced CT or MR analysis within 14 days of transjugular catheterization for hepatic venous pressure gradient measurement. The CT cohort comprised participants with cirrhosis in the CHESS1701 study, performed at 4 university hospitals in China from August 2016 through September 2017. The MR cohort comprised participants with cirrhosis in the CHESS1802 study, performed at 8 university hospitals in China and 1 in Turkey from December 2018 through April 2019. Patients with CSPH were identified as those with a hepatic venous pressure gradient ≥10 mmHg. In total, we analyzed 10014 liver images and 899 spleen images collected from 679 participants who underwent CT analysis, and 45554 liver and spleen images from 271 participants who underwent MR analysis. For each cohort, participants were shuffled and then randomly and equiprobably sampled for 6 times into training, validation, and test datasets (ratio of 3:1:1). Therefore, a total of 6 deep CNN models for each cohort were developed for identification of CSPH.

RESULTS : The CT-based CNN analysis identified patients with CSPH with the area under receiver operating characteristic curve (AUC) value of 0.998 in the training set (95% CI, 0.996-1.000), an AUC of 0.912 in the validation set (95% CI, 0.854-0.971), and an AUC of 0.933 (95% CI, 0.883-0.984) in the test datasets. The MR-based CNN analysis identified patients with CSPH with an AUC of 1.000 in the training set (95% CI, 0.999-1.000), and AUC of 0.924 in the validation set (95% CI, 0.833-1.000), and an AUC of 0.940 in the test dataset (95% CI, 0.880-0.999). When the model development procedures were repeated 6 times, and AUCs for all CNN analyses were 0.888 or greater, with no significant differences between rounds (P>.05).

CONCLUSIONS : We developed a deep CNN to analyze CT or MR images of liver and spleen from patients with cirrhosis that identifies patients with CSPH with an AUC value of 0.9. This provides a non-invasive and rapid method for detection of CSPH (ClincialTrials.gov number, NCT03138915; NCT03766880).

Liu Yanna, Ning Zhenyuan, Örmeci Necati, An Weimin, Yu Qian, Han Kangfu, Huang Yifei, Liu Dengxiang, Liu Fuquan, Li Zhiwei, Ding Huiguo, Luo Hongwu, Zuo Changzeng, Liu Changchun, Wang Jitao, Zhang Chunqing, Ji Jiansong, Wang Wenhui, Wang Zhiwei, Wang Weidong, Yuan Min, Li Lei, Zhao Zhongwei, Wang Guangchuan, Li Mingxing, Liu Qingbo, Lei Junqiang, Liu Chuan, Tang Tianyu, Akçalar Seray, Çelebioğlu Emrecan, Üstüner Evren, Bilgiç Sadık, Ellik Zeynep, Asiller Özgün Ömer, Liu Zaiyi, Teng Gaojun, Chen Yaolong, Hou Jinlin, Li Xun, He Xiaoshun, Dong Jiahong, Tian Jie, Liang Ping, Ju Shenghong, Zhang Yu, Qi Xiaolong

2020-Mar-20

AI, HVPG, deep learning, diagnostic

Radiology Radiology

Characterizing Static and Dynamic Fractional Amplitude of Low-Frequency Fluctuation and its Prediction of Clinical Dysfunction in Patients with Diffuse Axonal Injury.

In Academic radiology

RATIONALE AND OBJECTIVES : Recently, advanced magnetic resonance imaging has been widely adopted to investigate altered structure and functional activities in patients with diffuse axonal injury (DAI), this patient presumed to be caused by shearing forces and results in significant neurological effects. However, little is known regarding cerebral temporal dynamics and its predictive ability in the clinical dysfunction of DAI.

MATERIALS AND METHODS : In this study, static and dynamic fractional amplitude of low-frequency fluctuation (fALFF), an improved approach to detect the intensity of intrinsic neural activities, and their temporal variability were applied to examine the alteration between DAI patients (n = 24) and healthy controls (n = 26) at the voxel level. Then, the altered functional index was used to explore the clinical relationship and predict dysfunction in DAI patients.

RESULTS : We discovered that, compared to healthy controls, DAI patients showed commonly altered regions of static fALFF, and its variability was mainly located in the left cerebellum posterior lobe. Furthermore, decreased static fALFF values over the left cerebellum posterior lobe and bilateral medial frontal gyrus showed significant correlations with disease duration and Mini-Mental State Examination scores. More important, the increased temporal variability of dynamic fALFF in the left caudate could predict the severity of the Glasgow Coma Scale score in DAI patients.

CONCLUSION : Overall, these results suggested selective abnormalities in intrinsic neural activities with reduced intensity and increased variability, and this novel predictive marker may be developed as a useful indicator for future connectomics or artificial intelligence analyses.

Zhou Fuqing, Zhan Jie, Gong Tao, Xu Wenhua, Kuang Hongmei, Li Jian, Wang Yinhua, Gong Honghan

2020-Mar-20

Diffuse axonal injury, Fractional amplitude of low-frequency fluctuation, Temporal variability

General General

Evil doctor, ethical android: Star Trek's instantiation of conscience in subroutines.

In Early human development

Machine intelligence, whether it constitutes Strong artificial intelligence (AI) or Weak AI, may have varying degrees of independence. Both Strong and Weak AIs are often depicted as being programmed with safeguards which prevent harm to humanity, informed by Asimov's programs called the Laws of Robotics. This paper will review these programs through a reading of instances of machine intelligence in Star Trek, and will attempt to show that these "ethical subroutines" may well be vital to our continued existence, irrespective of whether the machine intelligences constitute Strong or Weak AI. In effect, this paper will analyse the machine analogues of conscience in Star Trek, and will do so through an analysis of the android Data and the Emergency Medical Hologram. We will argue that AI should be treated with caution, lest we create powerful intelligences that may not only ignore us but also find us threatening.

Grech Victor, Scerri Mariella

2020-Mar-20

General General

Evaluation of Machine Learning Algorithms for Predicting Readmission After Acute Myocardial Infarction Using Routinely Collected Clinical Data.

In The Canadian journal of cardiology

BACKGROUND : The ability to predict readmission accurately after hospitalization for acute myocardial infarction (AMI) is limited in current statistical models. Machine-learning (ML) methods have shown improved predictive ability in various clinical contexts, but their utility in predicting readmission after hospitalization for AMI is unknown.

METHODS : Using detailed clinical information collected from patients hospitalized with AMI, we evaluated 6 ML algorithms (logistic regression, naïve Bayes, support vector machines, random forest, gradient boosting, and deep neural networks) to predict readmission within 30 days and 1 year of discharge. A nested cross-validation approach was used to develop and test models. We used C-statistics to compare discriminatory capacity, whereas the Brier score was used to indicate overall model performance. Model calibration was assessed using calibration plots.

RESULTS : The 30-day readmission rate was 16.3%, whereas the 1-year readmission rate was 45.1%. For 30-day readmission, the discriminative ability for the ML models was modest (C-statistic 0.641; 95% confidence interval (CI), 0.621-0.662 for gradient boosting) and did not outperform previously reported methods. For 1-year readmission, different ML models showed moderate performance, with C-statistics around 0.72. Despite modest discriminatory capabilities, the observed readmission rates were markedly higher in the tenth decile of predicted risk compared with the first decile of predicted risk for both 30-day and 1-year readmission.

CONCLUSIONS : Despite including detailed clinical information and evaluating various ML methods, these models did not have better discriminatory ability to predict readmission outcomes compared with previously reported methods.

Gupta Shagun, Ko Dennis T, Azizi Paymon, Bouadjenek Mohamed Reda, Koh Maria, Chong Alice, Austin Peter C, Sanner Scott

2019-Oct-25

General General

Artificial Intelligence in the Intensive Care Unit.

In Critical care (London, England)

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2020. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2020. Further information about the Annual Update in Intensive Care and Emergency Medicine is available from http://www.springer.com/series/8901.

Gutierrez Guillermo

2020-Mar-24

oncology Oncology

Predicting Glioblastoma Recurrence from Preoperative MR Scans Using Fractional-Anisotropy Maps with Free-Water Suppression.

In Cancers

Diffusion tensor imaging (DTI), and fractional-anisotropy (FA) maps in particular, have shown promise in predicting areas of tumor recurrence in glioblastoma. However, analysis of peritumoral edema, where most recurrences occur, is impeded by free-water contamination. In this study, we evaluated the benefits of a novel, deep-learning-based approach for the free-water correction (FWC) of DTI data for prediction of later recurrence. We investigated 35 glioblastoma cases from our prospective glioma cohort. A preoperative MR image and the first MR scan showing tumor recurrence were semiautomatically segmented into areas of contrast-enhancing tumor, edema, or recurrence of the tumor. The 10th, 50th and 90th percentiles and mean of FA and mean-diffusivity (MD) values (both for the original and FWC-DTI data) were collected for areas with and without recurrence in the peritumoral edema. We found significant differences in the FWC-FA maps between areas of recurrence-free edema and areas with later tumor recurrence, where differences in noncorrected FA maps were less pronounced. Consequently, a generalized mixed-effect model had a significantly higher area under the curve when using FWC-FA maps (AUC = 0.9) compared to noncorrected maps (AUC = 0.77, p < 0.001). This may reflect tumor infiltration that is not visible in conventional imaging, and may therefore reveal important information for personalized treatment decisions.

Metz Marie-Christin, Molina-Romero Miguel, Lipkova Jana, Gempt Jens, Liesche-Starnecker Friederike, Eichinger Paul, Grundl Lioba, Menze Bjoern, Combs Stephanie E, Zimmer Claus, Wiestler Benedikt

2020-Mar-19

DTI, FA, deep learning, glioblastoma, recurrence prediction.

General General

[Phenotypes of bronchial asthma during the health resort period and personalized programs of medical rehabilitation].

In Voprosy kurortologii, fizioterapii, i lechebnoi fizicheskoi kultury

INTRODUCTION : Bronchial asthma (BA) is a relevant social and medical problem in our country and around the world. Currently, phenotypes of the disease are distinguished. One of the original decisions in phenotypes distinguishing is the use of cluster analysis. However, the concept of BA phenotypes at the health resort period of rehabilitation has not yet been formed.

AIM : To determine the BA phenotypes upon admission of patients to a health resort medical rehabilitation (HRMR) using cluster analysis and to offer personalized rehabilitation programs.

MATERIAL AND METHODS : 518 patients with asthma who underwent HRMR on the southern coast of Crimea were examined. Each patient received clinical, functional and laboratory examination. HRMR included correction of long-term treatment according to the severity of asthma, climate therapy, respiratory therapy, educational programs, and physiotherapeutic procedures. We applied cluster analysis in order to identify BA phenotypes. Description statistics methods were used for phenotype-cluster characterization, comparative analysis - for determination of reliable phenotypic characteristics and relation of the effectiveness of HRMR and phenotypes.

RESULTS : A model of three phenotype-clusters was developed. The first cluster included patients with BA of moderate severity, uncontrolled course, frequent exacerbations, history of atopy, a tendency to obesity, moderately reduced external respiration function, fixed airway obstruction, high adherence to long-term therapy with medium doses of inhaled glucocorticoids (ICS) combined with long-acting β-2 agonists (LABA). Cluster 2 included patients with mild asthma, controlled or partially controlled course of the disease, with rare short exacerbations, late onset, preserved external respiration function and exercise tolerance, but low adherence to long-term therapy with medium and low doses of ICS. Cluster 3 included patients with moderate to severe BA, uncontrolled course, with early onset, frequent and prolonged exacerbations, severe symptoms, significantly reduced external respiration function with fixed obstruction, decreased exercise tolerance, but low adherence to long-term therapy (4th stage) with high doses of ICS in combination with LABA and long-acting anticholinergics. A close relationship was found between phenotypes-clusters and the achieved effects: a significant increase in the control of BA and a high efficiency of rehabilitation in patients of the 1st and especially 3rd clusters and low in the 2nd cluster. The optimal rehabilitation programs for each of the selected cluster phenotypes were determined.

CONCLUSION : The cluster model developed with the help of artificial intelligence has demonstrated high prognostic value in the determination of the effectiveness and change of control over the course of asthma as a result of HRMR. Personalized HRMR programs are suggested.

Ivashchenko A S, Dudchenko L Sh, Kaladze N N, Mizin V I

2020

bronchial asthma, cluster analysis, health resort medical rehabilitation, phenotypes

Surgery Surgery

The ways of using machine learning in dentistry.

In Advances in clinical and experimental medicine : official organ Wroclaw Medical University

Innovative computer techniques are starting to be employed not only in academic research, but also in commercial production, finding use in many areas of dentistry. This is conducive to the digitalization of dentistry and its increasing treatment and diagnostic demands. In many areas of dentistry, such as orthodontics and maxillofacial surgery, but also periodontics or prosthetics, only a correct diagnosis ensures the correct treatment plan, which is the only way to restore the patient's health. The diagnosis and treatment plan is based on the specialist's knowledge, but is subject to a large, multi-factorial risk of error. Therefore, the introduction of multiparametric pattern recognition methods (statistics, machine learning and artificial intelligence (AI)) is a great hope for both the physicians and the patients. However, the general use of clinical decision support systems (CDSS) in a dental clinic is not yet realistic and requires work in many aspects - methodical, technological and business. The article presents a review of the latest attempts to apply AI, such as CDSS or genetic algorithms (GAs) in research and clinical dentistry, taking under consideration all of the main dental specialties. Work on the introduction of public CDSS has been continued for years. The article presents the latest achievements in this field, analyzing their real-life application and credibility.

Machoy Monika Elżbieta, Szyszka-Sommerfeld Liliana, Vegh Andras, Gedrange Tomasz, Woźniak Krzysztof

2020-Mar-24

CDSS, artificial intelligence, clinical decision support systems, dentistry, machine learning

General General

Brewery: Deep Learning and deeper profiles for the prediction of 1D protein structure annotations.

In Bioinformatics (Oxford, England)

MOTIVATION : Protein Structural Annotations are essential abstractions to deal with the prediction of Protein Structures. Many increasingly sophisticated Protein Structural Annotations have been devised in the last few decades. However the need for annotations that are easy to compute, process and predict has not diminished. This is especially true for protein structures that are hardest to predict such as novel folds.

RESULTS : We propose Brewery, a suite of ab initio predictors of 1D Protein Structural Annotations. Brewery uses multiple sources of evolutionary information to achieve state-of-the-art predictions of Secondary Structure, Structural Motifs, Relative Solvent Accessibility and Contact Density.

AVAILABILITY : The web server, standalone program, Docker image and training sets of Brewery are available at http://distilldeep.ucd.ie/brewery/.

Torrisi Mirko, Pollastri Gianluca

2020-Mar-24

Pathology Pathology

Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset.

In International journal of molecular sciences ; h5-index 102.0

Drug-induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized and candidate drugs, and predicting hepatotoxicity from the chemical structure of a substance remains a task worth pursuing. Such an approach is coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016, a group of researchers from the FDA published an improved annotated list of drugs with respect to their DILI risk, constituting "the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans" (DILIrank). This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A total of 78 models with reasonable performance were selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.

Ancuceanu Robert, Hovanet Marilena Viorica, Anghel Adriana Iuliana, Furtunescu Florentina, Neagu Monica, Constantin Carolina, Dinu Mihaela

2020-Mar-19

DILI, DILIrank, QSAR, drug hepatotoxicity, in silico, nested cross-validation, virtual screening

General General

Cancer subtype classification and modeling by pathway attention and propagation.

In Bioinformatics (Oxford, England)

MOTIVATION : Biological pathway is important curated knowledge of biological processes. Thus, cancer subtype classification based on pathways will be very useful to understand differences in biological mechanisms among cancer subtypes. However, pathways include only a fraction of the entire gene set, only 1/3 of human genes in KEGG, and pathways are fragmented. For this reason, there are few computational methods to use pathways for cancer subtype classification.

RESULTS : We present an explainable deep learning model with attention mechanism and network propagation for cancer subtype classification. Each pathway is modeled by a graph convolutional network. then, a multi-attention based ensemble model combines several hundreds of pathways in an explainable manner. Lastly, network propagation on pathway-gene network explains why gene expression profiles in subtypes are different. In experiments with five TCGA cancer data sets, our method achieved very good classification accuracies and, additionally, identified subtype-specific pathways and biological functions.

SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.

Lee Sangseon, Lim Sangsoo, Lee Taeheon, Sung Inyoung, Kim Sun

2020-Mar-24

Internal Medicine Internal Medicine

Machine Learning Applications in Endocrinology and Metabolism Research: An Overview.

In Endocrinology and metabolism (Seoul, Korea)

Machine learning (ML) applications have received extensive attention in endocrinology research during the last decade. This review summarizes the basic concepts of ML and certain research topics in endocrinology and metabolism where ML principles have been actively deployed. Relevant studies are discussed to provide an overview of the methodology, main findings, and limitations of ML, with the goal of stimulating insights into future research directions. Clear, testable study hypotheses stem from unmet clinical needs, and the management of data quality (beyond a focus on quantity alone), open collaboration between clinical experts and ML engineers, the development of interpretable high-performance ML models beyond the black-box nature of some algorithms, and a creative environment are the core prerequisites for the foreseeable changes expected to be brought about by ML and artificial intelligence in the field of endocrinology and metabolism, with actual improvements in clinical practice beyond hype. Of note, endocrinologists will continue to play a central role in these developments as domain experts who can properly generate, refine, analyze, and interpret data with a combination of clinical expertise and scientific rigor.

Hong Namki, Park Heajeong, Rhee Yumie

2020-Mar

Adrenal, Artificial intelligence, Deep learning, Diabetes, Endocrinology, Machine learning, Metabolism, Osteoporosis, Pituitary, Thyroid

General General

Overview of quantitative susceptibility mapping using deep learning: Current status, challenges and opportunities.

In NMR in biomedicine ; h5-index 41.0

Quantitative susceptibility mapping (QSM) has gained broad interest in the field by extracting bulk tissue magnetic susceptibility, predominantly determined by myelin, iron and calcium from magnetic resonance imaging (MRI) phase measurements in vivo. Thereby, QSM can reveal pathological changes of these key components in a variety of diseases. QSM requires multiple processing steps such as phase unwrapping, background field removal and field-to-source inversion. Current state-of-the-art techniques utilize iterative optimization procedures to solve the inversion and background field correction, which are computationally expensive and require a careful choice of regularization parameters. With the recent success of deep learning using convolutional neural networks for solving ill-posed reconstruction problems, the QSM community also adapted these techniques and demonstrated that the QSM processing steps can be solved by efficient feed forward multiplications not requiring either iterative optimization or the choice of regularization parameters. Here, we review the current status of deep learning-based approaches for processing QSM, highlighting limitations and potential pitfalls, and discuss the future directions the field may take to exploit the latest advances in deep learning for QSM.

Jung Woojin, Bollmann Steffen, Lee Jongho

2020-Mar-23

background field correction, deep learning, dipole inversion, quantitative susceptibility mapping, unwrapping

General General

Segmentation of Masses on Mammograms Using Data Augmentation and Deep Learning.

In Journal of digital imaging

The diagnosis of breast cancer in early stage is essential for successful treatment. Detection can be performed in several ways, the most common being through mammograms. The projections acquired by this type of examination are directly affected by the composition of the breast, which density can be similar to the suspicious masses, being a challenge the identification of malignant lesions. In this article, we propose a computer-aided detection (CAD) system to aid in the diagnosis of masses in digitized mammograms using a model based in the U-Net, allowing specialists to monitor the lesion over time. Unlike most of the studies, we propose the use of an entire base of digitized mammograms using normal, benign, and malignant cases. Our research is divided into four stages: (1) pre-processing, with the removal of irrelevant information, enhancement of the contrast of 7989 images of the Digital Database for Screening Mammography (DDSM), and obtaining regions of interest. (2) Data augmentation, with horizontal mirroring, zooming, and resizing of images; (3) training, with tests of six-based U-Net models, with different characteristics; (4) testing, evaluating four metrics, accuracy, sensitivity, specificity, and Dice Index. The tested models obtained different results regarding the assessed parameters. The best model achieved a sensitivity of 92.32%, specificity of 80.47%, accuracy of 85.95% Dice Index of 79.39%, and AUC of 86.40%. Even using a full base without case selection bias, the results obtained demonstrate that the use of a complete database can provide knowledge to the CAD expert.

Zeiser Felipe André, da Costa Cristiano André, Zonta Tiago, Marques Nuno M C, Roehe Adriana Vial, Moreno Marcelo, da Rosa Righi Rodrigo

2020-Mar-23

Breast cancer, Computer-aided detection, Deep learning, Fully convolutional network, Segmentation, U-Net

General General

Human vs. machine: the psychological and behavioral consequences of being compared to an outperforming artificial agent.

In Psychological research

While artificial agents (AA) such as Artificial Intelligence are being extensively developed, a popular belief that AA will someday surpass human intelligence is growing. The present research examined whether this common belief translates into negative psychological and behavioral consequences when individuals assess that an AA performs better than them on cognitive and intellectual tasks. In two studies, participants were led to believe that an AA performed better or less well than them on a cognitive inhibition task (Study 1) and on an intelligence task (Study 2). Results indicated that being outperformed by an AA increased subsequent participants' performance as long as they did not experience psychological discomfort towards the AA and self-threat. Psychological implications in terms of motivation and potential threat as well as the prerequisite for the future interactions of humans with AAs are further discussed.

Spatola Nicolas, Normand Alice

2020-Mar-21

Cognitive control, Human–machine interaction, Logical reasoning, Social comparison

General General

Sonification based de novo protein design using artificial intelligence, structure prediction, and analysis using molecular modeling.

In APL bioengineering

We report the use of a deep learning model to design de novo proteins, based on the interplay of elementary building blocks via hierarchical patterns. The deep neural network model is based on translating protein sequences and structural information into a musical score that features different pitches for each of the amino acids, and variations in note length and note volume reflecting secondary structure information and information about the chain length and distinct protein molecules. We train a deep learning model whose architecture is composed of several long short-term memory units from data consisting of musical representations of proteins classified by certain features, focused here on alpha-helix rich proteins. Using the deep learning model, we then generate de novo musical scores and translate the pitch information and chain lengths into sequences of amino acids. We use a Basic Local Alignment Search Tool to compare the predicted amino acid sequences against known proteins, and estimate folded protein structures using the Optimized protein fold RecognitION method (ORION) and MODELLER. We find that the method proposed here can be used to design de novo proteins that do not exist yet, and that the designed proteins fold into specified secondary structures. We validate the newly predicted protein by molecular dynamics equilibration in explicit water and subsequent characterization using a normal mode analysis. The method provides a tool to design novel protein materials that could find useful applications as materials in biology, medicine, and engineering.

Yu Chi-Hua, Buehler Markus J

2020-Mar

General General

10,000 social brains: Sex differentiation in human brain anatomy.

In Science advances

In human and nonhuman primates, sex differences typically explain much interindividual variability. Male and female behaviors may have played unique roles in the likely coevolution of increasing brain volume and more complex social dynamics. To explore possible divergence in social brain morphology between men and women living in different social environments, we applied probabilistic generative modeling to ~10,000 UK Biobank participants. We observed strong volume effects especially in the limbic system but also in regions of the sensory, intermediate, and higher association networks. Sex-specific brain volume effects in the limbic system were linked to the frequency and intensity of social contact, such as indexed by loneliness, household size, and social support. Across the processing hierarchy of neural networks, different conditions for social interplay may resonate in and be influenced by brain anatomy in sex-dependent ways.

Kiesow Hannah, Dunbar Robin I M, Kable Joseph W, Kalenscher Tobias, Vogeley Kai, Schilbach Leonhard, Marquand Andre F, Wiecki Thomas V, Bzdok Danilo

2020-Mar

Radiology Radiology

Quantitative imaging feature pipeline: a web-based tool for utilizing, sharing, and building image-processing pipelines.

In Journal of medical imaging (Bellingham, Wash.)

Quantitative image features that can be computed from medical images are proving to be valuable biomarkers of underlying cancer biology that can be used for assessing treatment response and predicting clinical outcomes. However, validation and eventual clinical implementation of these tools is challenging due to the absence of shared software algorithms, architectures, and the tools required for computing, comparing, evaluating, and disseminating predictive models. Similarly, researchers need to have programming expertise in order to complete these tasks. The quantitative image feature pipeline (QIFP) is an open-source, web-based, graphical user interface (GUI) of configurable quantitative image-processing pipelines for both planar (two-dimensional) and volumetric (three-dimensional) medical images. This allows researchers and clinicians a GUI-driven approach to process and analyze images, without having to write any software code. The QIFP allows users to upload a repository of linked imaging, segmentation, and clinical data or access publicly available datasets (e.g., The Cancer Imaging Archive) through direct links. Researchers have access to a library of file conversion, segmentation, quantitative image feature extraction, and machine learning algorithms. An interface is also provided to allow users to upload their own algorithms in Docker containers. The QIFP gives researchers the tools and infrastructure for the assessment and development of new imaging biomarkers and the ability to use them for single and multicenter clinical and virtual clinical trials.

Mattonen Sarah A, Gude Dev, Echegaray Sebastian, Bakr Shaimaa, Rubin Daniel L, Napel Sandy

2020-Jul

feature extraction, machine learning, medical image analysis, processing pipeline, radiomics

General General

DirectPET: full-size neural network PET reconstruction from sinogram data.

In Journal of medical imaging (Bellingham, Wash.)

Purpose: Neural network image reconstruction directly from measurement data is a relatively new field of research, which until now has been limited to producing small single-slice images (e.g., 1 × 128 × 128 ). We proposed a more efficient network design for positron emission tomography called DirectPET, which is capable of reconstructing multislice image volumes (i.e., 16 × 400 × 400 ) from sinograms. Approach: Large-scale direct neural network reconstruction is accomplished by addressing the associated memory space challenge through the introduction of a specially designed Radon inversion layer. Using patient data, we compare the proposed method to the benchmark ordered subsets expectation maximization (OSEM) algorithm using signal-to-noise ratio, bias, mean absolute error, and structural similarity measures. In addition, line profiles and full-width half-maximum measurements are provided for a sample of lesions. Results: DirectPET is shown capable of producing images that are quantitatively and qualitatively similar to the OSEM target images in a fraction of the time. We also report on an experiment where DirectPET is trained to map low-count raw data to normal count target images, demonstrating the method's ability to maintain image quality under a low-dose scenario. Conclusion: The ability of DirectPET to quickly reconstruct high-quality, multislice image volumes suggests potential clinical viability of the method. However, design parameters and performance boundaries need to be fully established before adoption can be considered.

Whiteley William, Luk Wing K, Gregor Jens

2020-May

deep learning, image reconstruction, medical imaging, neural network, positron emission tomography

General General

Determining hypertensive patients' beliefs towards medication and associations with medication adherence using machine learning methods.

In PeerJ

Background : This study assesses the feasibility of using machine learning methods such as Random Forests (RF), Artificial Neural Networks (ANN), Support Vector Regression (SVR) and Self-Organizing Feature Maps (SOM) to identify and determine factors associated with hypertensive patients' adherence levels. Hypertension is the medical term for systolic and diastolic blood pressure higher than 140/90 mmHg. A conventional medication adherence scale was used to identify patients' adherence to their prescribed medication. Using machine learning applications to predict precise numeric adherence scores in hypertensive patients has not yet been reported in the literature.

Methods : Data from 160 hypertensive patients from a tertiary hospital in Kuala Lumpur, Malaysia, were used in this study. Variables were ranked based on their significance to adherence levels using the RF variable importance method. The backward elimination method was then performed using RF to obtain the variables significantly associated with the patients' adherence levels. RF, SVR and ANN models were developed to predict adherence using the identified significant variables. Visualizations of the relationships between hypertensive patients' adherence levels and variables were generated using SOM.

Result : Machine learning models constructed using the selected variables reported RMSE values of 1.42 for ANN, 1.53 for RF, and 1.55 for SVR. The accuracy of the dichotomised scores, calculated based on a percentage of correctly identified adherence values, was used as an additional model performance measure, resulting in accuracies of 65% (ANN), 78% (RF) and 79% (SVR), respectively. The Wilcoxon signed ranked test reported that there was no significant difference between the predictions of the machine learning models and the actual scores. The significant variables identified from the RF variable importance method were educational level, marital status, General Overuse, monthly income, and Specific Concern.

Conclusion : This study suggests an effective alternative to conventional methods in identifying the key variables to understand hypertensive patients' adherence levels. This can be used as a tool to educate patients on the importance of medication in managing hypertension.

Aziz Firdaus, Malek Sorayya, Mhd Ali Adliah, Wong Mee Sieng, Mosleh Mogeeb, Milow Pozi

2020

Adherence level, Artificial neural network, Hypertension, Random forest, Self-organizing Map (SOM), Support Vector Regression, Variable importance

Public Health Public Health

The Impact of COVID-19 Epidemic Declaration on Psychological Consequences: A Study on Active Weibo Users.

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

COVID-19 (Corona Virus Disease 2019) has significantly resulted in a large number of psychological consequences. The aim of this study is to explore the impacts of COVID-19 on people's mental health, to assist policy makers to develop actionable policies, and help clinical practitioners (e.g., social workers, psychiatrists, and psychologists) provide timely services to affected populations. We sample and analyze the Weibo posts from 17,865 active Weibo users using the approach of Online Ecological Recognition (OER) based on several machine-learning predictive models. We calculated word frequency, scores of emotional indicators (e.g., anxiety, depression, indignation, and Oxford happiness) and cognitive indicators (e.g., social risk judgment and life satisfaction) from the collected data. The sentiment analysis and the paired sample t-test were performed to examine the differences in the same group before and after the declaration of COVID-19 on 20 January, 2020. The results showed that negative emotions (e.g., anxiety, depression and indignation) and sensitivity to social risks increased, while the scores of positive emotions (e.g., Oxford happiness) and life satisfaction decreased. People were concerned more about their health and family, while less about leisure and friends. The results contribute to the knowledge gaps of short-term individual changes in psychological conditions after the outbreak. It may provide references for policy makers to plan and fight against COVID-19 effectively by improving stability of popular feelings and urgently prepare clinical practitioners to deliver corresponding therapy foundations for the risk groups and affected people.

Li Sijia, Wang Yilin, Xue Jia, Zhao Nan, Zhu Tingshao

2020-Mar-19

cognition, emotion, mental health, public health emergencies, word frequency analysis

General General

Comparative study of deep learning models for optical coherence tomography angiography.

In Biomedical optics express

Optical coherence tomography angiography (OCTA) is a promising imaging modality for microvasculature studies. Meanwhile, deep learning has achieved rapid development in image-to-image translation tasks. Some studies have proposed applying deep learning models to OCTA reconstruction and have obtained preliminary results. However, current studies are mostly limited to a few specific deep neural networks. In this paper, we conducted a comparative study to investigate OCTA reconstruction using deep learning models. Four representative network architectures including single-path models, U-shaped models, generative adversarial network (GAN)-based models and multi-path models were investigated on a dataset of OCTA images acquired from rat brains. Three potential solutions were also investigated to study the feasibility of improving performance. The results showed that U-shaped models and multi-path models are two suitable architectures for OCTA reconstruction. Furthermore, merging phase information should be the potential improving direction in further research.

Jiang Zhe, Huang Zhiyu, Qiu Bin, Meng Xiangxi, You Yunfei, Liu Xi, Liu Gangjun, Zhou Chuangqing, Yang Kun, Maier Andreas, Ren Qiushi, Lu Yanye

2020-Mar-01

Radiology Radiology

Tumor detection of the thyroid and salivary glands using hyperspectral imaging and deep learning.

In Biomedical optics express

The performance of hyperspectral imaging (HSI) for tumor detection is investigated in ex-vivo specimens from the thyroid (N = 200) and salivary glands (N = 16) from 82 patients. Tissues were imaged with HSI in broadband reflectance and autofluorescence modes. For comparison, the tissues were imaged with two fluorescent dyes. Additionally, HSI was used to synthesize three-band RGB multiplex images to represent the human-eye response and Gaussian RGBs, which are referred to as HSI-synthesized RGB images. Using histological ground truths, deep learning algorithms were developed for tumor detection. For the classification of thyroid tumors, HSI-synthesized RGB images achieved the best performance with an AUC score of 0.90. In salivary glands, HSI had the best performance with 0.92 AUC score. This study demonstrates that HSI could aid surgeons and pathologists in detecting tumors of the thyroid and salivary glands.

Halicek Martin, Dormer James D, Little James V, Chen Amy Y, Fei Baowei

2020-Mar-01

General General

Analysis of CNN-based remote-PPG to understand limitations and sensitivities.

In Biomedical optics express

Deep learning based on convolutional neural network (CNN) has shown promising results in various vision-based applications, recently also in camera-based vital signs monitoring. The CNN-based photoplethysmography (PPG) extraction has, so far, been focused on performance rather than understanding. In this paper, we try to answer four questions with experiments aiming at improving our understanding of this methodology as it gains popularity. We conclude that the network exploits the blood absorption variation to extract the physiological signals, and that the choice and parameters (phase, spectral content, etc.) of the reference-signal may be more critical than anticipated. The availability of multiple convolutional kernels is necessary for CNN to arrive at a flexible channel combination through the spatial operation, but may not provide the same motion-robustness as a multi-site measurement using knowledge-based PPG extraction. We also find that the PPG-related prior knowledge may still be helpful for the CNN-based PPG extraction, and recommend further investigation of hybrid CNN-based methods that include prior knowledge in their design.

Zhan Qi, Wang Wenjin, de Haan Gerard

2020-Mar-01

Surgery Surgery

Real-time diagnosis and visualization of tumor margins in excised breast specimens using fluorescence lifetime imaging and machine learning.

In Biomedical optics express

Tumor-free surgical margins are critical in breast-conserving surgery. In up to 38% of the cases, however, patients undergo a second surgery since malignant cells are found at the margins of the excised resection specimen. Thus, advanced imaging tools are needed to ensure clear margins at the time of surgery. The objective of this study was to evaluate a random forest classifier that makes use of parameters derived from point-scanning label-free fluorescence lifetime imaging (FLIm) measurements of breast specimens as a means to diagnose tumor at the resection margins and to enable an intuitive visualization of a probabilistic classifier on tissue specimen. FLIm data from fresh lumpectomy and mastectomy specimens from 18 patients were used in this study. The supervised training was based on a previously developed registration technique between autofluorescence imaging data and cross-sectional histology slides. A pathologist's histology annotations provide the ground truth to distinguish between adipose, fibrous, and tumor tissue. Current results demonstrate the ability of this approach to classify the tumor with 89% sensitivity and 93% specificity and to rapidly (∼ 20 frames per second) overlay the probabilistic classifier overlaid on excised breast specimens using an intuitive color scheme. Furthermore, we show an iterative imaging refinement that allows surgeons to switch between rapid scans with a customized, low spatial resolution to quickly cover the specimen and slower scans with enhanced resolution (400 μm per point measurement) in suspicious regions where more details are required. In summary, this technique provides high diagnostic prediction accuracy, rapid acquisition, adaptive resolution, nondestructive probing, and facile interpretation of images, thus holding potential for clinical breast imaging based on label-free FLIm.

Unger Jakob, Hebisch Christoph, Phipps Jennifer E, Lagarto João L, Kim Hanna, Darrow Morgan A, Bold Richard J, Marcu Laura

2020-Mar-01

General General

PhUn-Net: ready-to-use neural network for unwrapping quantitative phase images of biological cells.

In Biomedical optics express

We present a deep-learning approach for solving the problem of 2π phase ambiguities in two-dimensional quantitative phase maps of biological cells, using a multi-layer encoder-decoder residual convolutional neural network. We test the trained network, PhUn-Net, on various types of biological cells, captured with various interferometric setups, as well as on simulated phantoms. These tests demonstrate the robustness and generality of the network, even for cells of different morphologies or different illumination conditions than PhUn-Net has been trained on. In this paper, for the first time, we make the trained network publicly available in a global format, such that it can be easily deployed on every platform, to yield fast and robust phase unwrapping, not requiring prior knowledge or complex implementation. By this, we expect our phase unwrapping approach to be widely used, substituting conventional and more time-consuming phase unwrapping algorithms.

Dardikman-Yoffe Gili, Roitshtain Darina, Mirsky Simcha K, Turko Nir A, Habaza Mor, Shaked Natan T

2020-Feb-01

General General

Automatic recognition of breast invasive ductal carcinoma based on terahertz spectroscopy with wavelet packet transform and machine learning.

In Biomedical optics express

We demonstrate an automatic recognition strategy for terahertz (THz) pulsed signals of breast invasive ductal carcinoma (IDC) based on a wavelet entropy feature extraction and a machine learning classifier. The wavelet packet transform was implemented into the complexity analysis of the transmission THz signal from a breast tissue sample. A novel index of energy to Shannon entropy ratio (ESER) was proposed to distinguish different tissues. Furthermore, the principal component analysis (PCA) method and machine learning classifier were further adopted and optimized for automatic classification of the THz signal from breast IDC sample. The areas under the receiver operating characteristic curves are all larger than 0.89 for the three adopted classifiers. The best breast IDC recognition performance is with the precision, sensitivity and specificity of 92.85%, 89.66% and 96.67%, respectively. The results demonstrate the effectiveness of the ESER index together with the machine learning classifier for automatically identifying different breast tissues.

Liu Wenquan, Zhang Rui, Ling Yu, Tang Hongping, She Rongbin, Wei Guanglu, Gong Xiaojing, Lu Yuanfu

2020-Feb-01

General General

Agents of change: Understanding the therapeutic processes associated with the helpfulness of therapy for mental health problems with relational agent MYLO.

In Digital health

Objective : To understand the therapeutic processes associated with the helpfulness of an online relational agent intervention, Manage Your Life Online (MYLO).

Methods : Fifteen participants experiencing a mental health related problem used Manage Your Life Online for 2 weeks. At follow-up, the participants each identified two helpful and two unhelpful questions posed by Manage Your Life Online within a single intervention session. Qualitative interviews were conducted and analyzed using thematic and content analysis to gain insight into the process of therapy with Manage Your Life Online.

Results : MYLO appeared acceptable to participants with a range of presenting problems. Questions enabling free expression, increased awareness, and new insights were key to a helpful intervention. The findings were consistent with the core processes of therapeutic change, according to Perceptual Control Theory, a unifying theory of psychological distress. Questions that elicited intense emotions, were repetitive, confusing, or inappropriate were identified as unhelpful and were associated with disengagement or loss of faith in Manage Your Life Online.

Conclusions : The findings provide insight into the likely core therapy processes experienced as helpful or hindering and outlines further ways to optimize acceptability of Manage Your Life Online.

Gaffney Hannah, Mansell Warren, Tai Sara

Mental health, artificial intelligence, computer-assisted, conversational agent, psychotherapeutic processes, psychotherapy, relational agent, therapy

General General

The CARESSES study protocol: testing and evaluating culturally competent socially assistive robots among older adults residing in long term care homes through a controlled experimental trial.

In Archives of public health = Archives belges de sante publique

Background : This article describes the design of an intervention study that focuses on whether and to what degree culturally competent social robots can improve health and well-being related outcomes among older adults residing long-term care homes. The trial forms the final stage of the international, multidisciplinary CARESSES project aimed at designing, developing and evaluating culturally competent robots that can assist older people according to the culture of the individual they are supporting. The importance of cultural competence has been demonstrated in previous nursing literature to be key towards improving health outcomes among patients.

Method : This study employed a mixed-method, single-blind, parallel-group controlled before-and-after experimental trial design that took place in England and Japan. It aimed to recruit 45 residents of long-term care homes aged ≥65 years, possess sufficient cognitive and physical health and who self-identify with the English, Indian or Japanese culture (n = 15 each). Participants were allocated to either the experimental group, control group 1 or control group 2 (all n = 15). Those allocated to the experimental group or control group 1 received a Pepper robot programmed with the CARESSES culturally competent artificial intelligence (experimental group) or a limited version of this software (control group 1) for 18 h across 2 weeks. Participants in control group 2 did not receive a robot and continued to receive care as usual. Participants could also nominate their informal carer(s) to participate. Quantitative data collection occurred at baseline, after 1 week of use, and after 2 weeks of use with the latter time-point also including qualitative semi-structured interviews that explored their experience and perceptions further. Quantitative outcomes of interest included perceptions of robotic cultural competence, health-related quality of life, loneliness, user satisfaction, attitudes towards robots and caregiver burden.

Discussion : This trial adds to the current preliminary and limited pool of evidence regarding the benefits of socially assistive robots for older adults which to date indicates considerable potential for improving outcomes. It is the first to assess whether and to what extent cultural competence carries importance in generating improvements to well-being.

Trial registration : Name of the registry: ClinicalTrials.govTrial registration number: NCT03756194.Date of registration: 28 November 2018. URL of trial registry record.

Papadopoulos Chris, Hill Tetiana, Battistuzzi Linda, Castro Nina, Nigath Abiha, Randhawa Gurch, Merton Len, Kanoria Sanjeev, Kamide Hiroko, Chong Nak-Young, Hewson David, Davidson Rosemary, Sgorbissa Antonio

2020

Artificial intelligence, CARESSES, Cultural competence, Culturally competent robots, Social robotics, Study protocol

General General

Bio-inspired dimensionality reduction for Parkinson's disease (PD) classification.

In Health information science and systems

Given the demand for developing the efficient Machine Learning (ML) classification models for healthcare data, and the potentiality of Bio-Inspired Optimization (BIO) algorithms to tackle the problem of high dimensional data, we investigate the range of ML classification models trained with the optimal subset of features of PD data set for efficient PD classification. We used two BIO algorithms, Genetic Algorithm (GA) and Binary Particle Swarm Optimization (BPSO), to determine the optimal subset of features of PD data set. The data set chosen for investigation comprises 756 observations (rows or records) taken over 755 attributes (columns or dimensions or features) from 252 PD patients. We employed MaxAbsolute feature scaling method to normalize the data and one hold cross-validation method to avoid biased results. Accordingly, the data is split in to training and testing set in the ratio of 70% and 30%. Subsequently, we employed GA and BPSO algorithms separately on 11 ML classifiers (Logistic Regression (LR), linear Support Vector Machine (lSVM), radial basis function Support Vector Machine (rSVM), Gaussian Naïve Bayes (GNB), Gaussian Process Classifier (GPC), k-Nearest Neighbor (kNN), Decision Tree (DT), Random Forest (RF), Multilayer Perceptron (MLP), Ada Boost (AB) and Quadratic Discriminant Analysis (QDA)), to determine the optimal subset of features (reduction of dimensionality) contributing to the highest classification accuracy. Among all the bio-inspired ML classifiers employed: GA-inspired MLP produced the maximum dimensionality reduction of 52.32% by selecting only 359 features and delivering 85.1% of the classification accuracy; GA-inspired AB delivered the maximum classification accuracy of 90.7% producing the dimensionality reduction of 41.43% by selecting only 441 features; And, BPSO-inspired GNB produced the maximum dimensionality reduction of 47.14% by selecting 396 features and delivering the classification accuracy of 79.3%; BPSOMLP delivered the maximum classification accuracy of 89% and produced 46.48% of the dimensionality reduction by selecting only 403 features.

Pasha Akram, Latha P H

2020-Dec

Binary particle swarm optimization, Bio-inspired computing, Classification, Data mining, Dimensionality reduction, Feature selection, Genetic algorithm, Machine learning

Pathology Pathology

101 Dothideomycetes genomes: A test case for predicting lifestyles and emergence of pathogens.

In Studies in mycology

Dothideomycetes is the largest class of kingdom Fungi and comprises an incredible diversity of lifestyles, many of which have evolved multiple times. Plant pathogens represent a major ecological niche of the class Dothideomycetes and they are known to infect most major food crops and feedstocks for biomass and biofuel production. Studying the ecology and evolution of Dothideomycetes has significant implications for our fundamental understanding of fungal evolution, their adaptation to stress and host specificity, and practical implications with regard to the effects of climate change and on the food, feed, and livestock elements of the agro-economy. In this study, we present the first large-scale, whole-genome comparison of 101 Dothideomycetes introducing 55 newly sequenced species. The availability of whole-genome data produced a high-confidence phylogeny leading to reclassification of 25 organisms, provided a clearer picture of the relationships among the various families, and indicated that pathogenicity evolved multiple times within this class. We also identified gene family expansions and contractions across the Dothideomycetes phylogeny linked to ecological niches providing insights into genome evolution and adaptation across this group. Using machine-learning methods we classified fungi into lifestyle classes with >95 % accuracy and identified a small number of gene families that positively correlated with these distinctions. This can become a valuable tool for genome-based prediction of species lifestyle, especially for rarely seen and poorly studied species.

Haridas S, Albert R, Binder M, Bloem J, LaButti K, Salamov A, Andreopoulos B, Baker S E, Barry K, Bills G, Bluhm B H, Cannon C, Castanera R, Culley D E, Daum C, Ezra D, González J B, Henrissat B, Kuo A, Liang C, Lipzen A, Lutzoni F, Magnuson J, Mondo S J, Nolan M, Ohm R A, Pangilinan J, Park H-J, Ramírez L, Alfaro M, Sun H, Tritt A, Yoshinaga Y, Zwiers L-H, Turgeon B G, Goodwin S B, Spatafora J W, Crous P W, Grigoriev I V

2020-Jun

Aulographales Crous, Spatafora, Haridas & Grigoriev, Coniosporiaceae Crous, Spatafora, Haridas & Grigoriev, Coniosporiales Crous, Spatafora, Haridas & Grigoriev, Eremomycetales Crous, Spatafora, Haridas & Grigoriev, Fungal evolution, Genome-based prediction, Lineolataceae Crous, Spatafora, Haridas & Grigoriev, Lineolatales Crous, Spatafora, Haridas & Grigoriev, Machine-learning, New taxa, Rhizodiscinaceae Crous, Spatafora, Haridas & Grigoriev

General General

Modelling of Urban Air Pollutant Concentrations with Artificial Neural Networks Using Novel Input Variables.

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

Since operating urban air quality stations is not only time consuming but also costly, and because air pollutants can cause serious health problems, this paper presents the hourly prediction of ten air pollutant concentrations (CO2, NH3, NO, NO2, NOx, O3, PM1, PM2.5, PM10 and PN10) in a street canyon in Münster using an artificial neural network (ANN) approach. Special attention was paid to comparing three predictor options representing the traffic volume: we included acoustic sound measurements (sound), the total number of vehicles (traffic), and the hour of the day and the day of the week (time) as input variables and then compared their prediction powers. The models were trained, validated and tested to evaluate their performance. Results showed that the predictions of the gaseous air pollutants NO, NO2, NOx, and O3 reveal very good agreement with observations, whereas predictions for particle concentrations and NH3 were less successful, indicating that these models can be improved. All three input variable options (sound, traffic and time) proved to be suitable and showed distinct strengths for modelling various air pollutant concentrations.

Goulier Laura, Paas Bastian, Ehrnsperger Laura, Klemm Otto

2020-Mar-19

ANN, acoustic, ammonia, deep learning, nitrogen oxides, ozone, particulate matter, prediction, sound, traffic

Public Health Public Health

The Impact of COVID-19 Epidemic Declaration on Psychological Consequences: A Study on Active Weibo Users.

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

COVID-19 (Corona Virus Disease 2019) has significantly resulted in a large number of psychological consequences. The aim of this study is to explore the impacts of COVID-19 on people's mental health, to assist policy makers to develop actionable policies, and help clinical practitioners (e.g., social workers, psychiatrists, and psychologists) provide timely services to affected populations. We sample and analyze the Weibo posts from 17,865 active Weibo users using the approach of Online Ecological Recognition (OER) based on several machine-learning predictive models. We calculated word frequency, scores of emotional indicators (e.g., anxiety, depression, indignation, and Oxford happiness) and cognitive indicators (e.g., social risk judgment and life satisfaction) from the collected data. The sentiment analysis and the paired sample t-test were performed to examine the differences in the same group before and after the declaration of COVID-19 on 20 January, 2020. The results showed that negative emotions (e.g., anxiety, depression and indignation) and sensitivity to social risks increased, while the scores of positive emotions (e.g., Oxford happiness) and life satisfaction decreased. People were concerned more about their health and family, while less about leisure and friends. The results contribute to the knowledge gaps of short-term individual changes in psychological conditions after the outbreak. It may provide references for policy makers to plan and fight against COVID-19 effectively by improving stability of popular feelings and urgently prepare clinical practitioners to deliver corresponding therapy foundations for the risk groups and affected people.

Li Sijia, Wang Yilin, Xue Jia, Zhao Nan, Zhu Tingshao

2020-Mar-19

cognition, emotion, mental health, public health emergencies, word frequency analysis

General General

Mapping the Landscape of Artificial Intelligence Applications against COVID-19

ArXiv Preprint

COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization, with over 294,000 cases as of March 22nd 2020. In this review, we present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence, to tackle many aspects of the COVID-19 crisis at different scales including molecular, medical and epidemiological applications. We finish with a discussion of promising future directions of research and the tools and resources needed to facilitate AI research.

Joseph Bullock, Alexandra, Luccioni, Katherine Hoffmann Pham, Cynthia Sin Nga Lam, Miguel Luengo-Oroz

2020-03-25

Radiology Radiology

Basal and Acetazolamide Brain Perfusion SPECT in Internal Carotid Artery Stenosis.

In Nuclear medicine and molecular imaging

Internal carotid artery (ICA) stenosis including Moyamoya disease needs revascularization when hemodynamic insufficiency is validated. Vascular reserve impairment was the key to find the indication for endarterectomy/bypass surgery in the atherosclerotic ICA stenosis and to determine the indication, treatment effect, and prognosis in Moyamoya diseases. Vascular reserve was quantitatively assessed by 1-day split-dose I-123 IMP basal/acetazolamide SPECT in Japan or by Tc-99m HMPAO SPECT in other countries using qualitative or semi-quantitative method. We summarized the development of 1-day basal/ acetazolamide brain perfusion SPECT for ICA stenosis, both quantitative and qualitative methods, and their methodological issues regarding (1) acquisition protocol; (2) qualitative assessment, either visual or deep learning-based; (3) clinical use for atherosclerotic ICA steno-occlusive diseases and mostly Moyamoya diseases; and (4) their impact on the choice of treatment options. Trials to use CT perfusion or perfusion MRI using contrast materials or arterial spin labeling were briefly discussed in their endeavor to use basal studies alone to replace acetazolamide-challenge SPECT. Theoretical and practical issues imply that basal perfusion evaluation, no matter how much sophisticated, will not disclose vascular reserve. Acetazolamide rarely causes serious adverse reactions but included fatality, and now, we need to monitor patients closely in acetazolamide-challenge studies.

Wong Teck Huat, Shagera Qaid Ahmed, Ryoo Hyun Gee, Ha Seunggyun, Lee Dong Soo

2020-Feb

Acetazolamide SPECT, Brain perfusion, Carotid artery stenosis, Deep learning, Moyamoya disease, Vascular reserve

General General

SpikeSegNet-a deep learning approach utilizing encoder-decoder network with hourglass for spike segmentation and counting in wheat plant from visual imaging.

In Plant methods

Background : High throughput non-destructive phenotyping is emerging as a significant approach for phenotyping germplasm and breeding populations for the identification of superior donors, elite lines, and QTLs. Detection and counting of spikes, the grain bearing organs of wheat, is critical for phenomics of a large set of germplasm and breeding lines in controlled and field conditions. It is also required for precision agriculture where the application of nitrogen, water, and other inputs at this critical stage is necessary. Further, counting of spikes is an important measure to determine yield. Digital image analysis and machine learning techniques play an essential role in non-destructive plant phenotyping analysis.

Results : In this study, an approach based on computer vision, particularly object detection, to recognize and count the number of spikes of the wheat plant from the digital images is proposed. For spike identification, a novel deep-learning network, SpikeSegNet, has been developed by combining two proposed feature networks: Local Patch extraction Network (LPNet) and Global Mask refinement Network (GMRNet). In LPNet, the contextual and spatial features are learned at the local patch level. The output of LPNet is a segmented mask image, which is further refined at the global level using GMRNet. Visual (RGB) images of 200 wheat plants were captured using LemnaTec imaging system installed at Nanaji Deshmukh Plant Phenomics Centre, ICAR-IARI, New Delhi. The precision, accuracy, and robustness (F1 score) of the proposed approach for spike segmentation are found to be 99.93%, 99.91%, and 99.91%, respectively. For counting the number of spikes, "analyse particles"-function of imageJ was applied on the output image of the proposed SpikeSegNet model. For spike counting, the average precision, accuracy, and robustness are 99%, 95%, and 97%, respectively. SpikeSegNet approach is tested for robustness with illuminated image dataset, and no significant difference is observed in the segmentation performance.

Conclusion : In this study, a new approach called as SpikeSegNet has been proposed based on combined digital image analysis and deep learning techniques. A dedicated deep learning approach has been developed to identify and count spikes in the wheat plants. The performance of the approach demonstrates that SpikeSegNet is an effective and robust approach for spike detection and counting. As detection and counting of wheat spikes are closely related to the crop yield, and the proposed approach is also non-destructive, it is a significant step forward in the area of non-destructive and high-throughput phenotyping of wheat.

Misra Tanuj, Arora Alka, Marwaha Sudeep, Chinnusamy Viswanathan, Rao Atmakuri Ramakrishna, Jain Rajni, Sahoo Rabi Narayan, Ray Mrinmoy, Kumar Sudhir, Raju Dhandapani, Jha Ranjeet Ranjan, Nigam Aditya, Goel Swati

2020

Deep learning, Encoder-decoder deep network, Image analysis, Non-destructive plant phenotyping, Wheat spikes identification and count

Radiology Radiology

Augmenting Interpretation of Chest Radiographs With Deep Learning Probability Maps.

In Journal of thoracic imaging

PURPOSE : Pneumonia is a common clinical diagnosis for which chest radiographs are often an important part of the diagnostic workup. Deep learning has the potential to expedite and improve the clinical interpretation of chest radiographs. While earlier approaches have emphasized the feasibility of "binary classification" to accomplish this task, alternative strategies may be possible. We explore the feasibility of a "semantic segmentation" deep learning approach to highlight the potential foci of pneumonia on frontal chest radiographs.

MATERIALS AND METHODS : In this retrospective study, we trained a U-net convolutional neural network (CNN) to predict pixel-wise probability maps for pneumonia using a public data set provided by the Radiological Society of North America (RSNA) comprised of 22,000 radiographs and radiologist-defined bounding boxes. We reserved 3684 radiographs as an independent validation data set and assessed overall performance for localization using Dice overlap and classification performance using the area under the receiver-operator characteristic curve.

RESULTS : For classification/detection of pneumonia, area under the receiver-operator characteristic curve on frontal radiographs was 0.854 with a sensitivity of 82.8% and specificity of 72.6%. Using this strategy of neural network training, probability maps localized pneumonia to lung parenchyma for essentially all validation cases. For segmentation of pneumonia for positive cases, predicted probability maps had a mean Dice score (±SD) of 0.603±0.204, and 60.0% of these had a Dice score >0.5.

CONCLUSIONS : A "semantic segmentation" deep learning approach can provide a probabilistic map to assist in the diagnosis of pneumonia. In combination with the patient's history, clinical findings and other imaging, this strategy may help expedite and improve diagnosis.

Hurt Brian, Yen Andrew, Kligerman Seth, Hsiao Albert

2020-Mar-20

General General

Artificial Intelligence in Diagnostic Imaging: Status Quo, Challenges, and Future Opportunities.

In Journal of thoracic imaging

In this review article, the current and future impact of artificial intelligence (AI) technologies on diagnostic imaging is discussed, with a focus on cardio-thoracic applications. The processing of imaging data is described at 4 levels of increasing complexity and wider implications. At the examination level, AI aims at improving, simplifying, and standardizing image acquisition and processing. Systems for AI-driven automatic patient iso-centering before a computed tomography (CT) scan, patient-specific adaptation of image acquisition parameters, and creation of optimized and standardized visualizations, for example, automatic rib-unfolding, are discussed. At the reading and reporting levels, AI focuses on automatic detection and characterization of features and on automatic measurements in the images. A recently introduced AI system for chest CT imaging is presented that reports specific findings such as nodules, low-attenuation parenchyma, and coronary calcifications, including automatic measurements of, for example, aortic diameters. At the prediction and prescription levels, AI focuses on risk prediction and stratification, as opposed to merely detecting, measuring, and quantifying images. An AI-based approach for individualizing radiation dose in lung stereotactic body radiotherapy is discussed. The digital twin is presented as a concept of individualized computational modeling of human physiology, with AI-based CT-fractional flow reserve modeling as a first example. Finally, at the cohort and population analysis levels, the focus of AI shifts from clinical decision-making to operational decisions.

Sharma Puneet, Suehling Michael, Flohr Thomas, Comaniciu Dorin

2020-Mar-20

Cardiology Cardiology

Detecting Patient Deterioration Using Artificial Intelligence in a Rapid Response System.

In Critical care medicine ; h5-index 87.0

OBJECTIVES : As the performance of a conventional track and trigger system in a rapid response system has been unsatisfactory, we developed and implemented an artificial intelligence for predicting in-hospital cardiac arrest, denoted the deep learning-based early warning system. The purpose of this study was to compare the performance of an artificial intelligence-based early warning system with that of conventional methods in a real hospital situation.

DESIGN : Retrospective cohort study.

SETTING : This study was conducted at a hospital in which deep learning-based early warning system was implemented.

PATIENTS : We reviewed the records of adult patients who were admitted to the general ward of our hospital from April 2018 to March 2019.

INTERVENTIONS : The study population included 8,039 adult patients. A total 83 events of deterioration occurred during the study period. The outcome was events of deterioration, defined as cardiac arrest and unexpected ICU admission. We defined a true alarm as an alarm occurring within 0.5-24 hours before a deteriorating event.

MEASUREMENTS AND MAIN RESULTS : We used the area under the receiver operating characteristic curve, area under the precision-recall curve, number needed to examine, and mean alarm count per day as comparative measures. The deep learning-based early warning system (area under the receiver operating characteristic curve, 0.865; area under the precision-recall curve, 0.066) outperformed the modified early warning score (area under the receiver operating characteristic curve, 0.682; area under the precision-recall curve, 0.010) and reduced the number needed to examine and mean alarm count per day by 69.2% and 59.6%, respectively. At the same specificity, deep learning-based early warning system had up to 257% higher sensitivity than conventional methods.

CONCLUSIONS : The developed artificial intelligence based on deep-learning, deep learning-based early warning system, accurately predicted deterioration of patients in a general ward and outperformed conventional methods. This study showed the potential and effectiveness of artificial intelligence in an rapid response system, which can be applied together with electronic health records. This will be a useful method to identify patients with deterioration and help with precise decision-making in daily practice.

Cho Kyung-Jae, Kwon Oyeon, Kwon Joon-Myoung, Lee Yeha, Park Hyunho, Jeon Ki-Hyun, Kim Kyung-Hee, Park Jinsik, Oh Byung-Hee

2020-Apr

Pathology Pathology

Artificial intelligence driven next-generation renal histomorphometry.

In Current opinion in nephrology and hypertension

PURPOSE OF REVIEW : Successful integration of artificial intelligence into extant clinical workflows is contingent upon a number of factors including clinician comprehension and interpretation of computer vision. This article discusses how image analysis and machine learning have enabled comprehensive characterization of kidney morphology for development of automated diagnostic and prognostic renal pathology applications.

RECENT FINDINGS : The primordial digital pathology informatics work employed classical image analysis and machine learning to prognosticate renal disease. Although this classical approach demonstrated tremendous potential, subsequent advancements in hardware technology rendered artificial neural networks '(ANNs) the method of choice for machine vision in computational pathology'. Offering rapid and reproducible detection, characterization and classification of kidney morphology, ANNs have facilitated the development of diagnostic and prognostic applications. In addition, modern machine learning with ANNs has revealed novel biomarkers in kidney disease, demonstrating the potential for machine vision to elucidate novel pathologic mechanisms beyond extant clinical knowledge.

SUMMARY : Despite the revolutionary developments potentiated by modern machine learning, several challenges remain, including data quality control and curation, image annotation and ontology, integration of multimodal data and interpretation of machine vision or 'opening the black box'. Resolution of these challenges will not only revolutionize diagnostic pathology but also pave the way for precision medicine and integration of artificial intelligence in the process of care.

Santo Briana A, Rosenberg Avi Z, Sarder Pinaki

2020-Mar-19

General General

Constructing Inpatient Pressure Injury Prediction Models Using Machine Learning Techniques.

In Computers, informatics, nursing : CIN

The incidence rate of pressure injury is a critical nursing quality indicator in clinic care; consequently, factors causing pressure injury are diverse and complex. The early prevention of pressure injury and monitoring of these complex high-risk factors are critical to reduce the patients' pain, prevent further surgical treatment, avoid prolonged hospital stay, decrease the risk of wound infection, and lower associated medical costs and expenses. Although a number of risk assessment scales of pressure injury have been adopted in various countries, their criteria are set for specific populations, which may not be suitable for the medical care systems of other countries. This study constructs three prediction models of inpatient pressure injury using machine learning techniques, including decision tree, logistic regression, and random forest. A total of 11 838 inpatient records were collected, and 30 sets of training samples were adopted for data analysis in the experiment. The experimental results and evaluations of the models suggest that the prediction model built using random forest had most favorable classification performance of 0.845. The critical risk factors for pressure injury identified in this study were skin integrity, systolic blood pressure, expression ability, capillary refill time, and level of consciousness.

Hu Ya-Han, Lee Yi-Lien, Kang Ming-Feng, Lee Pei-Ju

2020-Mar-19

Ophthalmology Ophthalmology

The retina revolution: signaling pathway therapies, genetic therapies, mitochondrial therapies, artificial intelligence.

In Current opinion in ophthalmology

PURPOSE OF REVIEW : The aim of this article is to review and discuss the history, current state, and future implications of promising biomedical offerings in the field of retina.

RECENT FINDINGS : The technologies discussed are some of the more recent promising biomedical developments within the field of retina. There is a US Food and Drug Administration-approved gene therapy product and artificial intelligence device for retina, with many other offerings in the pipeline.

SUMMARY : Signaling pathway therapies, genetic therapies, mitochondrial therapies, and artificial intelligence have shaped retina care as we know it and are poised to further impact the future of retina care. Retina specialists have the privilege and responsibility of shaping this future for the visual health of current and future generations.

Wood Edward H, Korot Edward, Storey Philip P, Muscat Stephanie, Williams George A, Drenser Kimberly A

2020-Mar-19

General General

Racial disparities in automated speech recognition.

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

Automated speech recognition (ASR) systems, which use sophisticated machine-learning algorithms to convert spoken language to text, have become increasingly widespread, powering popular virtual assistants, facilitating automated closed captioning, and enabling digital dictation platforms for health care. Over the last several years, the quality of these systems has dramatically improved, due both to advances in deep learning and to the collection of large-scale datasets used to train the systems. There is concern, however, that these tools do not work equally well for all subgroups of the population. Here, we examine the ability of five state-of-the-art ASR systems-developed by Amazon, Apple, Google, IBM, and Microsoft-to transcribe structured interviews conducted with 42 white speakers and 73 black speakers. In total, this corpus spans five US cities and consists of 19.8 h of audio matched on the age and gender of the speaker. We found that all five ASR systems exhibited substantial racial disparities, with an average word error rate (WER) of 0.35 for black speakers compared with 0.19 for white speakers. We trace these disparities to the underlying acoustic models used by the ASR systems as the race gap was equally large on a subset of identical phrases spoken by black and white individuals in our corpus. We conclude by proposing strategies-such as using more diverse training datasets that include African American Vernacular English-to reduce these performance differences and ensure speech recognition technology is inclusive.

Koenecke Allison, Nam Andrew, Lake Emily, Nudell Joe, Quartey Minnie, Mengesha Zion, Toups Connor, Rickford John R, Jurafsky Dan, Goel Sharad

2020-Mar-23

fair machine learning, natural language processing, speech-to-text

General General

Reporting quality of studies using machine learning models for medical diagnosis: a systematic review.

In BMJ open

AIMS : We conducted a systematic review assessing the reporting quality of studies validating models based on machine learning (ML) for clinical diagnosis, with a specific focus on the reporting of information concerning the participants on which the diagnostic task was evaluated on.

METHOD : Medline Core Clinical Journals were searched for studies published between July 2015 and July 2018. Two reviewers independently screened the retrieved articles, a third reviewer resolved any discrepancies. An extraction list was developed from the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guideline. Two reviewers independently extracted the data from the eligible articles. Third and fourth reviewers checked, verified the extracted data as well as resolved any discrepancies between the reviewers.

RESULTS : The search results yielded 161 papers, of which 28 conformed to the eligibility criteria. Detail of data source was reported in 24 of the 28 papers. For all of the papers, the set of patients on which the ML-based diagnostic system was evaluated was partitioned from a larger dataset, and the method for deriving such set was always reported. Information on the diagnostic/non-diagnostic classification was reported well (23/28). The least reported items were the use of reporting guideline (0/28), distribution of disease severity (8/28 patient flow diagram (10/28) and distribution of alternative diagnosis (10/28). A large proportion of studies (23/28) had a delay between the conduct of the reference standard and ML tests, while one study did not and four studies were unclear. For 15 studies, it was unclear whether the evaluation group corresponded to the setting in which the ML test will be applied to.

CONCLUSION : All studies in this review failed to use reporting guidelines, and a large proportion of them lacked adequate detail on participants, making it difficult to replicate, assess and interpret study findings.

PROSPERO REGISTRATION NUMBER : CRD42018099167.

Yusuf Mohamed, Atal Ignacio, Li Jacques, Smith Philip, Ravaud Philippe, Fergie Martin, Callaghan Michael, Selfe James

2020-Mar-23

clinical prediction, machine learning, medical diagnosis, reporting quality

Radiology Radiology

Deep Learning Based High-Resolution Reconstruction of Trabecular Bone Microstructures from Low-Resolution CT Scans using GAN-CIRCLE.

In Proceedings of SPIE--the International Society for Optical Engineering

Osteoporosis is a common age-related disease characterized by reduced bone density and increased fracture-risk. Microstructural quality of trabecular bone (Tb), commonly found at axial skeletal sites and at the end of long bones, is an important determinant of bone-strength and fracture-risk. High-resolution emerging CT scanners enable in vivo measurement of Tb microstructures at peripheral sites. However, resolution-dependence of microstructural measures and wide resolution-discrepancies among various CT scanners together with rapid upgrades in technology warrant data harmonization in CT-based cross-sectional and longitudinal bone studies. This paper presents a deep learning-based method for high-resolution reconstruction of Tb microstructures from low-resolution CT scans using GAN-CIRCLE. A network was developed and evaluated using post-registered ankle CT scans of nineteen volunteers on both low- and high-resolution CT scanners. 9,000 matching pairs of low- and high-resolution patches of size 64×64 were randomly harvested from ten volunteers for training and validation. Another 5,000 matching pairs of patches from nine other volunteers were used for evaluation. Quantitative comparison shows that predicted high-resolution scans have significantly improved structural similarity index (p < 0.01) with true high-resolution scans as compared to the same metric for low-resolution data. Different Tb microstructural measures such as thickness, spacing, and network area density are also computed from low- and predicted high-resolution images, and compared with the values derived from true high-resolution scans. Thickness and network area measures from predicted images showed higher agreement with true high-resolution CT (CCC = [0.95, 0.91]) derived values than the same measures from low-resolution images (CCC = [0.72, 0.88]).

Guha Indranil, Nadeem Syed Ahmed, You Chenyu, Zhang Xiaoliu, Levy Steven M, Wang Ge, Torner James C, Saha Punam K

2020-Feb

GAN-CIRCLE, deep learning, high-resolution reconstruction, microstructure, osteoporosis, trabecular bone

General General

Sport and exercise genomics: the FIMS 2019 consensus statement update.

In British journal of sports medicine ; h5-index 104.0

Rapid advances in technologies in the field of genomics such as high throughput DNA sequencing, big data processing by machine learning algorithms and gene-editing techniques are expected to make precision medicine and gene-therapy a greater reality. However, this development will raise many important new issues, including ethical, moral, social and privacy issues. The field of exercise genomics has also advanced by incorporating these innovative technologies. There is therefore an urgent need for guiding references for sport and exercise genomics to allow the necessary advancements in this field of sport and exercise medicine, while protecting athletes from any invasion of privacy and misuse of their genomic information. Here, we update a previous consensus and develop a guiding reference for sport and exercise genomics based on a SWOT (Strengths, Weaknesses, Opportunities and Threats) analysis. This SWOT analysis and the developed guiding reference highlight the need for scientists/clinicians to be well-versed in ethics and data protection policy to advance sport and exercise genomics without compromising the privacy of athletes and the efforts of international sports federations. Conducting research based on the present guiding reference will mitigate to a great extent the risks brought about by inappropriate use of genomic information and allow further development of sport and exercise genomics in accordance with best ethical standards and international data protection principles and policies. This guiding reference should regularly be updated on the basis of new information emerging from the area of sport and exercise medicine as well as from the developments and challenges in genomics of health and disease in general in order to best protect the athletes, patients and all other relevant stakeholders.

Tanisawa Kumpei, Wang Guan, Seto Jane, Verdouka Ioanna, Twycross-Lewis Richard, Karanikolou Antonia, Tanaka Masashi, Borjesson Mats, Di Luigi Luigi, Dohi Michiko, Wolfarth Bernd, Swart Jeroen, Bilzon James Lee John, Badtieva Victoriya, Papadopoulou Theodora, Casasco Maurizio, Geistlinger Michael, Bachl Norbert, Pigozzi Fabio, Pitsiladis Yannis

2020-Mar-22

genes, genetic testing, genetics

Cardiology Cardiology

Applications of artificial intelligence in multimodality cardiovascular imaging: A state-of-the-art review.

In Progress in cardiovascular diseases

There has been a tidal wave of recent interest in artificial intelligence (AI), machine learning and deep learning approaches in cardiovascular (CV) medicine. In the era of modern medicine, AI and electronic health records hold the promise to improve the understanding of disease conditions and bring a personalized approach to CV care. The field of CV imaging (CVI), incorporating echocardiography, cardiac computed tomography, cardiac magnetic resonance imaging and nuclear imaging, with sophisticated imaging techniques and high volumes of imaging data, is primed to be at the forefront of the revolution in precision cardiology. This review provides a contemporary overview of the CVI imaging applications of AI, including a critique of the strengths and potential limitations of deep learning approaches.

Xu Bo, Kocyigit Duygu, Griffin Brian P, Cheng Feixiong

2020-Mar-19

Artificial intelligence, Cardiac computed tomography, Cardiac magnetic resonance, Deep learning, Echocardiography, Machine learning, Nuclear cardiac imaging

Public Health Public Health

Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury.

In Journal of clinical epidemiology ; h5-index 60.0

OBJECTIVE : We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury.

STUDY DESIGN AND SETTING : We performed logistic (LR), lasso, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n=11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks, and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with GCS<13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (AUC) was quantified.

RESULTS : In the IMPACT-II database, 3,332/11,022(30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale below 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies, and less so between the studied algorithms. The mean AUC was 0.82 for mortality and 0.77 for unfavorable outcome in CENTER-TBI.

CONCLUSION : ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe TBI. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations.

Gravesteijn Benjamin Y, Nieboer Daan, Ercole Ari, Lingsma Hester F, Nelson David, van Calster Ben, Steyerberg Ewout W

2020-Mar-19

Cohort study, Data science, Machine learning, Prediction, Prognosis, Traumatic brain injury

Radiology Radiology

Disentangling Heterogeneity in Alzheimer's Disease and Related Dementias Using Data-Driven Methods.

In Biological psychiatry ; h5-index 105.0

Brain aging is a complex process that includes atrophy, vascular injury, and a variety of age-associated neurodegenerative pathologies, together determining an individual's course of cognitive decline. While Alzheimer's disease and related dementias contribute to the heterogeneity of brain aging, these conditions themselves are also heterogeneous in their clinical presentation, progression, and pattern of neural injury. We reviewed studies that leveraged data-driven approaches to examining heterogeneity in Alzheimer's disease and related dementias, with a principal focus on neuroimaging studies exploring subtypes of regional neurodegeneration patterns. Over the past decade, the steadily increasing wealth of clinical, neuroimaging, and molecular biomarker information collected within large-scale observational cohort studies has allowed for a richer understanding of the variability of disease expression within the aging and Alzheimer's disease and related dementias continuum. Moreover, the availability of these large-scale datasets has supported the development and increasing application of clustering techniques for studying disease heterogeneity in a data-driven manner. In particular, data-driven studies have led to new discoveries of previously unappreciated disease subtypes characterized by distinct neuroimaging patterns of regional neurodegeneration, which are paralleled by heterogeneous profiles of pathological, clinical, and molecular biomarker characteristics. Incorporating these findings into novel frameworks for more differentiated disease stratification holds great promise for improving individualized diagnosis and prognosis of expected clinical progression, and provides opportunities for development of precision medicine approaches for therapeutic intervention. We conclude with an account of the principal challenges associated with data-driven heterogeneity analyses and outline avenues for future developments in the field.

Habes Mohamad, Grothe Michel J, Tunc Birkan, McMillan Corey, Wolk David A, Davatzikos Christos

2020-Jan-31

Alzheimer’s disease, Brain aging, Clustering, Frontotemporal dementia, Heterogeneity, Lewy body dementias, MRI, Machine learning, Neuroimaging, PET

General General

Artificial intelligence in medicine.

In Early human development

Artificial Intelligence (AI) is based on accurate decision-making processes which can be carried out independently by a machine. AI may be subdivided into strong AI (with consciousness and intentionality) and weak AI (lacking both and programmed to perform specific tasks only). With AI currently making rapid progress in all domains, including those of healthcare, physicians face possible competitors. Various types of AI programs are already available as consultants to the physician, and these help in medical diagnostics and treatment. At the time of writing, extant programs constitute weak AI. This paper will explore the development of AI and robotics in medicine, and will refer to Star Trek's "Emergency Medical Hologram", who is portrayed as a strong AI program. This paper will also briefly explore the issues pertaining to AI in the medical field and will show that weak AI should not only suffice in the demesne of healthcare, but may actually be more desirable than strong AI.

Scerri Mariella, Grech Victor

2020-Mar-20

Pathology Pathology

Bulbar ALS Detection Based on Analysis of Voice Perturbation and Vibrato

ArXiv Preprint

On average the lack of biological markers causes a one year diagnostic delay to detect amyotrophic lateral sclerosis (ALS). To improve the diagnostic process an automatic voice assessment based on acoustic analysis can be used. The purpose of this work was to verify the sutability of the sustain vowel phonation test for automatic detection of patients with ALS. We proposed enhanced procedure for separation of voice signal into fundamental periods that requires for calculation of perturbation measurements (such as jitter and shimmer). Also we proposed method for quantitative assessment of pathological vibrato manifestations in sustain vowel phonation. The study's experiments show that using the proposed acoustic analysis methods, the classifier based on linear discriminant analysis attains 90.7\% accuracy with 86.7\% sensitivity and 92.2\% specificity.

Maxim Vashkevich, Alexander Petrovsky, Yuliya Rushkevich

2020-03-24

General General

Machine Learning Analysis of Digital Clock Drawing Test Performance for Differential Classification of Mild Cognitive Impairment Subtypes Versus Alzheimer's Disease.

In Journal of the International Neuropsychological Society : JINS

OBJECTIVE : To determine how well machine learning algorithms can classify mild cognitive impairment (MCI) subtypes and Alzheimer's disease (AD) using features obtained from the digital Clock Drawing Test (dCDT).

METHODS : dCDT protocols were administered to 163 patients diagnosed with AD(n = 59), amnestic MCI (aMCI; n = 26), combined mixed/dysexecutive MCI (mixed/dys MCI; n = 43), and patients without MCI (non-MCI; n = 35) using standard clock drawing command and copy procedures, that is, draw the face of the clock, put in all of the numbers, and set the hands for "10 after 11." A digital pen and custom software recorded patient's drawings. Three hundred and fifty features were evaluated for maximum information/minimum redundancy. The best subset of features was used to train classification models to determine diagnostic accuracy.

RESULTS : Neural network employing information theoretic feature selection approaches achieved the best 2-group classification results with 10-fold cross validation accuracies at or above 83%, that is, AD versus non-MCI = 91.42%; AD versus aMCI = 91.49%; AD versus mixed/dys MCI = 84.05%; aMCI versus mixed/dys MCI = 84.11%; aMCI versus non-MCI = 83.44%; and mixed/dys MCI versus non-MCI = 85.42%. A follow-up two-group non-MCI versus all MCI patients analysis yielded comparable results (83.69%). Two-group classification analyses were achieved with 25-125 dCDT features depending on group classification. Three- and four-group analyses yielded lower but still promising levels of classification accuracy.

CONCLUSION : Early identification of emergent neurodegenerative illness is criterial for better disease management. Applying machine learning to standard neuropsychological tests promises to be an effective first line screening method for classification of non-MCI and MCI subtypes.

Binaco Russell, Calzaretto Nicholas, Epifano Jacob, McGuire Sean, Umer Muhammad, Emrani Sheina, Wasserman Victor, Libon David J, Polikar Robi

2020-Mar-23

Clock drawing, Cognitive assessment, Machine learning, Mild cognitive impairment, The Digital Clock Drawing Test

Pathology Pathology

PanNuke Dataset Extension, Insights and Baselines

ArXiv Preprint

The emerging area of computational pathology (CPath) is ripe ground for the application of deep learning (DL) methods to healthcare due to the sheer volume of raw pixel data in whole-slide images (WSIs) of cancerous tissue slides, generally of the order of $100K{\times}80K$ pixels. However, it is imperative for the DL algorithms relying on nuclei-level details to be able to cope with data from `the clinical wild', which tends to be quite challenging. We study, and extend recently released PanNuke dataset consisting of more than 200,000 nuclei categorized into 5 clinically important classes for the challenging tasks of detecting, segmenting and classifying nuclei in WSIs \footnote{Download dataset here \href{https://jgamper.github.io/PanNukeDataset}{https://jgamper.github.io/PanNukeDataset}} \cite{gamper_pannuke:_2019}. Previous pan-cancer datasets consisted of only up to 9 different tissues and up to 21,000 unlabeled nuclei \cite{kumar2019multi} and just over 24,000 labeled nuclei with segmentation masks \cite{graham_hover-net:_2019}. PanNuke consists of 19 different tissue types from over 20,000 WSIs that have been semi-automatically annotated and quality controlled by clinical pathologists, leading to a dataset with statistics similar to `the clinical wild' and with minimal selection bias. We study the performance of segmentation and classification models when applied to the proposed dataset and demonstrate the application of models trained on PanNuke to whole-slide images. We provide comprehensive statistics about the dataset and outline recommendations and research directions to address the limitations of existing DL tools when applied to real-world CPath applications.

Jevgenij Gamper, Navid Alemi Koohbanani, Simon Graham, Mostafa Jahanifar, Syed Ali Khurram, Ayesha Azam, Katherine Hewitt, Nasir Rajpoot

2020-03-24

General General

Dynamic resource allocation during reinforcement learning accounts for ramping and phasic dopamine activity.

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

For an animal to learn about its environment with limited motor and cognitive resources, it should focus its resources on potentially important stimuli. However, too narrow focus is disadvantageous for adaptation to environmental changes. Midbrain dopamine neurons are excited by potentially important stimuli, such as reward-predicting or novel stimuli, and allocate resources to these stimuli by modulating how an animal approaches, exploits, explores, and attends. The current study examined the theoretical possibility that dopamine activity reflects the dynamic allocation of resources for learning. Dopamine activity may transition between two patterns: (1) phasic responses to cues and rewards, and (2) ramping activity arising as the agent approaches the reward. Phasic excitation has been explained by prediction errors generated by experimentally inserted cues. However, when and why dopamine activity transitions between the two patterns remain unknown. By parsimoniously modifying a standard temporal difference (TD) learning model to accommodate a mixed presentation of both experimental and environmental stimuli, we simulated dopamine transitions and compared them with experimental data from four different studies. The results suggested that dopamine transitions from ramping to phasic patterns as the agent focuses its resources on a small number of reward-predicting stimuli, thus leading to task dimensionality reduction. The opposite occurs when the agent re-distributes its resources to adapt to environmental changes, resulting in task dimensionality expansion. This research elucidates the role of dopamine in a broader context, providing a potential explanation for the diverse repertoire of dopamine activity that cannot be explained solely by prediction error.

Song Minryung R, Lee Sang Wan

2020-Mar-10

Habit, Pearce-Hall model, Prediction error, Salience, Striatum, Temporal-difference learning model

General General

AdaEn-Net: An ensemble of adaptive 2D-3D Fully Convolutional Networks for medical image segmentation.

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

Fully Convolutional Networks (FCNs) have emerged as powerful segmentation models but are usually designed manually, which requires extensive time and can result in large and complex architectures. There is a growing interest to automatically design efficient architectures that can accurately segment 3D medical images. However, most approaches either do not fully exploit volumetric information or do not optimize the model's size. To address these problems, we propose a self-adaptive 2D-3D ensemble of FCNs called AdaEn-Net for 3D medical image segmentation that incorporates volumetric data and adapts to a particular dataset by optimizing both the model's performance and size. The AdaEn-Net consists of a 2D FCN that extracts intra-slice information and a 3D FCN that exploits inter-slice information. The architecture and hyperparameters of the 2D and 3D architectures are found through a multiobjective evolutionary based algorithm that maximizes the expected segmentation accuracy and minimizes the number of parameters in the network. The main contribution of this work is a model that fully exploits volumetric information and automatically searches for a high-performing and efficient architecture. The AdaEn-Net was evaluated for prostate segmentation on the PROMISE12 Grand Challenge and for cardiac segmentation on the MICCAI ACDC challenge. In the first challenge, the AdaEn-Net ranks 9 out of 297 submissions and surpasses the performance of an automatically-generated segmentation network while producing an architecture with 13× fewer parameters. In the second challenge, the proposed model is ranked within the top 8 submissions and outperforms an architecture designed with reinforcement learning while having 1.25× fewer parameters.

Baldeon Calisto Maria, Lai-Yuen Susana K

2020-Mar-10

Deep learning, Hyperparameter optimization, Medical image segmentation, Multiobjective optimization, Neural architecture search

General General

SWIFT-Active Screener: Accelerated document screening through active learning and integrated recall estimation.

In Environment international

BACKGROUND : In the screening phase of systematic review, researchers use detailed inclusion/exclusion criteria to decide whether each article in a set of candidate articles is relevant to the research question under consideration. A typical review may require screening thousands or tens of thousands of articles in and can utilize hundreds of person-hours of labor.

METHODS : Here we introduce SWIFT-Active Screener, a web-based, collaborative systematic review software application, designed to reduce the overall screening burden required during this resource-intensive phase of the review process. To prioritize articles for review, SWIFT-Active Screener uses active learning, a type of machine learning that incorporates user feedback during screening. Meanwhile, a negative binomial model is employed to estimate the number of relevant articles remaining in the unscreened document list. Using a simulation involving 26 diverse systematic review datasets that were previously screened by reviewers, we evaluated both the document prioritization and recall estimation methods.

RESULTS : On average, 95% of the relevant articles were identified after screening only 40% of the total reference list. In the 5 document sets with 5,000 or more references, 95% recall was achieved after screening only 34% of the available references, on average. Furthermore, the recall estimator we have proposed provides a useful, conservative estimate of the percentage of relevant documents identified during the screening process.

CONCLUSION : SWIFT-Active Screener can result in significant time savings compared to traditional screening and the savings are increased for larger project sizes. Moreover, the integration of explicit recall estimation during screening solves an important challenge faced by all machine learning systems for document screening: when to stop screening a prioritized reference list. The software is currently available in the form of a multi-user, collaborative, online web application.

Howard Brian E, Phillips Jason, Tandon Arpit, Maharana Adyasha, Elmore Rebecca, Mav Deepak, Sedykh Alex, Thayer Kristina, Merrick B Alex, Walker Vickie, Rooney Andrew, Shah Ruchir R

2020-Mar-20

Active learning, Document screening, Evidence mapping, Machine learning, Recall estimation, Systematic review

General General

Deep learning based searching approach for RDF graphs.

In PloS one ; h5-index 176.0

The Internet is a remarkably complex technical system. Its rapid growth has also brought technical issues such as problems to information retrieval. Search engines retrieve requested information based on the provided keywords. Consequently, it is difficult to accurately find the required information without understanding the syntax and semantics of the content. Multiple approaches are proposed to resolve this problem by employing the semantic web and linked data techniques. Such approaches serialize the content using the Resource Description Framework (RDF) and execute the queries using SPARQL to resolve the problem. However, an exact match between RDF content and query structure is required. Although, it improves the keyword-based search; however, it does not provide probabilistic reasoning to find the semantic relationship between the queries and their results. From this perspective, in this paper, we propose a deep learning-based approach for searching RDF graphs. The proposed approach treats document requests as a classification problem. First, we preprocess the RDF graphs to convert them into N-Triples format. Second, bag-of-words (BOW) and word2vec feature modeling techniques are combined for a novel deep representation of RDF graphs. The attention mechanism enables the proposed approach to understand the semantic between RDF graphs. Third, we train a convolutional neural network for the accurate retrieval of RDF graphs using the deep representation. We employ 10-fold cross-validation to evaluate the proposed approach. The results show that the proposed approach is accurate and surpasses the state-of-the-art. The average accuracy, precision, recall, and f-measure are up to 97.12%, 98.17%, 95.56%, and 96.85%, respectively.

Soliman Hatem

2020

Internal Medicine Internal Medicine

Tumor Necrosis Factor (TNF) blocking agents are associated with lower risk for Alzheimer's disease in patients with rheumatoid arthritis and psoriasis.

In PloS one ; h5-index 176.0

This large, retrospective case-control study of electronic health records from 56 million unique adult patients examined whether or not treatment with a Tumor Necrosis Factor (TNF) blocking agent is associated with lower risk for Alzheimer's disease (AD) in patients with rheumatoid arthritis (RA), psoriasis, and other inflammatory diseases which are mediated in part by TNF and for which a TNF blocker is an approved treatment. The analysis compared the diagnosis of AD as an outcome measure in patients receiving at least one prescription for a TNF blocking agent (etanercept, adalimumab, and infliximab) or for methotrexate. Adjusted odds ratios (AORs) were estimated using the Cochran-Mantel-Haenszel (CMH) method and presented with 95% confidence intervals (CIs) and p-values. RA was associated with a higher risk for AD (Adjusted Odds Ratio (AOR) = 2.06, 95% Confidence Interval: (2.02-2.10), P-value <0.0001) as did psoriasis (AOR = 1.37 (1.31-1.42), P <0.0001), ankylosing spondylitis (AOR = 1.57 (1.39-1.77), P <0.0001), inflammatory bowel disease (AOR = 2.46 (2.33-2.59), P < 0.0001), ulcerative colitis (AOR = 1.82 (1.74-1.91), P <0.0001), and Crohn's disease (AOR = 2.33 (2.22-2.43), P <0.0001). The risk for AD in patients with RA was lower among patients treated with etanercept (AOR = 0.34 (0.25-0.47), P <0.0001), adalimumab (AOR = 0.28 (0.19-0.39), P < 0.0001), or infliximab (AOR = 0.52 (0.39-0.69), P <0.0001). Methotrexate was also associated with a lower risk for AD (AOR = 0.64 (0.61-0.68), P <0.0001), while lower risk was found in patients with a prescription history for both a TNF blocker and methotrexate. Etanercept and adalimumab also were associated with lower risk for AD in patients with psoriasis: AOR = 0.47 (0.30-0.73 and 0.41 (0.20-0.76), respectively. There was no effect of gender or race, while younger patients showed greater benefit from a TNF blocker than did older patients. This study identifies a subset of patients in whom systemic inflammation contributes to risk for AD through a pathological mechanism involving TNF and who therefore may benefit from treatment with a TNF blocking agent.

Zhou Mengshi, Xu Rong, Kaelber David C, Gurney Mark E

2020

Cardiology Cardiology

Deep Learning-Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography.

In Journal of the American Heart Association ; h5-index 70.0

Background Severe, symptomatic aortic stenosis (AS) is associated with poor prognoses. However, early detection of AS is difficult because of the long asymptomatic period experienced by many patients, during which screening tools are ineffective. The aim of this study was to develop and validate a deep learning-based algorithm, combining a multilayer perceptron and convolutional neural network, for detecting significant AS using ECGs. Methods and Results This retrospective cohort study included adult patients who had undergone both ECG and echocardiography. A deep learning-based algorithm was developed using 39 371 ECGs. Internal validation of the algorithm was performed with 6453 ECGs from one hospital, and external validation was performed with 10 865 ECGs from another hospital. The end point was significant AS (beyond moderate). We used demographic information, features, and 500-Hz, 12-lead ECG raw data as predictive variables. In addition, we identified which region had the most significant effect on the decision-making of the algorithm using a sensitivity map. During internal and external validation, the areas under the receiver operating characteristic curve of the deep learning-based algorithm using 12-lead ECG for detecting significant AS were 0.884 (95% CI, 0.880-0.887) and 0.861 (95% CI, 0.858-0.863), respectively; those using a single-lead ECG signal were 0.845 (95% CI, 0.841-0.848) and 0.821 (95% CI, 0.816-0.825), respectively. The sensitivity map showed the algorithm focused on the T wave of the precordial lead to determine the presence of significant AS. Conclusions The deep learning-based algorithm demonstrated high accuracy for significant AS detection using both 12-lead and single-lead ECGs.

Kwon Joon-Myoung, Lee Soo Youn, Jeon Ki-Hyun, Lee Yeha, Kim Kyung-Hee, Park Jinsik, Oh Byung-Hee, Lee Myong-Mook

2020-Apr-07

aortic valve stenosis, deep learning, electrocardiography

General General

Dividing Deep Learning Model for Continuous Anomaly Detection of Inconsistent ICT Systems

ArXiv Preprint

Health monitoring is important for maintaining reliable information and communications technology (ICT) systems. Anomaly detection methods based on machine learning, which train a model for describing "normality" are promising for monitoring the state of ICT systems. However, these methods cannot be used when the type of monitored log data changes from that of training data due to the replacement of certain equipment. Therefore, such methods may dismiss an anomaly that appears when log data changes. To solve this problem, we propose an ICT-systems-monitoring method with deep learning models divided based on the correlation of log data. We also propose an algorithm for extracting the correlations of log data from a deep learning model and separating log data based on the correlation. When some of the log data changes, our method can continue health monitoring with the divided models which are not affected by changes in the log data. We present the results from experiments involving benchmark data and real log data, which indicate that our method using divided models does not decrease anomaly detection accuracy and a model for anomaly detection can be divided to continue monitoring a network state even if some the log data change.

Kengo Tajiri, Yasuhiro Ikeda, Yuusuke Nakano, Keishiro Watanabe

2020-03-24

General General

Toward a unified framework for interpreting machine-learning models in neuroimaging.

In Nature protocols

Machine learning is a powerful tool for creating computational models relating brain function to behavior, and its use is becoming widespread in neuroscience. However, these models are complex and often hard to interpret, making it difficult to evaluate their neuroscientific validity and contribution to understanding the brain. For neuroimaging-based machine-learning models to be interpretable, they should (i) be comprehensible to humans, (ii) provide useful information about what mental or behavioral constructs are represented in particular brain pathways or regions, and (iii) demonstrate that they are based on relevant neurobiological signal, not artifacts or confounds. In this protocol, we introduce a unified framework that consists of model-, feature- and biology-level assessments to provide complementary results that support the understanding of how and why a model works. Although the framework can be applied to different types of models and data, this protocol provides practical tools and examples of selected analysis methods for a functional MRI dataset and multivariate pattern-based predictive models. A user of the protocol should be familiar with basic programming in MATLAB or Python. This protocol will help build more interpretable neuroimaging-based machine-learning models, contributing to the cumulative understanding of brain mechanisms and brain health. Although the analyses provided here constitute a limited set of tests and take a few hours to days to complete, depending on the size of data and available computational resources, we envision the process of annotating and interpreting models as an open-ended process, involving collaborative efforts across multiple studies and laboratories.

Kohoutová Lada, Heo Juyeon, Cha Sungmin, Lee Sungwoo, Moon Taesup, Wager Tor D, Woo Choong-Wan

2020-Mar-18

General General

Novel body fat estimation using machine learning and 3-dimensional optical imaging.

In European journal of clinical nutrition ; h5-index 46.0

Estimates of body composition have been derived using 3-dimensional optical imaging (3DO), but no equations to date have been calibrated using a 4-component (4C) model criterion. This investigation reports the development of a novel body fat prediction formula using anthropometric data from 3DO imaging and a 4C model. Anthropometric characteristics and body composition of 179 participants were measured via 3DO (Size Stream® SS20) and a 4C model. Machine learning was used to identify significant anthropometric predictors of body fat (BF%), and stepwise/lasso regression analyses were employed to develop new 3DO-derived BF% prediction equations. The combined equation was externally cross-validated using paired 3DO and DXA assessments (n = 158), producing a R2 value of 0.78 and a constant error of (X ± SD) 0.8 ± 4.5%. 3DO BF% estimates demonstrated equivalence with DXA based on equivalence testing with no proportional bias in the Bland-Altman analysis. Machine learning methods may hold potential for enhancing 3DO-derived BF% estimates.

Harty Patrick S, Sieglinger Breck, Heymsfield Steven B, Shepherd John A, Bruner David, Stratton Matthew T, Tinsley Grant M

2020-Mar-16

General General

A Subvision System for Enhancing the Environmental Adaptability of the Powered Transfemoral Prosthesis.

In IEEE transactions on cybernetics

Visual information is indispensable to human locomotion in complex environments. Although amputees can perceive the environmental information by eyes, they cannot transmit the neural signals to prostheses directly. To augment human-prosthesis interaction, this article introduces a subvision system that can perceive environments actively, assist to control the powered prosthesis predictively, and accordingly reconstruct a complete vision-locomotion loop for transfemoral amputees. By using deep learning, the subvision system can classify common static terrains (e.g., level ground, stairs, and ramps) and estimate corresponding motion intents of amputees with high accuracy (98%). After applying the subvision system to the locomotion control system, the powered prosthesis can help amputees to achieve nonrhythmic locomotion naturally, including switching between different locomotion modes and crossing the obstacle. The subvision system can also recognize dynamic objects, such as an unexpected obstacle approaching the amputee, and assist in generating an agile obstacle-avoidance reflex movement. The experimental results demonstrate that the subvision system can cooperate with the powered prosthesis to reconstruct a complete vision-locomotion loop, which enhances the environmental adaptability of the amputees.

Zhang Kuangen, Fu Chenglong, Luo Jianwen, Xiao Wentao, Zhang Wen, Liu Haiyuan, Zhu Jiale, Lu Zeyu, Rong Yiming, de Silva Clarence W

2020-Mar-19

General General

Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications.

In IEEE transactions on cybernetics

Reinforcement learning (RL) algorithms have been around for decades and employed to solve various sequential decision-making problems. These algorithms, however, have faced great challenges when dealing with high-dimensional environments. The recent development of deep learning has enabled RL methods to drive optimal policies for sophisticated and capable agents, which can perform efficiently in these challenging environments. This article addresses an important aspect of deep RL related to situations that require multiple agents to communicate and cooperate to solve complex tasks. A survey of different approaches to problems related to multiagent deep RL (MADRL) is presented, including nonstationarity, partial observability, continuous state and action spaces, multiagent training schemes, and multiagent transfer learning. The merits and demerits of the reviewed methods will be analyzed and discussed with their corresponding applications explored. It is envisaged that this review provides insights about various MADRL methods and can lead to the future development of more robust and highly useful multiagent learning methods for solving real-world problems.

Nguyen Thanh Thi, Nguyen Ngoc Duy, Nahavandi Saeid

2020-Mar-20

General General

Kernelized Sparse Bayesian Matrix Factorization.

In IEEE transactions on neural networks and learning systems

Extracting low-rank and/or sparse structures using matrix factorization techniques has been extensively studied in the machine learning community. Kernelized matrix factorization (KMF) is a powerful tool to incorporate side information into the low-rank approximation model, which has been applied to solve the problems of data mining, recommender systems, image restoration, and machine vision. However, most existing KMF models rely on specifying the rows and columns of the data matrix through a Gaussian process prior and have to tune manually the rank. There are also computational issues of existing models based on regularization or the Markov chain Monte Carlo. In this article, we develop a hierarchical kernelized sparse Bayesian matrix factorization (KSBMF) model to integrate side information. The KSBMF automatically infers the parameters and latent variables including the reduced rank using the variational Bayesian inference. In addition, the model simultaneously achieves low-rankness through sparse Bayesian learning and columnwise sparsity through an enforced constraint on latent factor matrices. We further connect the KSBMF with the nonlocal image processing framework to develop two algorithms for image denoising and inpainting. Experimental results demonstrate that KSBMF outperforms the state-of-the-art approaches for these image-restoration tasks under various levels of corruption.

Li Caoyuan, Xie Hong-Bo, Fan Xuhui, Xu Richard Yi Da, Van Huffel Sabine, Mengersen Kerrie

2020-Mar-23

General General

Statistical Loss and Analysis for Deep Learning in Hyperspectral Image Classification.

In IEEE transactions on neural networks and learning systems

Nowadays, deep learning methods, especially the convolutional neural networks (CNNs), have shown impressive performance on extracting abstract and high-level features from the hyperspectral image. However, the general training process of CNNs mainly considers the pixelwise information or the samples' correlation to formulate the penalization while ignores the statistical properties especially the spectral variability of each class in the hyperspectral image. These sample-based penalizations would lead to the uncertainty of the training process due to the imbalanced and limited number of training samples. To overcome this problem, this article characterizes each class from the hyperspectral image as a statistical distribution and further develops a novel statistical loss with the distributions, not directly with samples for deep learning. Based on the Fisher discrimination criterion, the loss penalizes the sample variance of each class distribution to decrease the intraclass variance of the training samples. Moreover, an additional diversity-promoting condition is added to enlarge the interclass variance between different class distributions, and this could better discriminate samples from different classes in the hyperspectral image. Finally, the statistical estimation form of the statistical loss is developed with the training samples through multivariant statistical analysis. Experiments over the real-world hyperspectral images show the effectiveness of the developed statistical loss for deep learning.

Gong Zhiqiang, Zhong Ping, Hu Weidong

2020-Mar-19

General General

Categorical Matrix Completion with Active Learning for High-throughput Screening.

In IEEE/ACM transactions on computational biology and bioinformatics

The recent advances in wet-lab automation enable high-throughput experiments to be conducted seamlessly. In particular, the exhaustive enumeration of all possible conditions is always involved in high-throughput screening. Nonetheless, such a screening strategy is hardly believed to be optimal and cost-effective. By incorporating artificial intelligence, we design an open-source model based on categorical matrix completion and active machine learning to guide high throughput screening experiments. Specifically, we narrow our scope to the high-throughput screening for chemical compound effects on diverse protein sub-cellular locations. In the proposed model, we believe that exploration is more important than the exploitation in the long-run of high-throughput screening experiment, Therefore, we design several innovations to circumvent the existing limitations. In particular, categorical matrix completion is designed to accurately impute the missing experiments while margin sampling is also implemented for uncertainty estimation. The model is systematically tested on both simulated and real data. The simulation results reflect that our model can be robust to diverse scenarios, while the real data results demonstrate the wet-lab applicability of our model for high-throughput screening experiments. Lastly, we attribute the model success to its exploration ability by revealing the related matrix ranks and distinct experiment coverage comparisons.

Chen Junyi, Hou Junhui, Wong Ka-Chun

2020-Mar-20

General General

CohereNet: A Deep Learning Architecture for Ultrasound Spatial Correlation Estimation and Coherence-Based Beamforming.

In IEEE transactions on ultrasonics, ferroelectrics, and frequency control

Deep fully connected networks are often considered "universal approximators" that are capable of learning any function. In this paper, we utilize this particular property of deep neural networks (DNNs) to estimate normalized cross correlation as a function of spatial lag (i.e., spatial coherence functions) for applications in coherence-based beamforming, specifically short-lag spatial coherence (SLSC) beamforming. We detail the composition, assess the performance, and evaluate the computational efficiency of CohereNet, our custom fully connected DNN, which was trained to estimate the spatial coherence functions of in vivo breast data from 18 unique patients. CohereNet performance was evaluated on in vivo breast data from three additional patients who were not included during training, as well as data from in vivo liver and tissue mimicking phantoms scanned with a variety of ultrasound transducer array geometries and two different ultrasound systems. The mean correlation between the SLSC images computed on a central processing unit (CPU) and the corresponding DNN SLSC images created with CohereNet was 0.93 across the entire test set. The DNN SLSC approach was up to 3.4 times faster than the CPU SLSC approach, with similar computational speed, less variability in computational times, and improved image quality compared to a graphical processing unit (GPU)-based SLSC approach. These results are promising for the application of deep learning to estimate correlation functions derived from ultrasound data in multiple areas of ultrasound imaging and beamforming (e.g., speckle tracking, elastography, blood flow estimation), possibly replacing GPU-based approaches in low-power, remote, and synchronization-dependent applications.

Wiacek Alycen, Gonzalez Eduardo, Bell Muyinatu A Lediju

2020-Mar-23

General General

Structural Analysis and Identification of False Positive Hits in Luciferase-based Assays.

In Journal of chemical information and modeling

Luciferase-based bioluminescence detection techniques are highly favored in high-throughput screening (HTS), in which the firefly luciferase (FLuc) is the most commonly used variant. However, FLuc inhibitors can interfere with the activity of luciferase, which may result in false positive signals in HTS assays. In order to reduce the unnecessary cost of time and money, in silico prediction model for FLuc inhibitors is highly desirable. In this study, we built an extensive dataset consisting of 20,888 FLuc inhibitors and 198,608 noninhibitors, and then developed a group of classification models based on the combination of three machine learning (ML) algorithms and four types of molecular representations. The best prediction model based on XGBoost and ECFP4 and MOE2d descriptors yielded a balanced accuracy (BA) of 0.878 and an AUC of 0.958 for the validation set, and a BA of 0.886 and an AUC of 0.947 for the test set. Three external validation sets, including Set 1 (3,231 FLuc inhibitors and 69,783 noninhibitors), Set 2 (695 FLuc inhibitors and 75,913 noninhibitors) and Set 3 (1,138 FLuc inhibitors and 8,155 noninhibitors), were used to verify the predictive ability of our models. The BA values for the three external validation sets given by the best model are 0.864, 0.845 and 0.791, respectively. In addition, the important features or structural fragments related to FLuc inhibitors were recognized by the Shapley additive explanations (SHAP) method along with their influences on predictions, which may provide valuable clues to detect undesirable luciferase inhibitors. Based on the important and explanatory features, 16 rules were proposed for detecting FLuc inhibitors, which can achieve the correction rate of 70% for FLuc inhibitors. Furthermore, a comparison with existing prediction rules and models for FLuc inhibitors used in virtual screening verified the high reliability of the models and rules proposed in this study. We also used the model to screen three curated chemical databases, and almost 10% of the molecules in the evaluated databases were predicted as inhibitors, highlighting the potential risk of false positives in luciferase-based assays. Finally, a public webserver called ChemFLuc was developed (http://admet.scbdd.com/chemfluc/index) and it offers a free available service to predict potential FLuc inhibitors.

Yang Ziyi, Dong Jie, Yang Zhijiang, Lu Aiping, Hou Tingjun, Cao Dongsheng

2020-Mar-23

General General

Spatially and Temporally Explicit Life Cycle Environmental Impacts of Soybean Production in the U.S. Midwest.

In Environmental science & technology ; h5-index 132.0

Understanding spatially and temporally explicit life cycle environmental impacts is critical for designing sustainable supply chains for biofuel and animal sectors. However, annual life cycle environmental impacts of crop production at county scale are lacking. To address this knowledge gap, this study used a combination of Environmental Policy Integrated Climate and process-based life cycle assessment models to quantify life cycle global warming (GWP), eutrophication (EU) and acidification (AD) impacts of soybean production in nearly 1,000 Midwest counties yr-1 over 9 years. Sequentially, a machine learning approach was applied to identify the top influential factors among soil, climate and farming practices, which drive the spatial and temporal heterogeneity of life cycle environmental impacts. The results indicated that significant variations existed in life cycle GWP, EU and AD among counties and across years. Life cycle GWP impacts ranged from -11.4 to 22.0 kg CO2-eq. kg soybean-1, whereas life cycle EU and AD impacts varied by factors of 302 and 44, respectively. Nitrogen application rates, temperature in March and soil texture were the top influencing factors for life cycle GWP impacts. In contrast, soil organic content and nitrogen application rate were the top influencing factors for life cycle EU and AD impacts.

Romeiko Xiaobo Xue, Lee Eun Kyung, Sorunmu Yetunde, Zhang Xuesong

2020-Mar-23

General General

Speech exemplar and evaluation database (SEED) for clinical training in articulatory phonetics and speech science.

In Clinical linguistics & phonetics ; h5-index 19.0

One challenge faced by teachers of phonetics, speech science, and clinical speech disorders courses is providing meaningful instruction that closes the theory to practice gap. One barrier to providing this type of deep learning experience is the lack of publicly available examples of speech recordings that illustrate comparisons between typical and disordered speech production across a broad range of disorder populations. Data of this type exist, but are typically collected for specific research projects under narrowly written IRB protocols that do not allow for release of even de-identified speech recordings to other investigators or teachers. As a partial corrective to this problem, we have developed an approved publicly available database of speech recordings that provides illustrative examples of adult and child speech production from individuals with and without speech disorders. The recorded speech materials were designed to illustrate important clinical concepts, and the recordings were collected under controlled conditions using high-quality equipment. The ultimate goal of creating this corpus is to improve practitioners' and scientists' understanding of the scientific bases of knowledge in our profession and improve our ability to develop clinical scientists and young researchers in the field.

Speights Atkins Marisha, Bailey Dallin J, Boyce Suzanne E

2020-Mar-23

Database, adult speech, child speech, speech disorders, speech production

General General

Towards Neural Machine Translation for Edoid Languages

ArXiv Preprint

Many Nigerian languages have relinquished their previous prestige and purpose in modern society to English and Nigerian Pidgin. For the millions of L1 speakers of indigenous languages, there are inequalities that manifest themselves as unequal access to information, communications, health care, security as well as attenuated participation in political and civic life. To minimize exclusion and promote socio-linguistic and economic empowerment, this work explores the feasibility of Neural Machine Translation (NMT) for the Edoid language family of Southern Nigeria. Using the new JW300 public dataset, we trained and evaluated baseline translation models for four widely spoken languages in this group: \`Ed\'o, \'Es\'an, Urhobo and Isoko. Trained models, code and datasets have been open-sourced to advance future research efforts on Edoid language technology.

Iroro Orife

2020-03-24

General General

Predicting Metabolic Syndrome With Machine Learning Models Using a Decision Tree Algorithm: Retrospective Cohort Study.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : Metabolic syndrome is a cluster of disorders that significantly influence the development and deterioration of numerous diseases. FibroScan is an ultrasound device that was recently shown to predict metabolic syndrome with moderate accuracy. However, previous research regarding prediction of metabolic syndrome in subjects examined with FibroScan has been mainly based on conventional statistical models. Alternatively, machine learning, whereby a computer algorithm learns from prior experience, has better predictive performance over conventional statistical modeling.

OBJECTIVE : We aimed to evaluate the accuracy of different decision tree machine learning algorithms to predict the state of metabolic syndrome in self-paid health examination subjects who were examined with FibroScan.

METHODS : Multivariate logistic regression was conducted for every known risk factor of metabolic syndrome. Principal components analysis was used to visualize the distribution of metabolic syndrome patients. We further applied various statistical machine learning techniques to visualize and investigate the pattern and relationship between metabolic syndrome and several risk variables.

RESULTS : Obesity, serum glutamic-oxalocetic transaminase, serum glutamic pyruvic transaminase, controlled attenuation parameter score, and glycated hemoglobin emerged as significant risk factors in multivariate logistic regression. The area under the receiver operating characteristic curve values for classification and regression trees and for the random forest were 0.831 and 0.904, respectively.

CONCLUSIONS : Machine learning technology facilitates the identification of metabolic syndrome in self-paid health examination subjects with high accuracy.

Yu Cheng-Sheng, Lin Yu-Jiun, Lin Chang-Hsien, Wang Sen-Te, Lin Shiyng-Yu, Lin Sanders H, Wu Jenny L, Chang Shy-Shin

2020-Mar-23

controlled attenuation parameter technology, decision tree, machine learning, metabolic syndrome

Internal Medicine Internal Medicine

Learning Latent Space Representations to Predict Patient Outcomes: Model Development and Validation.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Scalable and accurate health outcome prediction using electronic health record (EHR) data has gained much attention in research recently. Previous machine learning models mostly ignore relations between different types of clinical data (ie, laboratory components, International Classification of Diseases codes, and medications).

OBJECTIVE : This study aimed to model such relations and build predictive models using the EHR data from intensive care units. We developed innovative neural network models and compared them with the widely used logistic regression model and other state-of-the-art neural network models to predict the patient's mortality using their longitudinal EHR data.

METHODS : We built a set of neural network models that we collectively called as long short-term memory (LSTM) outcome prediction using comprehensive feature relations or in short, CLOUT. Our CLOUT models use a correlational neural network model to identify a latent space representation between different types of discrete clinical features during a patient's encounter and integrate the latent representation into an LSTM-based predictive model framework. In addition, we designed an ablation experiment to identify risk factors from our CLOUT models. Using physicians' input as the gold standard, we compared the risk factors identified by both CLOUT and logistic regression models.

RESULTS : Experiments on the Medical Information Mart for Intensive Care-III dataset (selected patient population: 7537) show that CLOUT (area under the receiver operating characteristic curve=0.89) has surpassed logistic regression (0.82) and other baseline NN models (<0.86). In addition, physicians' agreement with the CLOUT-derived risk factor rankings was statistically significantly higher than the agreement with the logistic regression model.

CONCLUSIONS : Our results support the applicability of CLOUT for real-world clinical use in identifying patients at high risk of mortality.

Rongali Subendhu, Rose Adam J, McManus David D, Bajracharya Adarsha S, Kapoor Alok, Granillo Edgard, Yu Hong

2020-Mar-23

ablation, neural networks, patient mortality, predictive modeling

General General

Performance evaluation of pattern recognition networks using electromyography signal and time-domain features for the classification of hand gestures.

In Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine

The problem of classifying individual finger movements of one hand is focused in this article. The input electromyography signal is processed and eight time-domain features are extracted for classifying hand gestures. The classified finger movements are thumb, middle, index, little, ring, hand close, thumb index, thumb ring, thumb little and thumb middle and the hand grasps are palmar class, spherical class, hook class, cylindrical class, tip class and lateral class. Four state-of-the-art classifiers namely feed forward artificial neural network, cascaded feed forward artificial neural network, deep learning neural network and support vector machine are selected for this work to classify the finger movements and hand grasps using the extracted time-domain features. The experimental results show that the artificial neural network classifier is stabilized at 6 epochs for finger movement dataset and at 4 epochs for hand grasps dataset with low mean square error. However, the support vector machine classifier attains the maximum accuracy of 97.3077% for finger movement dataset and 98.875% for hand grasp dataset which is significantly greater than feed forward artificial neural network, cascaded feed forward artificial neural network and deep learning neural network classifiers.

Vasanthi S Mary, Jayasree T

2020-Mar-23

Finger movements, artificial neural network, discrete wavelet transform, pattern recognition, time-domain features

General General

A Computer Vision Approach for Classifying Isometric Grip Force Exertion Levels.

In Ergonomics

Exposure to high and/or repetitive force exertions can lead to musculoskeletal injuries. However, measuring worker force exertion levels is challenging, and existing techniques can be intrusive, interfere with human-machine interface, and/or limited by subjectivity. In this work, computer vision techniques are developed to detect the force exertions using facial videos and wearable photoplethysmogram. Eighteen participants (19-24 years) performed isometric grip exertions at varying levels of maximum voluntary contraction. Novel features that predict forces were identified and extracted from video and photoplethysmogram data. Two experiments with two (High/Low) and three (0%MVC/50%MVC/100%MVC) labels were performed to classify exertions. The Deep Neural Network classifier performed the best with 96% and 87% accuracy for two- and three-level classifications. This approach was robust to leave subjects out during cross-validation (86% accuracy when 3-subjects were left out) and robust to noise (i.e., 89% accuracy for classifying talking activities as low force exertions).Practitioner Summary: Forceful exertions are contributing factors to musculoskeletal injuries, yet it remains difficult to measure in work environments. This paper presents an approach to estimate force exertion levels, which is less distracting to workers, easier to implement by practitioners, and could potentially be used in wide variety of workplaces.

Asadi Hamed, Zhou Guoyang, Lee Jae Joong, Aggarwal Vaneet, Yu Denny

2020-Mar-22

Computer Vision, Facial Expressions, High Force Exertions, Machine Learning

Public Health Public Health

Super learner analysis of real-time electronically monitored adherence to antiretroviral therapy under constrained optimization and comparison to non-differentiated care approaches for persons living with HIV in rural Uganda.

In Journal of the International AIDS Society ; h5-index 45.0

INTRODUCTION : Real-time electronic adherence monitoring (EAM) systems could inform on-going risk assessment for HIV viraemia and be used to personalize viral load testing schedules. We evaluated the potential of real-time EAM (transferred via cellular signal) and standard EAM (downloaded via USB cable) in rural Uganda to inform individually differentiated viral load testing strategies by applying machine learning approaches.

METHODS : We evaluated an observational cohort of persons living with HIV and treated with antiretroviral therapy (ART) who were monitored longitudinally with standard EAM from 2005 to 2011 and real-time EAM from 2011 to 2015. Super learner, an ensemble machine learning method, was used to develop a tool for targeting viral load testing to detect viraemia (>1000 copies/ml) based on clinical (CD4 count, ART regimen), viral load and demographic data, together with EAM-based adherence. Using sample-splitting (cross-validation), we evaluated area under the receiver operating characteristic curve (cvAUC), potential for EAM data to selectively defer viral load tests while minimizing delays in viraemia detection, and performance compared to WHO-recommended testing schedules.

RESULTS : In total, 443 persons (1801 person-years) and 485 persons (930 person-years) contributed to standard and real-time EAM analyses respectively. In the 2011 to 2015 dataset, addition of real-time EAM (cvAUC: 0.88; 95% CI: 0.83, 0.93) significantly improved prediction compared to clinical/demographic data alone (cvAUC: 0.78; 95% CI: 0.72, 0.86; p = 0.03). In the 2005 to 2011 dataset, addition of standard EAM (cvAUC: 0.77; 95% CI: 0.72, 0.81) did not significantly improve prediction compared to clinical/demographic data alone (cvAUC: 0.70; 95% CI: 0.64, 0.76; p = 0.08). A hypothetical testing strategy using real-time EAM to guide deferral of viral load tests would have reduced the number of tests by 32% while detecting 87% of viraemia cases without delay. By comparison, the WHO-recommended testing schedule would have reduced the number of tests by 69%, but resulted in delayed detection of viraemia a mean of 74 days for 84% of individuals with viraemia. Similar rules derived from standard EAM also resulted in potential testing frequency reductions.

CONCLUSIONS : Our machine learning approach demonstrates potential for combining EAM data with other clinical measures to develop a selective testing rule that reduces number of viral load tests ordered, while still identifying those at highest risk for viraemia.

Benitez Alejandra E, Musinguzi Nicholas, Bangsberg David R, Bwana Mwebesa B, Muzoora Conrad, Hunt Peter W, Martin Jeffrey N, Haberer Jessica E, Petersen Maya L

2020-Mar

adherence, machine learning, real-time adherence monitoring, viraemia, viral load monitoring, virologic failure

oncology Oncology

Multi-atlas-based auto-segmentation for prostatic urethra using novel prediction of deformable image registration accuracy.

In Medical physics ; h5-index 59.0

PURPOSE : Accurate identification of the prostatic urethra and bladder can help determine dosing and evaluate urinary toxicity during intensity-modulated radiation therapy (IMRT) planning in patients with localized prostate cancer. However, it is challenging to locate the prostatic urethra in planning CT (pCT). In the present study, we developed a multi-atlas-based auto-segmentation method for prostatic urethra identification using deformable image registration accuracy prediction with machine learning (ML) and assessed its feasibility.

METHODS : We examined 120 patients with prostate cancer treated with IMRT. All patients underwent temporary urinary catheter placement for identification and contouring of the prostatic urethra in pCT images (ground truth). Our method comprises the following three steps: (1) select four atlas datasets from the atlas datasets using the deformable image registration (DIR) accuracy prediction model, (2) deform them by structure-based DIR, (3) and propagate urethra contour using displacement vector field calculated by the DIR. In (1), for identifying suitable datasets, we used the trained support vector machine regression (SVR) model and five feature descriptors (e.g., prostate volume) to increase DIR accuracy. This method was trained/validated using 100 patients and performance was evaluated within an independent test set of 20 patients. Five-fold cross-validation was used to optimize the hype parameters of the DIR accuracy prediction model. We assessed the accuracy of our method by comparing it with those of two others: Acosta's method-based patient selection (previous study method, by Acosta et al.), and the Waterman's method (defines the prostatic urethra based on the center of the prostate, by Waterman et al.). We used the centerlines distance (CLD) between the ground truth and the predicted prostatic urethra as the evaluation index.

RESULTS : The CLD in the entire prostatic urethra was 2.09 ± 0.89 mm (our proposed method), 2.77 ± 0.99 mm (Acosta et al., p = 0.022), and 3.47 ± 1.19 mm (Waterman et al., p< 0.001); our proposed method showed the highest accuracy. In segmented CLD, CLD in the top 1/3 segment was highly improved from that of Waterman et.al. and was slightly improved from that of Acosta et.al., with results of 2.49 ± 1.78 mm (our proposed method), 2.95 ± 1.75 mm (Acosta et al., p = 0.42), and 5.76 ± 3.09 mm (Waterman et al., p < 0.001).

CONCLUSIONS : We developed a DIR accuracy prediction model-based multi-atlas-based auto-segmentation method for prostatic urethra identification. Our method identified prostatic urethra with mean error of 2.09 mm, likely due to combined effects of SVR model employment in patient selection, modified atlas dataset characteristics and DIR algorithm. Our method has potential utility in prostate cancer IMRT and can replace use of temporary indwelling urinary catheters.

Takagi Hisamichi, Kadoya Noriyuki, Kajikawa Tomohiro, Tanaka Shohei, Takayama Yoshiki, Chiba Takahito, Ito Kengo, Dobashi Suguru, Takeda Ken, Jingu Keiichi

2020-Mar-22

auto-segmentation, deformable image registration, machine learning, prostate cancer, radiotherapy

General General

Forecasting beef production and quality using large scale integrated data from Brazil.

In Journal of animal science

With agriculture rapidly becoming a data driven field it is imperative to extract useful information from large data collections to optimize the production systems. We compared the efficacy of regression (linear regression or generalized linear regression for continuous or categorical outcomes, respectively), random forests (RF) and multilayer neural networks (NN) to predict beef carcass weight (CW), age when finished (AS), fat deposition (FD), and carcass quality (CQ). The data analyzed contained information on over 4 million beef cattle from 5,204 farms, corresponding to 4.3% of Brazil's national production between 2014-2016. Explanatory variables were integrated from different data sources and encompassed animal traits, participation in a technical advising program, nutritional products sold to farms, economic variables related to beef production, month when finished, soil fertility, and climate in the location in which animals were raised. The training set was composed of information collected in 2014 and 2015, while the testing set had information recorded in 2016. After parameter tuning for each algorithm, models were used to predict the testing set. The best model to predict CW and AS was RF (CW: predicted Root Mean Square Error (RMSEp) = 0.65, R2 = 0.61 and Mean Absolute Error (MAE) = 0.49; AS: Accuracy = 28.7%, Cohen's kappa coefficient (Kappa) = 0.08). While the best approach for FD and CQ was generalized linear regression (Accuracy = 45.7%, Kappa = 0.05, and Accuracy = 58.7%, Kappa = 0.09, respectively). Across all models there was a tendency for better performance with RF and regression and worse with NN. Animal category, nutritional plan, cattle sales price, participation in a technical advising program and climate and soil in which animals were raised were deemed important for prediction of meat production and quality with regression and RF. The development of strategies for prediction of livestock production using real-world large scale data will be core to projecting future trends and optimizing the allocation of resources at all levels of the production chain, rendering animal production more sustainable. Despite beef cattle production being a complex system, this analysis shows that by integrating different sources of data it is possible to forecast meat production and quality at the national level with moderate-high levels of accuracy.

Aiken Vera Cardoso Ferreira, Fernandes Arthur Francisco Araújo, Passafaro Tiago Luciano, Acedo Juliano Sabella, Dias Fábio Guerra, Dórea João Ricardo Rebouças, Rosa Guilherme Jordão de Magalhães

2020-Mar-23

Brazil, beef, forecasting, large scale data, machine learning

General General

Application of artificially intelligent systems for the identification of discrete fossiliferous levels.

In PeerJ

The separation of discrete fossiliferous levels within an archaeological or paleontological site with no clear stratigraphic horizons has historically been carried out using qualitative approaches, relying on two-dimensional transversal and longitudinal projection planes. Analyses of this type, however, can often be conditioned by subjectivity based on the perspective of the analyst. This study presents a novel use of Machine Learning algorithms for pattern recognition techniques in the automated separation and identification of fossiliferous levels. This approach can be divided into three main steps including: (1) unsupervised Machine Learning for density based clustering (2) expert-in-the-loop Collaborative Intelligence Learning for the integration of geological data followed by (3) supervised learning for the final fine-tuning of fossiliferous level models. For evaluation of these techniques, this method was tested in two Late Miocene sites of the Batallones Butte paleontological complex (Madrid, Spain). Here we show Machine Learning analyses to be a valuable tool for the processing of spatial data in an efficient and quantitative manner, successfully identifying the presence of discrete fossiliferous levels in both Batallones-3 and Batallones-10. Three discrete fossiliferous levels have been identified in Batallones-3, whereas another three have been differentiated in Batallones-10.

Martín-Perea David M, Courtenay Lloyd A, Domingo M Soledad, Morales Jorge

2020

Archaeological site, Archaeostratigraphy, Batallones Butte sites, Machine Learning, Palaeontological site, Palaeostratigraphy, Spatial data

Public Health Public Health

Protocol for the Effectiveness of an Anesthesiology Control Tower System in Improving Perioperative Quality Metrics and Clinical Outcomes: the TECTONICS randomized, pragmatic trial.

In F1000Research

Introduction: Perioperative morbidity is a public health priority, and surgical volume is increasing rapidly. With advances in technology, there is an opportunity to research the utility of a telemedicine-based control center for anesthesia clinicians that assess risk, diagnoses negative patient trajectories, and implements evidence-based practices. Objectives: The primary objective of this trial is to determine whether an anesthesiology control tower (ACT) prevents clinically relevant adverse postoperative outcomes including 30-day mortality, delirium, respiratory failure, and acute kidney injury. Secondary objectives are to determine whether the ACT improves perioperative quality of care metrics including management of temperature, mean arterial pressure, mean airway pressure with mechanical ventilation, blood glucose, anesthetic concentration, antibiotic redosing, and efficient fresh gas flow. Methods and analysis: We are conducting a single center, randomized, controlled, phase 3 pragmatic clinical trial. A total of 58 operating rooms are randomized daily to receive support from the ACT or not. All adults (eighteen years and older) undergoing surgical procedures in these operating rooms are included and followed until 30 days after their surgery. Clinicians in operating rooms randomized to ACT support receive decision support from clinicians in the ACT. In operating rooms randomized to no intervention, the current standard of anesthesia care is delivered. The intention-to-treat principle will be followed for all analyses. Differences between groups will be presented with 99% confidence intervals; p-values <0.005 will be reported as providing compelling evidence, and p-values between 0.05 and 0.005 will be reported as providing suggestive evidence. Registration: TECTONICS is registered on ClinicalTrials.gov, NCT03923699; registered on 23 April 2019.

King Christopher R, Abraham Joanna, Kannampallil Thomas G, Fritz Bradley A, Ben Abdallah Arbi, Chen Yixin, Henrichs Bernadette, Politi Mary, Torres Brian A, Mickle Angela, Budelier Thaddeus P, McKinnon Sherry, Gregory Stephen, Kheterpal Sachin, Wildes Troy, Avidan Michael S

2019

Anesthesiology, Artificial Intelligence, Decision Support, Forecasting Algorithms, Machine Learning, Randomized Controlled Trial, Telemedicine

Public Health Public Health

Peptide arrays of three collections of human sera from patients infected with mosquito-borne viruses.

In F1000Research

Background: Global outbreaks caused by emerging or re-emerging arthropod-borne viruses (arboviruses) are becoming increasingly more common. These pathogens include the mosquito-borne viruses belonging to the Flavivirus and Alphavirus genera. These viruses often cause non-specific or asymptomatic infection, which can confound viral prevalence studies. In addition, many acute phase diagnostic tests rely on the detection of viral components such as RNA or antigen. Standard serological tests are often not reliable for diagnosis after seroconversion and convalescence due to cross-reactivity among flaviviruses. Methods: In order to contribute to development efforts for mosquito-borne serodiagnostics, we incubated 137 human sera on individual custom peptide arrays that consisted of over 866 unique peptides in quadruplicate. Our bioinformatics workflow to analyze these data incorporated machine learning, statistics, and B-cell epitope prediction. Results: Here we report the results of our peptide array data analysis, which revealed sets of peptides that have diagnostic potential for detecting past exposure to a subset of the tested human pathogens including Zika virus. These peptides were then confirmed using the well-established ELISA method. Conclusions: These array data, and the resulting peptides can be useful in diverse efforts including the development of new pan-flavivirus antibodies, more accurate epitope mapping, and vaccine development against these viral pathogens.

Martinez Viedma Maria Del Pilar, Kose Nurgun, Parham Leda, Balmaseda Angel, Kuan Guillermina, Lorenzana Ivette, Harris Eva, Crowe James E, Pickett Brett E

2019

B-cell epitopes, Zika virus, bioinformatics, mosquito-borne viruses, peptide arrays, serodiagnostic

Radiology Radiology

CT-based radiomics for preoperative prediction of early recurrent hepatocellular carcinoma: technical reproducibility of acquisition and scanners.

In La Radiologia medica

PURPOSE : To test the technical reproducibility of acquisition and scanners of CT image-based radiomics model for early recurrent hepatocellular carcinoma (HCC).

METHODS : We included primary HCC patient undergone curative therapies, using early recurrence as endpoint. Four datasets were constructed: 109 images from hospital #1 for training (set 1: 1-mm image slice thickness), 47 images from hospital #1 for internal validation (sets 2 and 3: 1-mm and 10-mm image slice thicknesses, respectively), and 47 images from hospital #2 for external validation (set 4: vastly different from training dataset). A radiomics model was constructed. Radiomics technical reproducibility was measured by overfitting and calibration deviation in external validation dataset. The influence of slice thickness on reproducibility was evaluated in two internal validation datasets.

RESULTS : Compared with set 1, the model in set 2 indicated favorable prediction efficiency (the area under the curve 0.79 vs. 0.80, P = 0.47) and good calibration (unreliability statistic U: P = 0.33). However, in set 4, significant overfitting (0.63 vs. 0.80, P < 0.01) and calibration deviation (U: P < 0.01) were observed. Similar poor performance was also observed in set 3 (0.56 vs. 0.80, P = 0.02; U: P < 0.01).

CONCLUSIONS : CT-based radiomics has poor reproducibility between centers. Image heterogeneity, such as slice thickness, can be a significant influencing factor.

Hu Hang-Tong, Shan Quan-Yuan, Chen Shu-Ling, Li Bin, Feng Shi-Ting, Xu Er-Jiao, Li Xin, Long Jian-Yan, Xie Xiao-Yan, Lu Ming-de, Kuang Ming, Shen Jing-Xian, Wang Wei

2020-Mar-21

Hepatocellular carcinoma, Radiomics, Reproducibility, Tomography

General General

Deep feature-based automatic classification of mammograms.

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

Breast cancer has the second highest frequency of death rate among women worldwide. Early-stage prevention becomes complex due to reasons unknown. However, some typical signatures like masses and micro-calcifications upon investigating mammograms can help diagnose women better. Manual diagnosis is a hard task the radiologists carry out frequently. For their assistance, many computer-aided diagnosis (CADx) approaches have been developed. To improve upon the state of the art, we proposed a deep ensemble transfer learning and neural network classifier for automatic feature extraction and classification. In computer-assisted mammography, deep learning-based architectures are generally not trained on mammogram images directly. Instead, the images are pre-processed beforehand, and then they are adopted to be given as input to the ensemble model proposed. The robust features extracted from the ensemble model are optimized into a feature vector which are further classified using the neural network (nntraintool). The network was trained and tested to separate out benign and malignant tumors, thus achieving an accuracy of 0.88 with an area under curve (AUC) of 0.88. The attained results show that the proposed methodology is a promising and robust CADx system for breast cancer classification. Graphical Abstract Flow diagram of the proposed approach. Figure depicts the deep ensemble extracting the robust features with the final classification using neural networks.

Arora Ridhi, Rai Prateek Kumar, Raman Balasubramanian

2020-Mar-21

Breast lesion, Computer-aided diagnosis, Feature extraction, Image processing, Medical imaging

Public Health Public Health

Efficacy of robot-assisted gait training in multiple sclerosis: A systematic review and meta-analysis.

In Multiple sclerosis and related disorders

BACKGROUND : Multiple sclerosis is a progressive disease responsible for gait disabilities and cognitive impairment, which affect functional performance. Robot-assisted gait training is an emerging training method to facilitate body-weight-supported treadmill training in many neurologic diseases. Through this study, we aimed to determine the efficacy of robot-assisted gait training in patients with multiple sclerosis.

METHODS : We performed a systematic review and meta-analysis of randomized controlled trials evaluating the effect of robot-assisted gait training for multiple sclerosis. We searched PubMed, EMBASE, the Cochrane Library, and ClinicalTrials.gov registry for articles published before May 2019. The primary outcome was walking performance (gait parameters, balance, and ambulation capability). The secondary outcomes were changes in perceived fatigue, severity of spasticity, global mobility, physical and mental quality of life, severity of pain, activities of daily living, and treatment acceptance.

RESULTS : We identified 10 studies (9 different trials) that included patients with multiple sclerosis undergoing robot-assisted gait training or conventional walk training. The meta-analysis showed comparable effectiveness between robot-assisted gait training and conventional walking therapy in walking performance, quality of life, pain, or activities of daily living. The robot-assisted gait training was even statistically superior to conventional walking therapy in improving perceived fatigue (pooled SMD: 0.34, 95% CI: 0.02-0.67), spasticity (pooled SMD: 0.70, 95% CI: 0.08-1.33, I² = 53%), and global mobility (borderline) after the intervention.

CONCLUSION : Our results provide the most up-to-date evidence regarding the robot-assisted gait training on multiple sclerosis. In addition to the safety and good tolerance, its efficacy on multiple sclerosis is comparable to that of conventional walking training and is even superior in improving fatigue and spasticity.

Yeh Shu-Wei, Lin Li-Fong, Tam Ka-Wai, Tsai Ching-Piao, Hong Chien-Hsiung, Kuan Yi-Chun

2020-Mar-03

Fatigue, Multiple sclerosis, Quality of life, Robot-assisted gait training, Walking performance

oncology Oncology

A lesson in implementation: A pre-post study of providers' experience with artificial intelligence-based clinical decision support.

In International journal of medical informatics ; h5-index 49.0

BACKGROUND : To explore attitudes about artificial intelligence (AI) among staff who utilized AI-based clinical decision support (CDS).

METHODS : A survey was designed to assess staff attitudes about AI-based CDS tools. The survey was anonymously and voluntarily completed by clinical staff in three primary care outpatient clinics before and after implementation of an AI-based CDS system aimed to improve glycemic control in patients with diabetes as part of a quality improvement project. The CDS identified patients at risk for poor glycemic control and generated intervention recommendations intended to reduce patients' risk.

RESULTS : Staff completed 45 surveys pre-intervention and 38 post-intervention. Following implementation, staff felt that care was better coordinated (11 favorable responses, 14 unfavorable responses pre-intervention; 21 favorable responses, 3 unfavorable responses post-intervention; p < 0.01). However, only 14 % of users would recommend the AI-based CDS. Staff feedback revealed that the most favorable aspect of the CDS was that it promoted team dialog about patient needs (N = 14, 52 %), and the least favorable aspect was inadequacy of the interventions recommended by the CDS.

CONCLUSIONS : AI-based CDS tools that are perceived negatively by staff may reduce staff excitement about AI technology, and hands-on experience with AI may lead to more realistic expectations about the technology's capabilities. In our setting, although AI-based CDS prompted an interdisciplinary discussion about the needs of patients at high risk for poor glycemic control, the interventions recommended by the CDS were often perceived to be poorly tailored, inappropriate, or not useful. Developers should carefully consider tasks that are best performed by AI and those best performed by the patient's care team.

Romero-Brufau Santiago, Wyatt Kirk D, Boyum Patricia, Mickelson Mindy, Moore Matthew, Cognetta-Rieke Cheristi

2019-Dec-30

Artificial, Clinical, Decision support systems, Diabetes mellitus, Intelligence

General General

Hyperspectral imaging for accurate determination of rice variety using a deep learning network with multi-feature fusion.

In Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

The phenomena of rice adulteration and shoddy rice arise continuously in high-quality rice and reduce the interests of producers, consumers and traders. Hyperspectral imaging (HSI) was conducted to determine rice variety using a deep learning network with multiple features, namely, spectroscopy, texture and morphology. HSI images of 10 representative high-quality rice varieties in China were measured. Spectroscopy and morphology were extracted from HSI images and binary images in region of interest, respectively. And texture was obtained from the monochromatic images of characteristic wavelengths which were highly correlated with rice varieties. A deep learning network, namely principal component analysis network (PCANet), was adopted with these features to develop classification models for determining rice variety, and machine learning methods as K-nearest neighbour and random forest were used to compare with PCANet. Meanwhile, multivariate scatter correction, standard normal variate, Savitzky-Golay smoothing and Savitzky-Golay's first-order were applied to eliminate spectral interference, and principal component analysis (PCA) was performed to obtain the main information of high-dimensional features. Multi-feature fusion improved recognition accuracy, and PCANet demonstrated considerable advantage in classification performance. The best result was achieved by PCANet with PCA-processed spectroscopic and texture features with correct classification rates of 98.66% and 98.57% for the training and prediction sets, respectively. In summary, the proposed method provides an accurate identification of rice variety and can be easily extended to the classification, attribution and grading of other agricultural products.

Weng Shizhuang, Tang Peipei, Yuan Hecai, Guo Bingqing, Yu Shuan, Huang Linsheng, Xu Chao

2020-Mar-06

Deep learning network, High-quality rice, Hyperspectral imaging, Multi-feature fusion

Cardiology Cardiology

Interpatient Similarities in Cardiac Function: A Platform for Personalized Cardiovascular Medicine.

In JACC. Cardiovascular imaging

OBJECTIVES : The authors applied unsupervised machine-learning techniques for integrating echocardiographic features of left ventricular (LV) structure and function into a patient similarity network that predicted major adverse cardiac events (MACE) in an individual patient.

BACKGROUND : Patient similarity analysis is an evolving paradigm for precision medicine in which patients are clustered or classified based on their similarities in several clinical features.

METHODS : A retrospective cohort of 866 patients was used to develop a network architecture using 9 echocardiographic features of LV structure and function. The data for 468 patients from 2 prospective cohort registries were then added to test the model's generalizability.

RESULTS : The map of cross-sectional data in the retrospective cohort resulted in a looped patient network that persisted even after the addition of data from the prospective cohort registries. After subdividing the loop into 4 regions, patients in each region showed unique differences in LV function, with Kaplan-Meier curves demonstrating significant differences in MACE-related rehospitalization and death (both p < 0.001). Addition of network information to clinical risk predictors resulted in significant improvements in net reclassification, integrated discrimination, and median risk scores for predicting MACE (p < 0.05 for all). Furthermore, the network predicted the cardiac disease cycle in each of the 96 patients who had second echocardiographic evaluations. An improvement or remaining in low-risk regions was associated with lower MACE-related rehospitalization rates than worsening or remaining in high-risk regions (3% vs. 37%; p < 0.001).

CONCLUSIONS : Patient similarity analysis integrates multiple features of cardiac function to develop a phenotypic network in which patients can be mapped to specific locations associated with specific disease stage and clinical outcomes. The use of patient similarity analysis may have relevance for automated staging of cardiac disease severity, personalized prediction of prognosis, and monitoring progression or response to therapies.

Tokodi Márton, Shrestha Sirish, Bianco Christopher, Kagiyama Nobuyuki, Casaclang-Verzosa Grace, Narula Jagat, Sengupta Partho P

2020-Mar-13

echocardiography, patient similarity, topological data analysis

General General

From Bit To Bedside: A Practical Framework For Artificial Intelligence Product Development In Healthcare

ArXiv Preprint

Artificial Intelligence (AI) in healthcare holds great potential to expand access to high-quality medical care, whilst reducing overall systemic costs. Despite hitting the headlines regularly and many publications of proofs-of-concept, certified products are failing to breakthrough to the clinic. AI in healthcare is a multi-party process with deep knowledge required in multiple individual domains. The lack of understanding of the specific challenges in the domain is, therefore, the major contributor to the failure to deliver on the big promises. Thus, we present a decision perspective framework, for the development of AI-driven biomedical products, from conception to market launch. Our framework highlights the risks, objectives and key results which are typically required to proceed through a three-phase process to the market launch of a validated medical AI product. We focus on issues related to Clinical validation, Regulatory affairs, Data strategy and Algorithmic development. The development process we propose for AI in healthcare software strongly diverges from modern consumer software development processes. We highlight the key time points to guide founders, investors and key stakeholders throughout their relevant part of the process. Our framework should be seen as a template for innovation frameworks, which can be used to coordinate team communications and responsibilities towards a reasonable product development roadmap, thus unlocking the potential of AI in medicine.

David Higgins, Vince I. Madai

2020-03-23

General General

Doctors in Star Trek: Reflections on the changing faces of future doctors.

In Early human development

Science fiction is all around us, manifesting in fiction, TV series, blockbuster movies etc. The Star Trek (ST) universe has become an integral element of popular culture and doctors play important roles. This paper introduces depictions of these individuals over the decades since the inception of the series in 1966. The doctors portrayed have reflected the shifting expectations of the general public in that medics have morphed successively from an old-style country doctor, to a single supermom, to a genetically engineered human, to a sentient, computer-generated hologram and to an alien who uses also uses natural healing methods. The doctor in the latest ST series has broken another barrier in that this is the first series to deliberately include homosexual couples within the Star Trek universe for the first time in its fifty-one odd year lifespan and the doctor is the first openly gay character. These doctors are expected to demonstrate total accessibility, the ability to utilise natural remedies when possible, compassion and unstinting commitment to their patients and their profession, infallibility and broad skills with flexibility that allows them to deal with virtually anything, in anyone/anything. These capacities appear desirable even if the traditional doctor is replaced by a machine, a warning for the medical profession. A second collection of papers will further explore the ST universe by analysing unethical medical experimentation, artificial intelligence (AI) and the institution of ethics in AI, the application of nanotechnology in biology and depictions of the nursing profession in this fictive future.

Grech Victor

2020-Mar-18

Surgery Surgery

Robust Medical Instrument Segmentation Challenge 2019

ArXiv Preprint

Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. While numerous methods for detecting, segmenting and tracking of medical instruments based on endoscopic video images have been proposed in the literature, key limitations remain to be addressed: Firstly, robustness, that is, the reliable performance of state-of-the-art methods when run on challenging images (e.g. in the presence of blood, smoke or motion artifacts). Secondly, generalization; algorithms trained for a specific intervention in a specific hospital should generalize to other interventions or institutions. In an effort to promote solutions for these limitations, we organized the Robust Medical Instrument Segmentation (ROBUST-MIS) challenge as an international benchmarking competition with a specific focus on the robustness and generalization capabilities of algorithms. For the first time in the field of endoscopic image processing, our challenge included a task on binary segmentation and also addressed multi-instance detection and segmentation. The challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures from three different types of surgery. The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap. While the average detection and segmentation quality of the best-performing algorithms is high, future research should concentrate on detection and segmentation of small, crossing, moving and transparent instrument(s) (parts).

Tobias Ross, Annika Reinke, Peter M. Full, Martin Wagner, Hannes Kenngott, Martin Apitz, Hellena Hempe, Diana Mindroc Filimon, Patrick Scholz, Thuy Nuong Tran, Pierangela Bruno, Pablo Arbeláez, Gui-Bin Bian, Sebastian Bodenstedt, Jon Lindström Bolmgren, Laura Bravo-Sánchez, Hua-Bin Chen, Cristina González, Dong Guo, Pål Halvorsen, Pheng-Ann Heng, Enes Hosgor, Zeng-Guang Hou, Fabian Isensee, Debesh Jha, Tingting Jiang, Yueming Jin, Kadir Kirtac, Sabrina Kletz, Stefan Leger, Zhixuan Li, Klaus H. Maier-Hein, Zhen-Liang Ni, Michael A. Riegler, Klaus Schoeffmann, Ruohua Shi, Stefanie Speidel, Michael Stenzel, Isabell Twick, Gutai Wang, Jiacheng Wang, Liansheng Wang, Lu Wang, Yujie Zhang, Yan-Jie Zhou, Lei Zhu, Manuel Wiesenfarth, Annette Kopp-Schneider, Beat P. Müller-Stich, Lena Maier-Hein

2020-03-23

General General

Editorial.

In Early human development

Science fiction (SF) is ubiquitous and it has also been utilised for the purposes of teaching since it has replaced legend, myth and fable. This is especially the case for Star Trek (ST) which has become an integral part of popular culture, even for those who do not follow SF. In this Best Practice Guideline (BPG) we will engage topics that ordinary readers of EHD might not normally come across, but may well find interesting. We will review the individual doctors in ST from the viewpoint of a medical doctor, and will demonstrate the ways in which the medic in the various series (which spans decades, since 1966) reflects the zeitgeist. A second BPG will provide an assortment of ST cautionary tales which range from nanotechnology, to The Holocaust, to artificial intelligence. SF is famously "extravagant fiction today, cold fact tomorrow" and takes us "where no man has gone before". The imaginings of SF authors (some of whom are scientists and doctors) should be taken seriously for potential detrimental effects/events that may befall the medical profession or the human race, and that might be avoided with the foresight provided by SF.

Grech Victor

2020-Mar-18

General General

DYSAN: Dynamically sanitizing motion sensor data against sensitive inferences through adversarial networks

ArXiv Preprint

With the widespread adoption of the quantified self movement, an increasing number of users rely on mobile applications to monitor their physical activity through their smartphones. Granting to applications a direct access to sensor data expose users to privacy risks. Indeed, usually these motion sensor data are transmitted to analytics applications hosted on the cloud leveraging machine learning models to provide feedback on their health to users. However, nothing prevents the service provider to infer private and sensitive information about a user such as health or demographic attributes.In this paper, we present DySan, a privacy-preserving framework to sanitize motion sensor data against unwanted sensitive inferences (i.e., improving privacy) while limiting the loss of accuracy on the physical activity monitoring (i.e., maintaining data utility). To ensure a good trade-off between utility and privacy, DySan leverages on the framework of Generative Adversarial Network (GAN) to sanitize the sensor data. More precisely, by learning in a competitive manner several networks, DySan is able to build models that sanitize motion data against inferences on a specified sensitive attribute (e.g., gender) while maintaining a high accuracy on activity recognition. In addition, DySan dynamically selects the sanitizing model which maximize the privacy according to the incoming data. Experiments conducted on real datasets demonstrate that DySan can drasticallylimit the gender inference to 47% while only reducing the accuracy of activity recognition by 3%.

Antoine Boutet, Carole Frindel, Sébastien Gambs, Théo Jourdan, Claude Rosin Ngueveu

2020-03-23

General General

Are parents ready to use autonomous vehicles to transport children? Concerns and safety features.

In Journal of safety research

INTRODUCTION : This study addressed a gap in the literature - the potential of using autonomous vehicles (AV) to enhance children's mobility. Prior studies documented the perceived benefits and concerns about this prospect, but did not examine the features in AV and support mechanisms that are desired by potential users.

METHOD : An on-line survey was used to collect public opinions within the United States. In the survey, willingness to use AVs for this use case was asked twice to assess if participants changed their mind after being asked about concerns related to this prospect and importance of car features. A combination of statistical and machine-learning methods were used to profile individuals with high versus low post-willingness and to identify variables that differentiated the two groups.

RESULTS : Results indicated that respondents who were lower on their post-willingness to use AVs to transport children were more concerned about how AVs would protect children, how someone could harm the children inside, and whether there would be someone at the destination. In addition, they were less in favor of technology, older in age, and rated car features such as having a designated adult waiting at destination, a camera, and a microphone as relatively required (as opposed to optional). These results highlight potential users' needs and requirements as they think about AVs in the context of parent-children mobility practices. Practical Applications: Relevant stakeholders should develop deployment and implementation plans while taking into account ridership contexts and vulnerable road users who can benefit from enhanced mobility.

Lee Yi-Ching, Hand Somer H, Lilly Hsien

2020-Feb

Autonomous vehicle, Children’s mobility, Ridership context, Safety, Vulnerable road users

General General

Deep Bayesian Gaussian Processes for Uncertainty Estimation in Electronic Health Records

ArXiv Preprint

One major impediment to the wider use of deep learning for clinical decision making is the difficulty of assigning a level of confidence to model predictions. Currently, deep Bayesian neural networks and sparse Gaussian processes are the main two scalable uncertainty estimation methods. However, deep Bayesian neural network suffers from lack of expressiveness, and more expressive models such as deep kernel learning, which is an extension of sparse Gaussian process, captures only the uncertainty from the higher level latent space. Therefore, the deep learning model under it lacks interpretability and ignores uncertainty from the raw data. In this paper, we merge features of the deep Bayesian learning framework with deep kernel learning to leverage the strengths of both methods for more comprehensive uncertainty estimation. Through a series of experiments on predicting the first incidence of heart failure, diabetes and depression applied to large-scale electronic medical records, we demonstrate that our method is better at capturing uncertainty than both Gaussian processes and deep Bayesian neural networks in terms of indicating data insufficiency and distinguishing true positive and false positive predictions, with a comparable generalisation performance. Furthermore, by assessing the accuracy and area under the receiver operating characteristic curve over the predictive probability, we show that our method is less susceptible to making overconfident predictions, especially for the minority class in imbalanced datasets. Finally, we demonstrate how uncertainty information derived by the model can inform risk factor analysis towards model interpretability.

Yikuan Li, Shishir Rao, Abdelaali Hassaine, Rema Ramakrishnan, Yajie Zhu, Dexter Canoy, Gholamreza Salimi-Khorshidi, Thomas Lukasiewicz, Kazem Rahimi

2020-03-23

General General

Expanded access as a source of real-world data An overview of FDA and EMA approvals.

In British journal of clinical pharmacology ; h5-index 58.0

AIMS : To identify, characterize, and compare all FDA and EMA approvals that included real-world data on efficacy from expanded access programs METHODS: Cross-sectional study of FDA (1955-2018) and EMA (1995-2018) regulatory approval documentation. We automated searching for terms related to expanded access in 22,506 documents using machine learning techniques. We included all approvals where expanded access terms appeared in the regulatory documentation. Our main outcome was the inclusion of expanded access data as evidence of clinical efficacy. Characterization was based on approval date, disease area, orphan designation and whether the evidence was supportive or pivotal.

RESULTS : Expanded access terms appeared in 693 out of 22,506 (3.1%) documents, which referenced 187 approvals. For 39 approvals, data from expanded access programs were used to inform on clinical efficacy. The yearly number of approvals with EA data increased from 1.25 for 1993-2013 to 4.6 from 2014-2018. In 13 cases, these programs formed the main evidence for approval. Of these, patients in expanded access programs formed over half (median 71%, IQR: 34-100) of the total patient population available for efficacy evaluation. All but one (12/13) approvals were granted orphan designation. In 8/13, there were differences between regulators in approval status and valuation of evidence. Strikingly, 4 treatments were granted approval based solely on efficacy from expanded access.

CONCLUSIONS : Sponsors and regulators increasingly include real-world data from expanded access programs in the efficacy profile of a treatment. The indications of the approved treatments are characterized by orphan designation and high unmet medical need.

Polak Tobias B, van Rosmalen Joost, Uyl-de Groot Carin A

2020-Mar-22

Drug Regulation, Effectiveness, Evidence-Based Medicine, Health Policy

Surgery Surgery

Machine Learning and Feature Selection Applied to SEER Data to Reliably Assess Thyroid Cancer Prognosis.

In Scientific reports ; h5-index 158.0

Utilizing historical clinical datasets to guide future treatment choices is beneficial for patients and physicians. Machine learning and feature selection algorithms (namely, Fisher's discriminant ratio, Kruskal-Wallis' analysis, and Relief-F) have been combined in this research to analyse a SEER database containing clinical features from de-identified thyroid cancer patients. The data covered 34 unique clinical variables such as patients' age at diagnosis or information regarding lymph nodes, which were employed to build various novel classifiers to distinguish patients that lived for over 10 years since diagnosis, from those who did not survive at least five years. By properly optimizing supervised neural networks, specifically multilayer perceptrons, using data from large groups of thyroid cancer patients (between 6,756 and 20,344 for different models), we demonstrate that unspecialized and existing medical recording can be reliably turned into power of prediction to help doctors make informed and optimized treatment decisions, as distinguishing patients in terms of prognosis has been achieved with 94.5% accuracy. We also envisage the potential of applying our machine learning strategy to other diseases and purposes such as in designing clinical trials for unmasking the maximum benefits and minimizing risks associated with new drug candidates on given populations.

Mourad Moustafa, Moubayed Sami, Dezube Aaron, Mourad Youssef, Park Kyle, Torreblanca-Zanca Albertina, Torrecilla José S, Cancilla John C, Wang Jiwu

2020-Mar-20

Surgery Surgery

Aging induces aberrant state transition kinetics in murine muscle stem cells.

In Development (Cambridge, England)

Murine muscle stem cells (MuSCs) experience a transition from quiescence to activation that is required for regeneration, but it remains unknown if the trajectory and dynamics of activation change with age. Here, we use timelapse imaging and single cell RNA-seq to measure activation trajectories and rates in young and aged MuSCs. We find that the activation trajectory is conserved in aged cells, and develop effective machine learning classifiers for cell age. Using cell behavior analysis and RNA velocity, we find that activation kinetics are delayed in aged MuSCs, suggesting that changes in stem cell dynamics may contribute to impaired stem cell function with age. Intriguingly, we also find that stem cell activation appears to be a random walk like process, with frequent reversals, rather than a continuous, linear progression. These results support a view of the aged stem cell phenotype as a combination of differences in the location of stable cell states and differences in transition rates between them.

Kimmel Jacob C, Hwang Ara B, Scaramozza Annarita, Marshall Wallace F, Brack Andrew S

2020-Mar-20

Aging, Muscle stem cell, Single cell RNA-seq, State transition, Stem cell activation, Timelapse imaging

Radiology Radiology

Identification of Non-Small Cell Lung Cancer Sensitive to Systemic Cancer Therapies Using Radiomics.

In Clinical cancer research : an official journal of the American Association for Cancer Research

PURPOSE : Using standard-of-care CT images obtained from patients with a diagnosis of non-small cell lung cancer (NSCLC), we defined radiomics signatures predicting the sensitivity of tumors to nivolumab, docetaxel, and gefitinib.

EXPERIMENTAL DESIGN : Data were collected prospectively and analyzed retrospectively across multicenter clinical trials [nivolumab, n = 92, CheckMate017 (NCT01642004), CheckMate063 (NCT01721759); docetaxel, n = 50, CheckMate017; gefitinib, n = 46, (NCT00588445)]. Patients were randomized to training or validation cohorts using either a 4:1 ratio (nivolumab: 72T:20V) or a 2:1 ratio (docetaxel: 32T:18V; gefitinib: 31T:15V) to ensure an adequate sample size in the validation set. Radiomics signatures were derived from quantitative analysis of early tumor changes from baseline to first on-treatment assessment. For each patient, 1,160 radiomics features were extracted from the largest measurable lung lesion. Tumors were classified as treatment sensitive or insensitive; reference standard was median progression-free survival (NCT01642004, NCT01721759) or surgery (NCT00588445). Machine learning was implemented to select up to four features to develop a radiomics signature in the training datasets and applied to each patient in the validation datasets to classify treatment sensitivity.

RESULTS : The radiomics signatures predicted treatment sensitivity in the validation dataset of each study group with AUC (95 confidence interval): nivolumab, 0.77 (0.55-1.00); docetaxel, 0.67 (0.37-0.96); and gefitinib, 0.82 (0.53-0.97). Using serial radiographic measurements, the magnitude of exponential increase in signature features deciphering tumor volume, invasion of tumor boundaries, or tumor spatial heterogeneity was associated with shorter overall survival.

CONCLUSIONS : Radiomics signatures predicted tumor sensitivity to treatment in patients with NSCLC, offering an approach that could enhance clinical decision-making to continue systemic therapies and forecast overall survival.

Dercle Laurent, Fronheiser Matthew, Lu Lin, Du Shuyan, Hayes Wendy, Leung David K, Roy Amit, Wilkerson Julia, Guo Pingzhen, Fojo Antonio T, Schwartz Lawrence H, Zhao Binsheng

2020-Mar-20

Ophthalmology Ophthalmology

Aggressive Posterior Retinopathy of Prematurity: Clinical and Quantitative Imaging Features in a Large North American Cohort.

In Ophthalmology ; h5-index 90.0

PURPOSE : Aggressive posterior retinopathy of prematurity (AP-ROP) is a vision-threatening disease with a significant rate of progression to retinal detachment. The purpose of this study was to characterize AP-ROP quantitatively by demographics, rate of disease progression, and a deep learning-based vascular severity score.

DESIGN : Retrospective analysis.

PARTICIPANTS : The Imaging and Informatics in ROP cohort from 8 North American centers, consisting of 947 patients and 5945 clinical eye examinations with fundus images, was used. Pretreatment eyes were categorized by disease severity: none, mild, type 2 or pre-plus, treatment-requiring (TR) without AP-ROP, TR with AP-ROP. Analyses compared TR with AP-ROP and TR without AP-ROP to investigate differences between AP-ROP and other TR disease.

METHODS : A reference standard diagnosis was generated for each eye examination using previously published methods combining 3 independent image-based gradings and 1 ophthalmoscopic grading. All fundus images were analyzed using a previously published deep learning system and were assigned a score from 1 through 9.

MAIN OUTCOME MEASURES : Birth weight, gestational age, postmenstrual age, and vascular severity score.

RESULTS : Infants who demonstrated AP-ROP were more premature by birth weight (617 g vs. 679 g; P = 0.01) and gestational age (24.3 weeks vs. 25.0 weeks; P < 0.01) and reached peak severity at an earlier postmenstrual age (34.7 weeks vs. 36.9 weeks; P < 0.001) compared with infants with TR without AP-ROP. The mean vascular severity score was greatest in TR with AP-ROP infants compared with TR without AP-ROP infants (8.79 vs. 7.19; P < 0.001). Analyzing the severity score over time, the rate of progression was fastest in infants with AP-ROP (P < 0.002 at 30-32 weeks).

CONCLUSIONS : Premature infants in North America with AP-ROP are born younger and demonstrate disease earlier than infants with less severe ROP. Disease severity is quantifiable with a deep learning-based score, which correlates with clinically identified categories of disease, including AP-ROP. The rate of progression to peak disease is greatest in eyes that demonstrate AP-ROP compared with other treatment-requiring eyes. Analysis of quantitative characteristics of AP-ROP may help improve diagnosis and treatment of an aggressive, vision-threatening form of ROP.

Bellsmith Kellyn N, Brown James, Kim Sang Jin, Goldstein Isaac H, Coyner Aaron, Ostmo Susan, Gupta Kishan, Chan R V Paul, Kalpathy-Cramer Jayashree, Chiang Michael F, Campbell J Peter

2020-Feb-07

Ophthalmology Ophthalmology

A Deep Learning Model for Segmentation of Geographic Atrophy to Study Its Long-Term Natural History.

In Ophthalmology ; h5-index 90.0

PURPOSE : To develop and validate a deep learning model for the automatic segmentation of geographic atrophy (GA) using color fundus images (CFIs) and its application to study the growth rate of GA.

DESIGN : Prospective, multicenter, natural history study with up to 15 years of follow-up.

PARTICIPANTS : Four hundred nine CFIs of 238 eyes with GA from the Rotterdam Study (RS) and Blue Mountain Eye Study (BMES) for model development, and 3589 CFIs of 376 eyes from the Age-Related Eye Disease Study (AREDS) for analysis of GA growth rate.

METHODS : A deep learning model based on an ensemble of encoder-decoder architectures was implemented and optimized for the segmentation of GA in CFIs. Four experienced graders delineated, in consensus, GA in CFIs from the RS and BMES. These manual delineations were used to evaluate the segmentation model using 5-fold cross-validation. The model was applied further to CFIs from the AREDS to study the growth rate of GA. Linear regression analysis was used to study associations between structural biomarkers at baseline and the GA growth rate. A general estimate of the progression of GA area over time was made by combining growth rates of all eyes with GA from the AREDS set.

MAIN OUTCOME MEASURES : Automatically segmented GA and GA growth rate.

RESULTS : The model obtained an average Dice coefficient of 0.72±0.26 on the BMES and RS set while comparing the automatically segmented GA area with the graders' manual delineations. An intraclass correlation coefficient of 0.83 was reached between the automatically estimated GA area and the graders' consensus measures. Nine automatically calculated structural biomarkers (area, filled area, convex area, convex solidity, eccentricity, roundness, foveal involvement, perimeter, and circularity) were significantly associated with growth rate. Combining all growth rates indicated that GA area grows quadratically up to an area of approximately 12 mm2, after which growth rate stabilizes or decreases.

CONCLUSIONS : The deep learning model allowed for fully automatic and robust segmentation of GA on CFIs. These segmentations can be used to extract structural characteristics of GA that predict its growth rate.

Liefers Bart, Colijn Johanna M, González-Gonzalo Cristina, Verzijden Timo, Wang Jie Jin, Joachim Nichole, Mitchell Paul, Hoyng Carel B, van Ginneken Bram, Klaver Caroline C W, Sánchez Clara I

2020-Feb-15

Surgery Surgery

Deep Learning-Based Risk Model for Best Management of Closed Groin Incisions After Vascular Surgery.

In The Journal of surgical research

BACKGROUND : Reduced surgical site infection (SSI) rates have been reported with use of closed incision negative pressure therapy (ciNPT) in high-risk patients.

METHODS : A deep learning-based, risk-based prediction model was developed from a large national database of 72,435 patients who received infrainguinal vascular surgeries involving upper thigh/groin incisions. Patient demographics, histories, laboratory values, and other variables were inputs to the multilayered, adaptive model. The model was then retrospectively applied to a prospectively tracked single hospital data set of 370 similar patients undergoing vascular surgery, with ciNPT or control dressings applied over the closed incision at the surgeon's discretion. Objective predictive risk scores were generated for each patient and used to categorize patients as "high" or "low" predicted risk for SSI.

RESULTS : Actual institutional cohort SSI rates were 10/148 (6.8%) and 28/134 (20.9%) for high-risk ciNPT versus control, respectively (P < 0.001), and 3/31 (9.7%) and 5/57 (8.8%) for low-risk ciNPT versus control, respectively (P = 0.99). Application of the model to the institutional cohort suggested that 205/370 (55.4%) patients were matched with their appropriate intervention over closed surgical incision (high risk with ciNPT or low risk with control), and 165/370 (44.6%) were inappropriately matched. With the model applied to the cohort, the predicted SSI rate with perfect utilization would be 27/370 (7.3%), versus 12.4% actual rate, with estimated cost savings of $231-$458 per patient.

CONCLUSIONS : Compared with a subjective practice strategy, an objective risk-based strategy using prediction software may be associated with superior results in optimizing SSI rates and costs after vascular surgery.

Chang Bora, Sun Zhifei, Peiris Prabath, Huang Erich S, Benrashid Ehsan, Dillavou Ellen D

2020-Mar-17

Closed incision negative pressure therapy, High risk vascular patients, Infrainguinal vascular surgery, Prediction model, Surgical site infection

General General

An intelligent system based on optimized ANFIS and association rules for power transformer fault diagnosis.

In ISA transactions

This research work put forward an intelligent method for diagnosis and classification of power transformers faults based on the instructive Dissolved Gas Analysis Method (DGAM) attributes and machine learning algorithms. In the proposed method, 14 attributes obtained through DGAM are utilized as the initial and unprocessed inputs of Adaptive Neuro-Fuzzy Inference System (ANFIS). In this method, attribute selection and improved learning algorithm are utilized to enhance fault detection and recognition precision. In the propounded fault detection and classification method, the most instructive attributes obtained by DGAM are selected by association rules learning technique (ARLT). Using efficient enlightening attributes and eliminating tautological attributes lead to higher accuracy and superior operation. Furthermore, appropriate training of ANFIS has significant effect on its precision and robustness. Therefore, Black Widow Optimization Algorithm (BWOA) is applied to train the ANFIS. Having excellent exploration and extraction capability, fast convergence speed and simplicity is the main reason for choosing the BWOA as the learning algorithm. Two industrial datasets are utilized to test and evaluate the performance of the put forward method. The results show that the propounded diagnosis system has high accuracy, robust performance and short run time. Selecting the most educative attributes of DGAM, training ANFIS optimally, improving the robustness of ANFIS and increasing the classification accuracy are the main contribution of this paper in the field of power transformer fault detection and classification.

Tightiz Lilia, Nasab Morteza Azimi, Yang Hyosik, Addeh Abdoljalil

2020-Mar-14

ANFIS, Association rules, BWOA, DGAM, Power transformer

oncology Oncology

Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data.

In BMC bioinformatics

BACKGROUND : The ability to confidently predict health outcomes from gene expression would catalyze a revolution in molecular diagnostics. Yet, the goal of developing actionable, robust, and reproducible predictive signatures of phenotypes such as clinical outcome has not been attained in almost any disease area. Here, we report a comprehensive analysis spanning prediction tasks from ulcerative colitis, atopic dermatitis, diabetes, to many cancer subtypes for a total of 24 binary and multiclass prediction problems and 26 survival analysis tasks. We systematically investigate the influence of gene subsets, normalization methods and prediction algorithms. Crucially, we also explore the novel use of deep representation learning methods on large transcriptomics compendia, such as GTEx and TCGA, to boost the performance of state-of-the-art methods. The resources and findings in this work should serve as both an up-to-date reference on attainable performance, and as a benchmarking resource for further research.

RESULTS : Approaches that combine large numbers of genes outperformed single gene methods consistently and with a significant margin, but neither unsupervised nor semi-supervised representation learning techniques yielded consistent improvements in out-of-sample performance across datasets. Our findings suggest that using l2-regularized regression methods applied to centered log-ratio transformed transcript abundances provide the best predictive analyses overall.

CONCLUSIONS : Transcriptomics-based phenotype prediction benefits from proper normalization techniques and state-of-the-art regularized regression approaches. In our view, breakthrough performance is likely contingent on factors which are independent of normalization and general modeling techniques; these factors might include reduction of systematic errors in sequencing data, incorporation of other data types such as single-cell sequencing and proteomics, and improved use of prior knowledge.

Smith Aaron M, Walsh Jonathan R, Long John, Davis Craig B, Henstock Peter, Hodge Martin R, Maciejewski Mateusz, Mu Xinmeng Jasmine, Ra Stephen, Zhao Shanrong, Ziemek Daniel, Fisher Charles K

2020-Mar-20

Deep learning, Machine learning, Molecular diagnostics, Normalization methods, Phenotype prediction, RNA-seq, Representation learning, Transcriptomics

General General

Recent Trends and Future Direction of Dental Research in the Digital Era.

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

The digital transformation in dental medicine, based on electronic health data information, is recognized as one of the major game-changers of the 21st century to tackle present and upcoming challenges in dental and oral healthcare. This opinion letter focuses on the estimated top five trends and innovations of this new digital era, with potential to decisively influence the direction of dental research: (1) rapid prototyping (RP), (2) augmented and virtual reality (AR/VR), (3) artificial intelligence (AI) and machine learning (ML), (4) personalized (dental) medicine, and (5) tele-healthcare. Digital dentistry requires managing expectations pragmatically and ensuring transparency for all stakeholders: patients, healthcare providers, university and research institutions, the medtech industry, insurance, public media, and state policy. It should not be claimed or implied that digital smart data technologies will replace humans providing dental expertise and the capacity for patient empathy. The dental team that controls digital applications remains the key and will continue to play the central role in treating patients. In this context, the latest trend word is created: augmented intelligence, e.g., the meaningful combination of digital applications paired with human qualities and abilities in order to achieve improved dental and oral healthcare, ensuring quality of life.

Joda Tim, Bornstein Michael M, Jung Ronald E, Ferrari Marco, Waltimo Tuomas, Zitzmann Nicola U

2020-Mar-18

artificial intelligence (AI), augmented and virtual reality (AR/VR), digital transformation, machine learning (ML), patient-centered outcomes, personalized dental medicine, rapid prototyping, tele-health

General General

Large-Scale Screening of COVID-19 from Community Acquired Pneumonia using Infection Size-Aware Classification

ArXiv Preprint

The worldwide spread of coronavirus disease (COVID-19) has become a threatening risk for global public health. It is of great importance to rapidly and accurately screen patients with COVID-19 from community acquired pneumonia (CAP). In this study, a total of 1658 patients with COVID-19 and 1027 patients of CAP underwent thin-section CT. All images were preprocessed to obtain the segmentations of both infections and lung fields, which were used to extract location-specific features. An infection Size Aware Random Forest method (iSARF) was proposed, in which subjects were automated categorized into groups with different ranges of infected lesion sizes, followed by random forests in each group for classification. Experimental results show that the proposed method yielded sensitivity of 0.907, specificity of 0.833, and accuracy of 0.879 under five-fold cross-validation. Large performance margins against comparison methods were achieved especially for the cases with infection size in the medium range, from 0.01% to 10%. The further inclusion of Radiomics features show slightly improvement. It is anticipated that our proposed framework could assist clinical decision making.

Feng Shi, Liming Xia, Fei Shan, Dijia Wu, Ying Wei, Huan Yuan, Huiting Jiang, Yaozong Gao, He Sui, Dinggang Shen

2020-03-22

General General

A taxonomy-free approach based on machine learning to assess the quality of rivers with diatoms.

In The Science of the total environment

Diatoms are a compulsory biological quality element in the ecological assessment of rivers according to the Water Framework Directive. The application of current official indices requires the identification of individuals to species or lower rank under a microscope based on the valve morphology. This is a highly time-consuming task, often susceptible of disagreements among analysts. In alternative, the use of DNA metabarcoding combined with High-Throughput Sequencing (HTS) has been proposed. The sequences obtained from environmental DNA are clustered into Operational Taxonomic Units (OTUs), which can be assigned to a taxon using reference databases, and from there calculate biotic indices. However, there is still a high percentage of unassigned OTUs to species due to the incompleteness of reference libraries. Alternatively, we tested a new taxonomy-free approach based on diatom community samples to assess rivers. A combination of three machine learning techniques is used to build models that predict diatom OTUs expected in test sites, under reference conditions, from environmental data. The Observed/Expected OTUs ratio indicates the deviation from reference condition and is converted into a quality class. This approach was never used with diatoms neither with OTUs data. To evaluate its efficiency, we built a model based on OTUs lists (HYDGEN) and another based on taxa lists from morphological identification (HYDMORPH), and also calculated a biotic index (IPS). The models were trained and tested with data from 81 sites (44 reference sites) from central Portugal. Both models were considered accurate (linear regression for Observed and Expected richness: R2 ≈ 0.7, interception ≈ 0.8) and sensitive to global anthropogenic disturbance (Rs2 > 0.30 p < 0.006 for global disturbance). Yet, the HYDGEN model based on molecular data was sensitive to more types of pressures (such as, changes in land use and habitat quality), which gives promising insights to its use for bioassessment of rivers.

Feio Maria João, Serra Sónia R Q, Mortágua Andreia, Bouchez Agnès, Rimet Frédéric, Vasselon Valentin, Almeida Salomé F P

2020-Mar-12

Bioassessment, HYDRA, Machine learning, Metabarcoding, OTUs, Rivers

General General

Application of learning vector quantization and different machine learning techniques to assessing forest fire influence factors and spatial modelling.

In Environmental research ; h5-index 67.0

This study assesses forest-fire susceptibility (FFS) in Fars Province, Iran using three geographic information system (GIS)-based machine-learning algorithms: boosted regression tree (BRT), general linear model (GLM), and mixture discriminant analysis (MDA). Recently, BRT, GLM, and MDA have become important machine-learning algorithms and their use has been enriched by application to various fields of research. A database of historical FFs identified using Landsat-8 OLI and MODIS satellite images (at 358 locations) and ten influencing factors (elevation, slope, topographical wetness index, aspect, distance from urban areas, annual mean temperature, land use, distance from road, annual mean rainfall, and distance from river) were input into a GIS. The 358 sites were divided into two sets for training (70%) and validation (30%). BRT, GLM, and MDA models were used to analyze the spatial relationships between the factors influencing FFs and the locations of fires to generate an FFS map. The prediction success of each modelled FFS map was determined with the help of the ROC curve, accuracy, overall accuracy, True-skill statistic (TSS), F-measures, corrected classify instances (CCI), and K-fold cross-validation (4-fold). The accuracy results of training and validation dataset in the BRT (AUC = 88.90% and 88.2%) and MDA (AUC = 86.4% and 85.6%) models are more effective than the GLM (AUC = 86.6% and 82.5%) model. Also, the outcome of the 4-fold measure confirmed the results from the other accuracy measures. Therefore, the accuracies of the BRT and MDA models are satisfactory and are suitable for FFS mapping in Fars Province. Finally, the well-accepted neural network application of learning-vector quantization (LVQ) reveals that land use, annual mean rainfall, and slope angle were the most useful determinants of FFS. The resulting FFS maps can enhance the effectiveness of planning and management of forest resources and ecological balances in this province.

Pourghasemi Hamid Reza, Gayen Amiya, Lasaponara Rosa, Tiefenbacher John P

2020-Feb-29

Boosted regression trees, Forest-fire susceptibility map, Generalized linear model, Learning-vector quantization, Mixture discriminant analysis

General General

Metabolic pathway engineering: Perspectives and applications.

In Computer methods and programs in biomedicine

BACKGROUND : Metabolic engineering aims at contriving microbes as biocatalysts for enhanced and cost-effective production of countless secondary metabolites. These secondary metabolites can be treated as the resources of industrial chemicals, pharmaceuticals and fuels. Plants are also crucial targets for metabolic engineers to produce necessary secondary metabolites. Metabolic engineering of both microorganism and plants also contributes towards drug discovery. In order to implement advanced metabolic engineering techniques efficiently, metabolic engineers should have detailed knowledge about cell physiology and metabolism. Principle behind methodologies: Genome-scale mathematical models of integrated metabolic, signal transduction, gene regulatory and protein-protein interaction networks along with experimental validation can provide such knowledge in this context. Incorporation of omics data into these models is crucial in the case of drug discovery. Inverse metabolic engineering and metabolic control analysis (MCA) can help in developing such models. Artificial intelligence methodology can also be applied for efficient and accurate metabolic engineering.

CONCLUSION : In this review, we discuss, at the beginning, the perspectives of metabolic engineering and its application on microorganism and plant leading to drug discovery. At the end, we elaborate why inverse metabolic engineering and MCA are closely related to modern metabolic engineering. In addition, some crucial steps ensuring efficient and optimal metabolic engineering strategies have been discussed. Moreover, we explore the use of genomics data for the activation of silent metabolic clusters and how it can be integrated with metabolic engineering. Finally, we exhibit a few applications of artificial intelligence to metabolic engineering.

Dasgupta Abhijit, Chowdhury Nirmalya, De Rajat K

2020-Mar-18

Artificial intelligence,, CRISPR-Cas9, Drug discovery, Genomics, Inverse metabolic engineering, MCA, Secondary metabolites, TALENs, ZFNs

Radiology Radiology

Resting-state EEG topographies: Reliable and sensitive signatures of unilateral spatial neglect.

In NeuroImage. Clinical

Theoretical advances in the neurosciences are leading to the development of an increasing number of proposed interventions for the enhancement of functional recovery after brain damage. Integration of these novel approaches in clinical practice depends on the availability of reliable, simple, and sensitive biomarkers of impairment level and extent of recovery, to enable an informed clinical-decision process. However, the neuropsychological tests currently in use do not tap into the complex neural re-organization process that occurs after brain insult and its modulation by treatment. Here we show that topographical analysis of resting-state electroencephalography (rsEEG) patterns using singular value decomposition (SVD) could be used to capture these processes. In two groups of subacute stroke patients, we show reliable detection of deviant neurophysiological patterns over repeated measurement sessions on separate days. These patterns generalized across patients groups. Additionally, they maintained a significant association with ipsilesional attention bias, discriminating patients with spatial neglect of different severity levels. The sensitivity and reliability of these rsEEG topographical analyses support their use as a tool for monitoring natural and treatment-induced recovery in the rehabilitation process.

Pirondini Elvira, Goldshuv-Ezra Nurit, Zinger Nofya, Britz Juliane, Soroker Nachum, Deouell Leon Y, Ville Dimitri Van De

2020-Mar-05

Computer-enhanced measurement, EEG analysis, EEG topography features, Machine learning, Outcome measurement, Rehabilitation, Resting-state EEG biomarkers, Stroke, Unilateral spatial neglect

General General

Cavitation characteristics of flowing low and high boiling-point perfluorocarbon phase-shift nanodroplets during focused ultrasound exposures.

In Ultrasonics sonochemistry

This work investigated and compared the dynamic cavitation characteristics between low and high boiling-point phase-shift nanodroplets (NDs) under physiologically relevant flow conditions during focused ultrasound (FUS) exposures at different peak rarefactional pressures. A passive cavitation detection (PCD) system was used to monitor cavitation activity during FUS exposure at various acoustic pressure levels. Root mean square (RMS) amplitudes of broadband noise, spectrograms of the passive cavitation detection signals, and normalized inertial cavitation dose (ICD) values were calculated. Cavitation activity of low-boiling-point perfluoropentane (PFP) NDs and high boiling-point perfluorohexane (PFH) NDs flowing at in vitro mean velocities of 0-15 cm/s were compared in a 4-mm diameter wall-less vessel in a transparent tissue-mimicking phantom. In the static state, both types of phase-shift NDs exhibit a sharp rise in cavitation intensity during initial FUS exposure. Under flow conditions, cavitation activity of the PFH NDs reached the steady state less rapidly compared to PFP NDs under the lower acoustic pressure (1.35 MPa); at the higher acoustic pressure (1.65 MPa), the RMS amplitude increased more sharply during the initial FUS exposure period. In particular, the RMS-time curves of the PFP NDs shifted upward as the mean flow velocity increased from 0 to 15 cm/s; the RMS amplitude of the PFH ND solution increased from 0 to 10 cm/s and decreased at 15 cm/s. Moreover, amplitudes of the echo signal for the low boiling-point PFP NDs were higher compared to the high boiling-point PFH NDs in the lower frequency range, whereas the inverse occurred in the higher frequency range. Both PFP and PFH NDs showed increased cavitation activity in the higher frequency under the flow condition compared to the static state, especially PFH NDs. At 1.65 MPa, normalized ICD values for PFH increased from 0.93 ± 0.03 to 0.96 ± 0.04 and from 0 to 10 cm/s, then decreased to 0.86 ± 0.05 at 15 cm/s. This work contributes to our further understanding of cavitation characteristics of phase-shift NDs under physiologically relevant flow conditions during FUS exposure. In addition, the results provide a reference for selecting suitable phase-shift NDs to enhance the efficiency of cavitation-mediated ultrasonic applications.

Xu Tianqi, Cui Zhiwei, Li Dapeng, Cao Fangyuan, Xu Jichen, Zong Yujin, Wang Supin, Bouakaz Ayache, Wan Mingxi, Zhang Siyuan

2020-Mar-09

Boiling-point, Cavitation activity, Flowing phase-shift nanodroplets, Focused ultrasound

General General

Brain morphological changes in hypokinetic dysarthria of Parkinson's disease and use of machine learning to predict severity.

In CNS neuroscience & therapeutics

BACKGROUND : Up to 90% of patients with Parkinson's disease (PD) eventually develop the speech and voice disorder referred to as hypokinetic dysarthria (HD). However, the brain morphological changes associated with HD have not been investigated. Moreover, no reliable model for predicting the severity of HD based on neuroimaging has yet been developed.

METHODS : A total of 134 PD patients were included in this study and divided into a training set and a test set. All participants underwent a structural magnetic resonance imaging (MRI) scan and neuropsychological evaluation. Individual cortical thickness, subcortical structure, and white matter volume were extracted, and their association with HD severity was analyzed. After feature selection, a machine-learning model was established using a support vector machine in the training set. The severity of HD was then predicted in the test set.

RESULTS : Atrophy of the right precentral cortex and the right fusiform gyrus was significantly associated with HD. No association was found between HD and volume of white matter or subcortical structures. Favorable and optimal performance of machine learning on HD severity prediction was achieved using feature selection, giving a correlation coefficient (r) of .7516 and a coefficient of determination (R2 ) of .5649 (P < .001).

CONCLUSION : The brain morphological changes were associated with HD. Excellent prediction of the severity of HD was achieved using machine learning based on neuroimaging.

Chen Yingchuan, Zhu Guanyu, Liu Defeng, Liu Yuye, Yuan Tianshuo, Zhang Xin, Jiang Yin, Du Tingting, Zhang Jianguo

2020-Mar-20

“Parkinsons disease”, brain morphology, hypokinetic dysarthria, machine learning, structural magnetic resonance imaging

Radiology Radiology

Development and Validation of an Electronic Medical Record Algorithm to Identify Phenotypes of Rotator Cuff Tear.

In PM & R : the journal of injury, function, and rehabilitation

BACKGROUND : A lack of studies with large sample sizes of patients with rotator cuff tears is a barrier to performing clinical and genomic research.

OBJECTIVE : To develop and validate an electronic medical record (EMR)-based algorithm to identify individuals with and without rotator cuff tear.

DESIGN : We used a de-identified version of the EMR of more than 2 million subjects. A screening algorithm was applied to classify subjects into likely rotator cuff tear and likely normal rotator cuff groups. From these subjects, 500 likely rotator cuff tear and 500 likely normal rotator cuff were randomly chosen for algorithm development. Chart review of all 1000 subjects confirmed the true phenotype of rotator cuff tear or normal rotator cuff based on magnetic resonance imaging and operative report. An algorithm was then developed based on logistic regression and validation of the algorithm was performed.

RESULTS : The variables significantly predicting rotator cuff tear included the number of times a Current Procedural Terminology code related to rotator cuff procedures was used (OR = 3.3; 95% CI: 1.6-6.8 for ≥3 vs 0), the number of times a term related to rotator cuff lesions occurred in radiology reports (OR = 2.2; 95% CI: 1.2-4.1 for ≥1 vs 0), and the number of times a term related to rotator cuff lesions occurred in physician notes (OR = 4.5; 95% CI: 2.2-9.1 for 1 or 2 times vs 0). This phenotyping algorithm had a specificity of 0.89 (95% CI: 0.79-0.95) for rotator cuff tear, AUC of 0.842, and diagnostic likelihood ratios, DLR+ and DLR- of 5.94 (95%CI: 3.07-11.48) and 0.363 (95%CI: 0.291-0.453).

CONCLUSION : Our informatics algorithm enables identification of cohorts of individuals with and without rotator cuff tear from an EMR-based dataset with moderate accuracy. This article is protected by copyright. All rights reserved.

Gao Chan, Fan Run, Ayers Gregory D, Giri Ayush, Harris Kindred, Atreya Ravi, Teixeira Pedro L, Jain Nitin B

2020-Mar-20

algorithm, electronic medical record, logistic regression, machine learning, phenotyping, rotator cuff tear

Cardiology Cardiology

Personalized Interventions: A Reality in the Next 20 Years or Pie in the Sky.

In Pediatric cardiology

There is no better representation of the need for personalization of care than the breadth and complexity of congenital heart disease. Advanced imaging modalities are now standard of care in the field, and the advancements being made to three-dimensional visualization technologies are growing as a means of pre-procedural preparation. Incorporating emerging modeling approaches, such as computational fluid dynamics, will push the limits of our ability to predict outcomes, and this information may be both obtained and utilized during a single procedure in the future. Artificial intelligence and customized devices may soon surface as realistic tools for the care of patients with congenital heart disease, as they are showing growing evidence of feasibility within other fields. This review illustrates the great strides that have been made and the persistent challenges that exist within the field of congenital interventional cardiology, a field which must continue to innovate and push the limits to achieve personalization of the interventions it provides.

Salavitabar Arash, Armstrong Aimee K

2020-Mar-20

3D rotational angiography, 3D visualization, Computational fluid dynamics, Mixed reality, Pediatric interventional cardiology, Personalized interventions

Cardiology Cardiology

Decision-Making in the Catheter Laboratory: The Most Important Variable in Successful Outcomes.

In Pediatric cardiology

Increasingly the importance of how and why we make decisions in the medical arena has been questioned. Traditionally the aeronautical and business worlds have shed a light on this complex area of human decision-making. In this review we reflect on what we already know about the complexity of decision-making in addition to directing particular focus on the challenges to decision-making in the high-intensity environment of the pediatric cardiac catheterization laboratory. We propose that the most critical factor in outcomes for children in the catheterization lab may not be technical failures but rather human factors and the lack of preparation and robust shared decision-making process between the catheterization team. Key technical factors involved in the decision-making process include understanding the anatomy, the indications and objective to be achieved, equipment availability, procedural flow, having a back-up plan and post-procedural care plan. Increased awareness, pre-catheterization planning, use of standardized clinical assessment and management plans and artificial intelligence may provide solutions to pitfalls in decision-making. Further research and efforts should be directed towards studying the impact of human factors in the cardiac catheterization laboratory as well as the broader medical environment.

Duignan Sophie, Walsh Kevin P, McMahon Colin J

2020-Mar-20

Cardiac, Catheterization, Decision-making, Human factors

General General

A multistep deep learning framework for the automated detection and segmentation of astrocytes in fluorescent images of brain tissue.

In Scientific reports ; h5-index 158.0

While astrocytes have been traditionally described as passive supportive cells, studies during the last decade have shown they are active players in many aspects of CNS physiology and function both in normal and disease states. However, the precise mechanisms regulating astrocytes function and interactions within the CNS are still poorly understood. This knowledge gap is due in large part to the limitations of current image analysis tools that cannot process astrocyte images efficiently and to the lack of methods capable of quantifying their complex morphological characteristics. To provide an unbiased and accurate framework for the quantitative analysis of fluorescent images of astrocytes, we introduce a new automated image processing pipeline whose main novelties include an innovative module for cell detection based on multiscale directional filters and a segmentation routine that leverages deep learning and sparse representations to reduce the need of training data and improve performance. Extensive numerical tests show that our method performs very competitively with respect to state-of-the-art methods also in challenging images where astrocytes are clustered together. Our code is released open source and freely available to the scientific community.

Kayasandik Cihan Bilge, Ru Wenjuan, Labate Demetrio

2020-Mar-20

General General

Star Trek, medicine, ethics, nanotechnology and nursing.

In Early human development

The first collection of Star Trek (ST) papers in this journal concentrated on the various doctors that appeared in ST in chronological screening order. This second collection will demonstrate that depictions of certain characters in ST harkens back to the high-ranking Nazis Adolf Eichmann and Josef Mengele, cautionary tales, lest we allow history to repeat itself and such atrocities to be relived. Artificial Intelligence (AI) is also explored as well as issues pertaining to AI in the medical field and ST's introduction of "ethical subroutines" that attempt to ensure that artificial beings (including doctors) are created with the machine analogues of a conscience. Nanotechnology in ST is also shown to be a potential field that can be applied not only for medical benefit but also with malevolent intentions. These narratives also serve as cautionary tales with regard to the potential unintended consequences of completely unfettered research. Finally, another paper will review the role of nurses in the Star Trek universe and will show that while nursing as a profession has striven for autonomy, in ST, the nurse continues to be overshadowed by the medical doctor. It is hoped that this collection of papers may help us to understand where it is that medicine may be heading and what are our best options are for averting problems, tragedy and outright disaster/s.

Grech Victor

2020-Mar-16

Surgery Surgery

Cluster analysis of nutritional factors associated with low muscle mass index in middle-aged and older adults.

In Clinical nutrition (Edinburgh, Scotland)

BACKGROUND & AIMS : Sarcopenia, or age-related muscle loss, is an enormous health problem in an aging world because of its many clinical and societal adverse effects. The uncovering of healthy dietary patterns is an important strategy to prevent or delay sarcopenia. We used K-means clustering to identify subgroups of men and women based on nutritional and health-related factors and investigated risk factors for low muscle mass in the subgroups and in the study population as a whole.

METHODS : We analyzed a total 10,863 participants over 40 years of age who participated in the Korea National Health and Nutrition Survey from 2008 to 2011. Dual energy X-ray absorptiometry was used to determine the appendicular lean mass (ALM) of the participants. Participants with low ALM adjusted BMI (ALM/BMI) were then identified using the criteria of the Foundation for the National Institutes of Health sarcopenia project. K-means clustering and multivariate logistic regression were used to analyze associations between nutritional and health-related variables and low ALM/BMI in the population as a whole and in the individual clusters.

RESULTS : A total of 712 (15.8%) men and 869 (13.7%) women had low ALM/BMI. Five clusters were identified in men and women, respectively. Two clusters of men and one cluster of women exhibited an increased risk of low ALM/BMI. Old age, low total energy intake, low levels of physical activity, and a high number of chronic diseases were consistent risk factors for low ALM/BMI in all Korean men and women. Low protein was a common risk factor for low ALM/BMI in men. After dividing all subjects by the K-means clustering algorithm, two risk factors (high fat intake and smoking) and four factors (low intakes of carbohydrate, protein and fat, and high alcohol consumption) were additionally proposed in Korean men and women, respectively.

CONCLUSIONS : Age, low total energy intake, low level of physical activity, and an increased number of chronic diseases were consistent risk factors for low ALM/BMI in men and women. Cluster-specific risk factors were also noted in men and women.

Kwon Yu-Jin, Kim Hyoung Sik, Jung Dong-Hyuk, Kim Jong-Koo

2020-Mar-06

Aging, Nutrition, Sarcopenia, Unsupervised machine learning

General General

Multiple Plasma Biomarkers for Risk Stratification in Patients With Heart Failure and Preserved Ejection Fraction.

In Journal of the American College of Cardiology ; h5-index 167.0

BACKGROUND : Better risk stratification strategies are needed to enhance clinical care and trial design in heart failure with preserved ejection fraction (HFpEF).

OBJECTIVES : The purpose of this study was to assess the value of a targeted plasma multi-marker approach to enhance our phenotypic characterization and risk prediction in HFpEF.

METHODS : In this study, the authors measured 49 plasma biomarkers from TOPCAT (Treatment of Preserved Cardiac Function Heart Failure With an Aldosterone Antagonist) trial participants (n = 379) using a Multiplex assay. The relationship between biomarkers and the risk of all-cause death or heart failure-related hospital admission (DHFA) was assessed. A tree-based pipeline optimizer platform was used to generate a multimarker predictive model for DHFA. We validated the model in an independent cohort of HFpEF patients enrolled in the PHFS (Penn Heart Failure Study) (n = 156).

RESULTS : Two large, tightly related dominant biomarker clusters were found, which included biomarkers of fibrosis/tissue remodeling, inflammation, renal injury/dysfunction, and liver fibrosis. Other clusters were composed of neurohormonal regulators of mineral metabolism, intermediary metabolism, and biomarkers of myocardial injury. Multiple biomarkers predicted incident DHFA, including 2 biomarkers related to mineral metabolism/calcification (fibroblast growth factor-23 and OPG [osteoprotegerin]), 3 inflammatory biomarkers (tumor necrosis factor-alpha, sTNFRI [soluble tumor necrosis factor-receptor I], and interleukin-6), YKL-40 (related to liver injury and inflammation), 2 biomarkers related to intermediary metabolism and adipocyte biology (fatty acid binding protein-4 and growth differentiation factor-15), angiopoietin-2 (related to angiogenesis), matrix metalloproteinase-7 (related to extracellular matrix turnover), ST-2, and N-terminal pro-B-type natriuretic peptide. A machine-learning-derived model using a combination of biomarkers was strongly predictive of the risk of DHFA (standardized hazard ratio: 2.85; 95% confidence interval: 2.03 to 4.02; p < 0.0001) and markedly improved the risk prediction when added to the MAGGIC (Meta-Analysis Global Group in Chronic Heart Failure Risk Score) risk score. In an independent cohort (PHFS), the model strongly predicted the risk of DHFA (standardized hazard ratio: 2.74; 95% confidence interval: 1.93 to 3.90; p < 0.0001), which was also independent of the MAGGIC risk score.

CONCLUSIONS : Various novel circulating biomarkers in key pathophysiological domains are predictive of outcomes in HFpEF, and a multimarker approach coupled with machine-learning represents a promising strategy for enhancing risk stratification in HFpEF.

Chirinos Julio A, Orlenko Alena, Zhao Lei, Basso Michael D, Cvijic Mary Ellen, Li Zhuyin, Spires Thomas E, Yarde Melissa, Wang Zhaoqing, Seiffert Dietmar A, Prenner Stuart, Zamani Payman, Bhattacharya Priyanka, Kumar Anupam, Margulies Kenneth B, Car Bruce D, Gordon David A, Moore Jason H, Cappola Thomas P

2020-Mar-24

HFpEF, Penn Heart Failure Study, TOPCAT trial, biomarkers, fibrosis, inflammation, kidney, liver

Ophthalmology Ophthalmology

Classification of optical coherence tomography images using a capsule network.

In BMC ophthalmology

BACKGROUND : Classification of optical coherence tomography (OCT) images can be achieved with high accuracy using classical convolution neural networks (CNN), a commonly used deep learning network for computer-aided diagnosis. Classical CNN has often been criticized for suppressing positional relations in a pooling layer. Therefore, because capsule networks can learn positional information from images, we attempted application of a capsule network to OCT images to overcome that shortcoming. This study is our attempt to improve classification accuracy by replacing CNN with a capsule network.

METHODS : From an OCT dataset, we produced a training dataset of 83,484 images and a test dataset of 1000 images. For training, the dataset comprises 37,205 images with choroidal neovascularization (CNV), 11,348 with diabetic macular edema (DME), 8616 with drusen, and 26,315 normal images. The test dataset has 250 images from each category. The proposed model was constructed based on a capsule network for improving classification accuracy. It was trained using the training dataset. Subsequently, the test dataset was used to evaluate the trained model.

RESULTS : Classification of OCT images using our method achieved accuracy of 99.6%, which is 3.2 percentage points higher than that of other methods described in the literature.

CONCLUSION : The proposed method achieved classification accuracy results equivalent to those reported for other methods for CNV, DME, drusen, and normal images.

Tsuji Takumasa, Hirose Yuta, Fujimori Kohei, Hirose Takuya, Oyama Asuka, Saikawa Yusuke, Mimura Tatsuya, Shiraishi Kenshiro, Kobayashi Takenori, Mizota Atsushi, Kotoku Jun’ichi

2020-Mar-19

Capsule network, Choroidal neovascularization, Deep learning, Diabetic macular edema, Drusen, Optical coherence tomography

oncology Oncology

Biomarkers of neoadjuvant/adjuvant chemotherapy for breast cancer.

In Chinese clinical oncology

The improvement of tumor biomarkers prepared for clinical use is a long process. A good biomarker should predict not only prognosis but also the response to therapies. In this review, we describe the biomarkers of neoadjuvant/adjuvant chemotherapy for breast cancer, considering different breast cancer subtypes. In hormone receptor (HR)-positive/human epidermal growth factor 2 (HER2)-negative breast cancers, various genomic markers highly associated with proliferation have been tested. Among them, only two genomic signatures, the 21-gene recurrence score and 70-gene signature, have been reported in prospective randomized clinical trials and met the primary endpoint. However, these genomic markers did not suffice in HER2-positive and triple-negative (TN) breast cancers, which present only classical clinical and pathological information (tumor size, nodal or distant metastatic status) for decision making in the adjuvant setting in daily clinic. Recently, patients with residual invasive cancer after neoadjuvant chemotherapy are at a high-risk of recurrence for metastasis, which, in turn, make these patients best applicants for clinical trials. Two clinical trials have shown improved outcomes with post-operative capecitabine and ado-trastuzumab emtansine treatment in patients with either TN or HER2-positive breast cancer, respectively, who had residual disease after neoadjuvant chemotherapy. Furthermore, tumor-infiltrating lymphocytes (TILs) have been reported to have a predictive value for prognosis and response to chemotherapy from the retrospective analyses. So far, TILs have to not be used to either withhold or prescribe chemotherapy based on the absence of standardized evaluation guidelines and confirmed information. To overcome the low reproducibility of evaluations of TILs, gene signatures or digital image analysis and machine learning algorithms with artificial intelligence may be useful for standardization of assessment for TILs in the future.

Iwamoto Takayuki, Kajiwara Yukiko, Zhu Yidan, Iha Shigemichi

2020-Mar-13

Biomarker, breast cancer, chemotherapy, gene expression

General General

A Back-End, CMOS Compatible Ferroelectric Field Effect Transistor for Synaptic Weights.

In ACS applied materials & interfaces ; h5-index 147.0

Neuromorphic computing architectures enable the dense co-location of memory and processing elements within a single circuit. This co-location removes the communication bottleneck of transferring data between separate memory and computing units as in standard von Neuman architectures for data-critical applications including machine learning. The essential building blocks of neuromorphic systems are non-volatile synaptic elements such as a memristor. Key memristor properties include a suitable non-volatile resistance range, continuous linear resistance modulation and symmetric switching. In this work, we demonstrate voltage-controlled, symmetric and analog potentiation and depression of a ferroelectric Hf0.57Zr0.43O2 (HZO) field effect transistor (FeFET) with good linearity. Our FeFET operates with a low writing energy (fJ) and fast programming time (40 ns). Retention measurements have been done over 4-bits depth with low noise in the tungsten oxide (WOx) read out channel. By adjusting the channel thickness from 15 nm to 8 nm, the on/off ratio of the FeFET can be engineered from 1% to 200% with an on-resistance >100 kΩ, depending on the channel geometry. The device concept is using earth-abundant materials, and is compatible with a back end of line (BEOL) integration into complementary metal-oxide-semiconductor (CMOS) processes. It has therefore a great potential for the fabrication of high density, large-scale integrated arrays of artificial analog synapses.

Halter Mattia, Bégon-Lours Laura, Bragaglia Valeria, Sousa Marilyne, Offrein Bert, Abel Stefan, Luisier Mathieu, Fompeyrine Jean

2020-Mar-20

General General

Using federated data sources and Varian Learning Portal framework to train a neural network model for automatic organ segmentation.

In Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)

PURPOSE : In this study we trained a deep neural network model for female pelvis organ segmentation using data from several sites without any personal data sharing. The goal was to assess its prediction power compared with the model trained in a centralized manner.

METHODS : Varian Learning Portal (VLP) is a distributed machine learning (ML) infrastructure enabling privacy-preserving research across hospitals from different regions or countries, within the framework of a trusted consortium. Such a framework is relevant in the case when there is a high level of trust among the participating sites, but there are legal restrictions which do not allow the actual data sharing between them. We trained an organ segmentation model for the female pelvic region using the synchronous data distributed framework provided by the VLP.

RESULTS : The prediction performance of the model trained using the federated framework offered by VLP was on the same level as the performance of the model trained in a centralized manner where all training data was pulled together in one centre.

CONCLUSIONS : VLP infrastructure can be used for GPU-based training of a deep neural network for organ segmentation for the female pelvic region. This organ segmentation instance is particularly difficult due to the high variation in the organs' shape and size. Being able to train the model using data from several clinics can help, for instance, by exposing the model to a larger range of data variations. VLP framework enables such a distributed training approach without sharing protected health information.

Czeizler Elena, Wiessler Wolfgang, Koester Thorben, Hakala Mikko, Basiri Shahab, Jordan Petr, Kuusela Esa

2020-Mar-17

Convolutional Neural Network, Distributed Training, Federated Data Sources, Female Pelvis Organ Segmentation, Varian Learning Portal

General General

Coupling cellular automata with area partitioning and spatiotemporal convolution for dynamic land use change simulation.

In The Science of the total environment

Urbanization processes have accelerated over recent decades, prompting efforts to model land use change (LUC) patterns for decision support and urban planning. Cellular automata (CA) are extensively employed given their simplicity, flexibility, and intuitiveness when simulating dynamic LUC. Previous research, however, has ignored the spatial heterogeneity among sub-regions, instead applying the same transition rules across entire regions; moreover, most existing methods extract neighborhood effects with only one data time slice, which is inconsistent with the nature of neighborhood interactions as a long-term process exhibiting obvious spatiotemporal dependency. Accordingly, we propose a hybrid cellular automata model coupling area partitioning and spatiotemporal neighborhood features learning, named PST-CA. We use a machine-learning-based partitioning strategy, self-organizing map (SOM), to divide entire regions into several homogeneous sub-regions, and further apply a spatiotemporal three-dimensional convolutional neural network (3D CNN) to extract the spatiotemporal neighborhood features. An artificial neural network (ANN) is then built to create a conversion probability map for each sub-region using both spatiotemporal neighborhood features and factors that drive the LUC. Finally, the dynamic simulation results of entire study area are generated by fusing these probability maps, constraints and stochastic factors. Land use data collected from 2000 to 2015 in Shanghai were selected to verify our proposed method. Four traditional models were implemented for comparison, including logistic regression (LR)-CA, support vector machine (SVM)-CA, random forest (RF)-CA and conventional ANN-CA. Results illustrate that the proposed PST-CA outperformed four traditional models, with overall accuracy increased by 4.66%~6.41%. Moreover, three distinctly different "coverage rate-growth rate" composite patterns of built-up areas are shown in the SOM partitioning results, which verifies SOM's ability to address spatial heterogeneity; while the optimal time steps in 3D CNN generally maintained a positive correlation with the growth rate of built-up areas, which implies longer temporal dependency should be captured for rapidly developing areas.

Qian Yuehui, Xing Weiran, Guan Xuefeng, Yang Tingting, Wu Huayi

2020-Mar-12

Cellular automata, Land use, Neighborhood effects, Spatial heterogeneity, Spatiotemporal features

General General

Predicting individual clinical trajectories of depression with generative embedding.

In NeuroImage. Clinical

Patients with major depressive disorder (MDD) show heterogeneous treatment response and highly variable clinical trajectories: while some patients experience swift recovery, others show relapsing-remitting or chronic courses. Predicting individual clinical trajectories at an early stage is a key challenge for psychiatry and might facilitate individually tailored interventions. So far, however, reliable predictors at the single-patient level are absent. Here, we evaluated the utility of a machine learning strategy - generative embedding (GE) - which combines interpretable generative models with discriminative classifiers. Specifically, we used functional magnetic resonance imaging (fMRI) data of emotional face perception in 85 MDD patients from the NEtherlands Study of Depression and Anxiety (NESDA) who had been followed up over two years and classified into three subgroups with distinct clinical trajectories. Combining a generative model of effective (directed) connectivity with support vector machines (SVMs), we could predict whether a given patient would experience chronic depression vs. fast remission with a balanced accuracy of 79%. Gradual improvement vs. fast remission could still be predicted above-chance, but less convincingly, with a balanced accuracy of 61%. Generative embedding outperformed classification based on conventional (descriptive) features, such as functional connectivity or local activation estimates, which were obtained from the same data and did not allow for above-chance classification accuracy. Furthermore, predictive performance of GE could be assigned to a specific network property: the trial-by-trial modulation of connections by emotional content. Given the limited sample size of our study, the present results are preliminary but may serve as proof-of-concept, illustrating the potential of GE for obtaining clinical predictions that are interpretable in terms of network mechanisms. Our findings suggest that abnormal dynamic changes of connections involved in emotional face processing might be associated with higher risk of developing a less favorable clinical course.

Frässle Stefan, Marquand Andre F, Schmaal Lianne, Dinga Richard, Veltman Dick J, van der Wee Nic J A, van Tol Marie-José, Schöbi Dario, Penninx Brenda W J H, Stephan Klaas E

2020-Feb-17

General General

Machine Learning Driven Analysis of Large Scale Simulations Reveals Conformational Characteristics of Ubiquitin Chains.

In Journal of chemical theory and computation

Understanding the conformational characteristics of protein complexes in solution is crucial for a deeper insight in their biological function. Molecular dynamics simulations performed on high performance computing plants and with modern simulation techniques can be used to obtain large data sets that contain conformational and thermodynamic information about biomolecular systems. While this can in principle give a detailed picture of protein-protein interactions in solution and therefore complement experimental data, it also raises the challenge of processing exceedingly large high-dimensional data sets with several million samples. Here we present a novel method for the characterization of protein-protein interactions, which combines a neural network based dimensionality reduction technique to obtain a two-dimensional representation of the conformational space with a density based clustering algorithm for state detection and a metric which assesses the (dis)similarity between different conformational spaces. This method is highly scalable and therefore makes the analysis of massive data sets computationally tractable. We demonstrate the power of this approach to large scale data analysis by characterizing highly dynamic conformational phase spaces of differently linked ubiquitin (Ub) oligomers from coarse-grained simulations. We are able to extract a protein-protein interaction model for two unlinked Ub proteins which is then used to determine how the Ub-Ub interaction pattern is altered in Ub oligomers by the introduction of a covalent linkage. We find that the Ub chain conformational ensemble depends highly on the linkage type and for some cases also on the Ub chain length. By this, we obtain insight into the conformational characteristics of different Ub chains and how this may contribute to linkage type and chain length specific recognition.

Berg Andrej, Franke Leon, Scheffner Martin, Peter Christine

2020-Mar-20

Radiology Radiology

Accounting for data variability in multi-institutional distributed deep learning for medical imaging.

In Journal of the American Medical Informatics Association : JAMIA

OBJECTIVES : Sharing patient data across institutions to train generalizable deep learning models is challenging due to regulatory and technical hurdles. Distributed learning, where model weights are shared instead of patient data, presents an attractive alternative. Cyclical weight transfer (CWT) has recently been demonstrated as an effective distributed learning method for medical imaging with homogeneous data across institutions. In this study, we optimize CWT to overcome performance losses from variability in training sample sizes and label distributions across institutions.

MATERIALS AND METHODS : Optimizations included proportional local training iterations, cyclical learning rate, locally weighted minibatch sampling, and cyclically weighted loss. We evaluated our optimizations on simulated distributed diabetic retinopathy detection and chest radiograph classification.

RESULTS : Proportional local training iteration mitigated performance losses from sample size variability, achieving 98.6% of the accuracy attained by centrally hosting in the diabetic retinopathy dataset split with highest sample size variance across institutions. Locally weighted minibatch sampling and cyclically weighted loss both mitigated performance losses from label distribution variability, achieving 98.6% and 99.1%, respectively, of the accuracy attained by centrally hosting in the diabetic retinopathy dataset split with highest label distribution variability across institutions.

DISCUSSION : Our optimizations to CWT improve its capability of handling data variability across institutions. Compared to CWT without optimizations, CWT with optimizations achieved performance significantly closer to performance from centrally hosting.

CONCLUSION : Our work is the first to identify and address challenges of sample size and label distribution variability in simulated distributed deep learning for medical imaging. Future work is needed to address other sources of real-world data variability.

Balachandar Niranjan, Chang Ken, Kalpathy-Cramer Jayashree, Rubin Daniel L

2020-Mar-20

deep learning, distributed learning, federated learning, medical imaging, transfer learning

Radiology Radiology

Machine Learning and Deep Neural Networks Applications in Computed Tomography for Coronary Artery Disease and Myocardial Perfusion.

In Journal of thoracic imaging

During the latest years, artificial intelligence, and especially machine learning (ML), have experienced a growth in popularity due to their versatility and potential in solving complex problems. In fact, ML allows the efficient handling of big volumes of data, allowing to tackle issues that were unfeasible before, especially with deep learning, which utilizes multilayered neural networks. Cardiac computed tomography (CT) is also experiencing a rise in examination numbers, and ML might help handle the increasing derived information. Moreover, cardiac CT presents some fields wherein ML may be pivotal, such as coronary calcium scoring, CT angiography, and perfusion. In particular, the main applications of ML involve image preprocessing and postprocessing, and the development of risk assessment models based on imaging findings. Concerning image preprocessing, ML can help improve image quality by optimizing acquisition protocols or removing artifacts that may hinder image analysis and interpretation. ML in image postprocessing might help perform automatic segmentations and shorten examination processing times, also providing tools for tissue characterization, especially concerning plaques. The development of risk assessment models from ML using data from cardiac CT could aid in the stratification of patients who undergo cardiac CT in different risk classes and better tailor their treatment to individual conditions. While ML is a powerful tool with great potential, applications in the field of cardiac CT are still expanding, and not yet routinely available in clinical practice due to the need for extensive validation. Nevertheless, ML is expected to have a big impact on cardiac CT in the near future.

Monti Caterina B, Codari Marina, van Assen Marly, De Cecco Carlo N, Vliegenthart Rozemarijn

2020-Mar-17

Surgery Surgery

External Validation of PATHFx Version 3.0 in Patients Treated Surgically and Nonsurgically for Symptomatic Skeletal Metastases.

In Clinical orthopaedics and related research ; h5-index 71.0

BACKGROUND : PATHFx is a clinical decision-support tool based on machine learning capable of estimating the likelihood of survival after surgery for patients with skeletal metastases. The applicability of any machine-learning tool depends not only on successful external validation in unique patient populations but also on remaining relevant as more effective systemic treatments are introduced. With advancements in the treatment of metastatic disease, it is our responsibility to patients to ensure clinical support tools remain contemporary and accurate.

QUESTION/PURPOSES : Therefore, we sought to (1) generate updated PATHFx models using recent data from patients treated at one large, urban tertiary referral center and (2) externally validate the models using two contemporary patient populations treated either surgically or nonsurgically with external-beam radiotherapy alone for symptomatic skeletal metastases for symptomatic lesions.

METHODS : After obtaining institutional review board approval, we collected data on 208 patients undergoing surgical treatment for pathologic fractures at Memorial Sloan Kettering Cancer Center between 2015 and 2018. These data were combined with the original PATHFx training set (n = 189) to create the final training set (n = 397). We then created six Bayesian belief networks designed to estimate the likelihood of 1-month, 3-month, 6-month, 12-month, 18-month, and 24-month survival after treatment. Bayesian belief analysis is a statistical method that allows data-driven learning to arise from conditional probabilities by exploring relationships between variables to estimate the likelihood of an outcome using observed data. For external validation, we extracted the records of patients treated between 2016 and 2018 from the International Bone Metastasis Registry and records of patients treated nonoperatively with external-beam radiation therapy for symptomatic skeletal metastases from 2012 to 2016 using the Military Health System Data Repository (radiotherapy-only group). From each record, we collected the date of treatment, laboratory values at the time of treatment initiation, demographic data, details of diagnosis, and the date of death. All records reported sufficient follow-up to establish survival (yes/no) at 24-months after treatment. For external validation, we applied the data from each record to the new PATHFx models. We assessed calibration (calibration plots), accuracy (Brier score), discriminatory ability (area under the receiver operating characteristic curve [AUC]).

RESULTS : The updated PATHFx version 3.0 models successfully classified survival at each time interval in both external validation sets and demonstrated appropriate discriminatory ability and model calibration. The Bayesian models were reasonably calibrated to the Memorial Sloan Kettering Cancer Center training set. External validation with 197 records from the International Bone Metastasis Registry and 192 records from the Military Health System Data Repository for analysis found Brier scores that were all less than 0.20, with upper bounds of the 95% confidence intervals all less than 0.25, both for the radiotherapy-only and International Bone Metastasis Registry groups. Additionally, AUC estimates were all greater than 0.70, with lower bounds of the 95% CI all greater than 0.68, except for the 1-month radiotherapy-only group. To complete external validation, decision curve analysis demonstrated clinical utility. This means it was better to use the PATHFx models when compared to the default assumption that all or no patients would survive at all time periods except for the 1-month models. We believe the favorable Brier scores (< 0.20) as well as DCA indicate these models are suitable for clinical use.

CONCLUSIONS : We successfully updated PATHFx using contemporary data from patients undergoing either surgical or nonsurgical treatment for symptomatic skeletal metastases. These models have been incorporated for clinical use on PATHFx version 3.0 (https://www.pathfx.org). Clinically, external validation suggests it is better to use PATHFx version 3.0 for all time periods except when deciding whether to give radiotherapy to patients with the life expectancy of less than 1 month. This is partly because most patients survived 1-month after treatment. With the advancement of medical technology in treatment and diagnosis for patients with metastatic bone disease, part of our fiduciary responsibility is to the main current clinical support tools.

LEVEL OF EVIDENCE : Level III, therapeutic study.

Anderson Ashley B, Wedin Rikard, Fabbri Nicola, Boland Patrick, Healey John, Forsberg Jonathan A

2020-Apr

General General

Deep Generative Models for 3D Linker Design.

In Journal of chemical information and modeling

Rational compound design remains a challenging problem for both computational methods and medicinal chemists. Computational generative methods have begun to show promising results for the design problem. However, they have not yet used the power of 3D structural information. We have developed a novel graph-based deep generative model that combines state-of-the-art machine learning techniques with structural knowledge. Our method ("DeLinker") takes two fragments or partial structures and designs a molecule incorporating both. The generation process is protein context dependent, utilising the relative distance and orientation between the partial structures. This 3D information is vital to successful compound design, and we demonstrate its impact on the generation process and the limitations of omitting such information. In a large scale evaluation, DeLinker designed 60% more molecules with high 3D similarity to the original molecule than a database baseline. When considering the more relevant problem of longer linkers with at least five atoms, the outperformance increased to 200%. We demonstrate the effectiveness and applicability of this approach on a diverse range of design problems: fragment linking, scaffold hopping, and proteolysis targeting chimera (PROTAC) design. As far as we are aware, this is the first molecular generative model to incorporate 3D structural information directly in the design process. Code is available at https://github.com/oxpig/DeLinker.

Imrie Fergus, Bradley Anthony R, van der Schaar Mihaela, Deane Charlotte M

2020-Mar-20

General General

A multi-branch separable convolution neural network for pedestrian attribute recognition.

In Heliyon

Video surveillance applications have made great strides in making the world a safer place. Extracting visual attributes from a scene, such as the type of shoes, the type of clothing, carrying any object or not, or wearing any accessory etc., is a challenging problem and an efficient solution holds the key to a great number of applications. In this paper, we present a multi-branch convolutional neural network that uses depthwise separable convolution (DSC) layers to solve the pedestrian attribute recognition problem. Researchers have proposed various solutions over the years making use of convolutional neural networks (CNN), however, we introduce DSC layers to the CNN for the problem of pedestrian attribute recognition. In addition, we make a novel use of the different color spaces and create a 3-branch CNN, denoted as 3bCNN, that is efficient, especially with smaller datasets. We experiment on two benchmark datasets and show results with improvement over the state of the art.

Junejo Imran N, Ahmed Naveed

2020-Mar

Computer Vision, Computer science, Deep learning, Image processing, Pedestrian attribute recognition

General General

A comparison of Arabic sign language dynamic gesture recognition models.

In Heliyon

Arabic Sign Language (ArSL) is similar to other sign languages in terms of the way it is gestured and interpreted and used as a medium of communication among the hearing-impaired and the communities in which they live in. Research investigating sensor utilization and natural user interfaces to facilitate ArSL recognition and interpretation, is lacking. Previous research has demonstrated that there is not a single classifier modeling approach that can be suitable for all hand gesture recognition tasks, therefore, this research investigated which combination of algorithms, set with different parameters used with a sensor device, produce higher ArSL recognition accuracy results in a gesture recognition system. This research proposed a dynamic prototype model (DPM) using Kinect as a sensor to recognize certain ArSL gestured dynamic words. The DPM used eleven predictive models of three algorithms (SVM, RF, KNN) based on different parameter settings. Research findings indicated that highest recognition accuracy rates for the dynamic words gestured were achieved by the SVM models, with linear kernel and cost parameter = 0.035.

Almasre Miada A, Al-Nuaim Hana

2020-Mar

Arabic sign language, Classification, Computer science, Dynamic gesture recognition models, Machine learning

Public Health Public Health

Modeling the Research Landscapes of Artificial Intelligence Applications in Diabetes (GAPRESEARCH).

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

The rising prevalence and global burden of diabetes fortify the need for more comprehensive and effective management to prevent, monitor, and treat diabetes and its complications. Applying artificial intelligence in complimenting the diagnosis, management, and prediction of the diabetes trajectory has been increasingly common over the years. This study aims to illustrate an inclusive landscape of application of artificial intelligence in diabetes through a bibliographic analysis and offers future direction for research. Bibliometrics analysis was combined with exploratory factor analysis and latent Dirichlet allocation to uncover emergent research domains and topics related to artificial intelligence and diabetes. Data were extracted from the Web of Science Core Collection database. The results showed a rising trend in the number of papers and citations concerning AI applications in diabetes, especially since 2010. The nucleus driving the research and development of AI in diabetes is centered around developed countries, mainly consisting of the United States, which contributed 44.1% of the publications. Our analyses uncovered the top five emerging research domains to be: (i) use of artificial intelligence in diagnosis of diabetes, (ii) risk assessment of diabetes and its complications, (iii) role of artificial intelligence in novel treatments and monitoring in diabetes, (iv) application of telehealth and wearable technology in the daily management of diabetes, and (v) robotic surgical outcomes with diabetes as a comorbid. Despite the benefits of artificial intelligence, challenges with system accuracy, validity, and confidentiality breach will need to be tackled before being widely applied for patients' benefits.

Vu Giang Thu, Tran Bach Xuan, McIntyre Roger S, Pham Hai Quang, Phan Hai Thanh, Ha Giang Hai, Gwee Kenneth K, Latkin Carl A, Ho Roger C M, Ho Cyrus S H

2020-Mar-17

LDA, artificial intelligence, bibliometric, diabetes, machine learning

General General

Digital envirotyping: quantifying environmental determinants of health and behavior.

In NPJ digital medicine

Digital phenotyping efforts have used wearable devices to connect a rich array of physiologic data to health outcomes or behaviors of interest. The environmental context surrounding these phenomena has received less attention, yet is critically needed to understand their antecedents and deliver context-appropriate interventions. The coupling of improved smart eyewear with deep learning represents a technological turning point, one that calls for more comprehensive, ambitious study of environments and health.

Engelhard Matthew M, Oliver Jason A, McClernon F Joseph

2020

Cardiovascular diseases, Psychiatric disorders, Risk factors

Pathology Pathology

Pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence.

In NPJ digital medicine

The emergence of digital pathology has opened new horizons for histopathology. Artificial intelligence (AI) algorithms are able to operate on digitized slides to assist pathologists with different tasks. Whereas AI-involving classification and segmentation methods have obvious benefits for image analysis, image search represents a fundamental shift in computational pathology. Matching the pathology of new patients with already diagnosed and curated cases offers pathologists a new approach to improve diagnostic accuracy through visual inspection of similar cases and computational majority vote for consensus building. In this study, we report the results from searching the largest public repository (The Cancer Genome Atlas, TCGA) of whole-slide images from almost 11,000 patients. We successfully indexed and searched almost 30,000 high-resolution digitized slides constituting 16 terabytes of data comprised of 20 million 1000 × 1000 pixels image patches. The TCGA image database covers 25 anatomic sites and contains 32 cancer subtypes. High-performance storage and GPU power were employed for experimentation. The results were assessed with conservative "majority voting" to build consensus for subtype diagnosis through vertical search and demonstrated high accuracy values for both frozen section slides (e.g., bladder urothelial carcinoma 93%, kidney renal clear cell carcinoma 97%, and ovarian serous cystadenocarcinoma 99%) and permanent histopathology slides (e.g., prostate adenocarcinoma 98%, skin cutaneous melanoma 99%, and thymoma 100%). The key finding of this validation study was that computational consensus appears to be possible for rendering diagnoses if a sufficiently large number of searchable cases are available for each cancer subtype.

Kalra Shivam, Tizhoosh H R, Shah Sultaan, Choi Charles, Damaskinos Savvas, Safarpoor Amir, Shafiei Sobhan, Babaie Morteza, Diamandis Phedias, Campbell Clinton J V, Pantanowitz Liron

2020

Cancer imaging, Data mining, Machine learning

General General

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases.

In NPJ digital medicine

Autoimmune diseases are chronic, multifactorial conditions. Through machine learning (ML), a branch of the wider field of artificial intelligence, it is possible to extract patterns within patient data, and exploit these patterns to predict patient outcomes for improved clinical management. Here, we surveyed the use of ML methods to address clinical problems in autoimmune disease. A systematic review was conducted using MEDLINE, embase and computers and applied sciences complete databases. Relevant papers included "machine learning" or "artificial intelligence" and the autoimmune diseases search term(s) in their title, abstract or key words. Exclusion criteria: studies not written in English, no real human patient data included, publication prior to 2001, studies that were not peer reviewed, non-autoimmune disease comorbidity research and review papers. 169 (of 702) studies met the criteria for inclusion. Support vector machines and random forests were the most popular ML methods used. ML models using data on multiple sclerosis, rheumatoid arthritis and inflammatory bowel disease were most common. A small proportion of studies (7.7% or 13/169) combined different data types in the modelling process. Cross-validation, combined with a separate testing set for more robust model evaluation occurred in 8.3% of papers (14/169). The field may benefit from adopting a best practice of validation, cross-validation and independent testing of ML models. Many models achieved good predictive results in simple scenarios (e.g. classification of cases and controls). Progression to more complex predictive models may be achievable in future through integration of multiple data types.

Stafford I S, Kellermann M, Mossotto E, Beattie R M, MacArthur B D, Ennis S

2020

Autoimmune diseases, Machine learning, Predictive medicine

General General

Prediction of stress and drug craving ninety minutes in the future with passively collected GPS data.

In NPJ digital medicine

Just-in-time adaptive interventions (JITAIs), typically smartphone apps, learn to deliver therapeutic content when users need it. The challenge is to "push" content at algorithmically chosen moments without making users trigger it with effortful input. We trained a randomForest algorithm to predict heroin craving, cocaine craving, or stress (reported via smartphone app 3x/day) 90 min into the future, using 16 weeks of field data from 189 outpatients being treated for opioid-use disorder. We used only one form of continuous input (along with person-level demographic data), collected passively: an indicator of environmental exposures along the past 5 h of movement, as assessed by GPS. Our models achieved excellent overall accuracy-as high as 0.93 by the end of 16 weeks of tailoring-but this was driven mostly by correct predictions of absence. For predictions of presence, "believability" (positive predictive value, PPV) usually peaked in the high 0.70s toward the end of the 16 weeks. When the prediction target was more rare, PPV was lower. Our findings complement those of other investigators who use machine learning with more broadly based "digital phenotyping" inputs to predict or detect mental and behavioral events. When target events are comparatively subtle, like stress or drug craving, accurate detection or prediction probably needs effortful input from users, not passive monitoring alone. We discuss ways in which accuracy is difficult to achieve or even assess, and warn that high overall accuracy (including high specificity) can mask the abundance of false alarms that low PPV reveals.

Epstein David H, Tyburski Matthew, Kowalczyk William J, Burgess-Hull Albert J, Phillips Karran A, Curtis Brenda L, Preston Kenzie L

2020

Disease-free survival, Risk factors

General General

Deep Learning for Cardiac Image Segmentation: A Review.

In Frontiers in cardiovascular medicine

Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound and major anatomical structures of interest (ventricles, atria, and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research.

Chen Chen, Qin Chen, Qiu Huaqi, Tarroni Giacomo, Duan Jinming, Bai Wenjia, Rueckert Daniel

2020

CT, MRI, artificial intelligence, cardiac image analysis, cardiac image segmentation, deep learning, neural networks, ultrasound

General General

3D Tracking of Human Motion Using Visual Skeletonization and Stereoscopic Vision.

In Frontiers in bioengineering and biotechnology

The design of markerless systems to reconstruct human motion in a timely, unobtrusive and externally valid manner is still an open challenge. Artificial intelligence algorithms based on automatic landmarks identification on video images opened to a new approach, potentially e-viable with low-cost hardware. OpenPose is a library that t using a two-branch convolutional neural network allows for the recognition of skeletons in the scene. Although OpenPose-based solutions are spreading, their metrological performances relative to video setup are still largely unexplored. This paper aimed at validating a two-cameras OpenPose-based markerless system for gait analysis, considering its accuracy relative to three factors: cameras' relative distance, gait direction and video resolution. Two volunteers performed a walking test within a gait analysis laboratory. A marker-based optical motion capture system was taken as a reference. Procedures involved: calibration of the stereoscopic system; acquisition of video recordings, simultaneously with the reference marker-based system; video processing within OpenPose to extract the subject's skeleton; videos synchronization; triangulation of the skeletons in the two videos to obtain the 3D coordinates of the joints. Two set of parameters were considered for the accuracy assessment: errors in trajectory reconstruction and error in selected gait space-temporal parameters (step length, swing and stance time). The lowest error in trajectories (~20 mm) was obtained with cameras 1.8 m apart, highest resolution and straight gait, and the highest (~60 mm) with the 1.0 m, low resolution and diagonal gait configuration. The OpenPose-based system tended to underestimate step length of about 1.5 cm, while no systematic biases were found for swing/stance time. Step length significantly changed according to gait direction (p = 0.008), camera distance (p = 0.020), and resolution (p < 0.001). Among stance and swing times, the lowest errors (0.02 and 0.05 s for stance and swing, respectively) were obtained with the 1 m, highest resolution and straight gait configuration. These findings confirm the feasibility of tracking kinematics and gait parameters of a single subject in a 3D space using two low-cost webcams and the OpenPose engine. In particular, the maximization of cameras distance and video resolution enabled to achieve the highest metrological performances.

Zago Matteo, Luzzago Matteo, Marangoni Tommaso, De Cecco Mariolino, Tarabini Marco, Galli Manuela

2020

artificial intelligence, computer vision, gait analysis, markerless motion capture, movement measurement

General General

Interpreting Deep Learning Features for Myoelectric Control: A Comparison With Handcrafted Features.

In Frontiers in bioengineering and biotechnology

Existing research on myoelectric control systems primarily focuses on extracting discriminative characteristics of the electromyographic (EMG) signal by designing handcrafted features. Recently, however, deep learning techniques have been applied to the challenging task of EMG-based gesture recognition. The adoption of these techniques slowly shifts the focus from feature engineering to feature learning. Nevertheless, the black-box nature of deep learning makes it hard to understand the type of information learned by the network and how it relates to handcrafted features. Additionally, due to the high variability in EMG recordings between participants, deep features tend to generalize poorly across subjects using standard training methods. Consequently, this work introduces a new multi-domain learning algorithm, named ADANN (Adaptive Domain Adversarial Neural Network), which significantly enhances (p = 0.00004) inter-subject classification accuracy by an average of 19.40% compared to standard training. Using ADANN-generated features, this work provides the first topological data analysis of EMG-based gesture recognition for the characterization of the information encoded within a deep network, using handcrafted features as landmarks. This analysis reveals that handcrafted features and the learned features (in the earlier layers) both try to discriminate between all gestures, but do not encode the same information to do so. In the later layers, the learned features are inclined to instead adopt a one-vs.-all strategy for a given class. Furthermore, by using convolutional network visualization techniques, it is revealed that learned features actually tend to ignore the most activated channel during contraction, which is in stark contrast with the prevalence of handcrafted features designed to capture amplitude information. Overall, this work paves the way for hybrid feature sets by providing a clear guideline of complementary information encoded within learned and handcrafted features.

Côté-Allard Ulysse, Campbell Evan, Phinyomark Angkoon, Laviolette François, Gosselin Benoit, Scheme Erik

2020

CNN, ConvNet, EMG, Grad-CAM, MAPPER, deep learning, feature extraction, gesture recognition

Pathology Pathology

Whole slide images reflect DNA methylation patterns of human tumors.

In NPJ genomic medicine

DNA methylation is an important epigenetic mechanism regulating gene expression and its role in carcinogenesis has been extensively studied. High-throughput DNA methylation assays have been used broadly in cancer research. Histopathology images are commonly obtained in cancer treatment, given that tissue sampling remains the clinical gold-standard for diagnosis. In this work, we investigate the interaction between cancer histopathology images and DNA methylation profiles to provide a better understanding of tumor pathobiology at the epigenetic level. We demonstrate that classical machine learning algorithms can associate the DNA methylation profiles of cancer samples with morphometric features extracted from whole slide images. Furthermore, grouping the genes into methylation clusters greatly improves the performance of the models. The well-predicted genes are enriched in key pathways in carcinogenesis including hypoxia in glioma and angiogenesis in renal cell carcinoma. Our results provide new insights into the link between histopathological and molecular data.

Zheng Hong, Momeni Alexandre, Cedoz Pierre-Louis, Vogel Hannes, Gevaert Olivier

2020

CNS cancer, Cancer, Cancer imaging, Computational biology and bioinformatics

General General

Learning to synthesize: robust phase retrieval at low photon counts.

In Light, science & applications

The quality of inverse problem solutions obtained through deep learning is limited by the nature of the priors learned from examples presented during the training phase. Particularly in the case of quantitative phase retrieval, spatial frequencies that are underrepresented in the training database, most often at the high band, tend to be suppressed in the reconstruction. Ad hoc solutions have been proposed, such as pre-amplifying the high spatial frequencies in the examples; however, while that strategy improves the resolution, it also leads to high-frequency artefacts, as well as low-frequency distortions in the reconstructions. Here, we present a new approach that learns separately how to handle the two frequency bands, low and high, and learns how to synthesize these two bands into full-band reconstructions. We show that this "learning to synthesize" (LS) method yields phase reconstructions of high spatial resolution and without artefacts and that it is resilient to high-noise conditions, e.g., in the case of very low photon flux. In addition to the problem of quantitative phase retrieval, the LS method is applicable, in principle, to any inverse problem where the forward operator treats different frequency bands unevenly, i.e., is ill-posed.

Deng Mo, Li Shuai, Goy Alexandre, Kang Iksung, Barbastathis George

2020

Applied optics, Imaging and sensing

Radiology Radiology

Real-time colorectal cancer diagnosis using PR-OCT with deep learning.

In Theranostics

Prior reports have shown optical coherence tomography (OCT) can differentiate normal colonic mucosa from neoplasia, potentially offering an alternative technique to endoscopic biopsy - the current gold-standard colorectal cancer screening and surveillance modality. To help clinical translation limited by processing the large volume of generated data, we designed a deep learning-based pattern recognition (PR) OCT system that automates image processing and provides accurate diagnosis potentially in real-time. Method: OCT is an emerging imaging technique to obtain 3-dimensional (3D) "optical biopsies" of biological samples with high resolution. We designed a convolutional neural network to capture the structure patterns in human colon OCT images. The network is trained and tested using around 26,000 OCT images acquired from 20 tumor areas, 16 benign areas, and 6 other abnormal areas. Results: The trained network successfully detected patterns that identify normal and neoplastic colorectal tissue. Experimental diagnoses predicted by the PR-OCT system were compared to the known histologic findings and quantitatively evaluated. A sensitivity of 100% and specificity of 99.7% can be reached. Further, the area under the receiver operating characteristic (ROC) curves (AUC) of 0.998 is achieved. Conclusions: Our results demonstrate that PR-OCT can be used to give an accurate real-time computer-aided diagnosis of colonic neoplastic mucosa. Future development of this system as an "optical biopsy" tool to assist doctors in real-time for early mucosal neoplasms screening and treatment evaluation following initial oncologic therapy is planned.

Zeng Yifeng, Xu Shiqi, Chapman William C, Li Shuying, Alipour Zahra, Abdelal Heba, Chatterjee Deyali, Mutch Matthew, Zhu Quing

2020

colorectal cancer, deep learning, optical biopsy, optical coherence tomography (OCT)

General General

Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms.

In Plants (Basel, Switzerland)

Tea trees are kept in shaded locations to increase their chlorophyll content, which influences green tea quality. Therefore, monitoring change in chlorophyll content under low light conditions is important for managing tea trees and producing high-quality green tea. Hyperspectral remote sensing is one of the most frequently used methods for estimating chlorophyll content. Numerous studies based on data collected under relatively low-stress conditions and many hyperspectral indices and radiative transfer models show that shade-grown tea performs poorly. The performance of four machine learning algorithms-random forest, support vector machine, deep belief nets, and kernel-based extreme learning machine (KELM)-in evaluating data collected from tea leaves cultivated under different shade treatments was tested. KELM performed best with a root-mean-square error of 8.94 ± 3.05 μg cm-2 and performance to deviation values from 1.70 to 8.04 for the test data. These results suggest that a combination of hyperspectral reflectance and KELM has the potential to trace changes in the chlorophyll content of shaded tea leaves.

Sonobe Rei, Hirono Yuhei, Oi Ayako

2020-Mar-17

deep belief nets, extreme learning machine, first derivative spectra, random forest, shade-grown tea, support vector machine

General General

Baseline Brain Gray Matter Volume as a Predictor of Acupuncture Outcome in Treating Migraine.

In Frontiers in neurology

Background: The present study aimed to investigate the use of imaging biomarkers to predict the outcome of acupuncture in patients with migraine without aura (MwoA). Methods: Forty-one patients with MwoA received 4 weeks of acupuncture treatment and two brain imaging sessions at the Beijing Traditional Chinese Medicine Hospital affiliated with Capital Medical University. Patients kept a headache diary for 4 weeks before treatment and during acupuncture treatment. Responders were defined as those with at least a 50% reduction in the number of migraine days. The machine learning method was used to distinguish responders from non-responders based on pre-treatment brain gray matter (GM) volume. Longitudinal changes in GM predictive regions were also analyzed. Results: After 4 weeks of acupuncture, 19 patients were classified as responders. Based on 10-fold cross-validation for the selection of GM features, the linear support vector machine produced a classification model with 73% sensitivity, 85% specificity, and 83% accuracy. The area under the receiver operating characteristic curve was 0.7871. This classification model included 10 GM areas that were mainly distributed in the frontal, temporal, parietal, precuneus, and cuneus gyri. The reduction in the number of migraine days was correlated with baseline GM volume in the cuneus, parietal, and frontal gyri in all patients. Moreover, the left cuneus showed a longitudinal increase in GM volume in responders. Conclusion: The results suggest that pre-treatment brain structure could be a novel predictor of the outcome of acupuncture in the treatment of MwoA. Imaging features could be a useful tool for the prediction of acupuncture efficacy, which would enable the development of a personalized medicine strategy.

Yang Xue-Juan, Liu Lu, Xu Zi-Liang, Zhang Ya-Jie, Liu Da-Peng, Fishers Marc, Zhang Lan, Sun Jin-Bo, Liu Peng, Zeng Xiao, Wang Lin-Peng, Qin Wei

2020

acupuncture, gray matter, machine learning, migraine, prediction

General General

BrainOS: A Novel Artificial Brain-Alike Automatic Machine Learning Framework.

In Frontiers in computational neuroscience

Human intelligence is constituted by a multitude of cognitive functions activated either directly or indirectly by external stimuli of various kinds. Computational approaches to the cognitive sciences and to neuroscience are partly premised on the idea that computational simulations of such cognitive functions and brain operations suspected to correspond to them can help to further uncover knowledge about those functions and operations, specifically, how they might work together. These approaches are also partly premised on the idea that empirical neuroscience research, whether following on from such a simulation (as indeed simulation and empirical research are complementary) or otherwise, could help us build better artificially intelligent systems. This is based on the assumption that principles by which the brain seemingly operate, to the extent that it can be understood as computational, should at least be tested as principles for the operation of artificial systems. This paper explores some of the principles of the brain that seem to be responsible for its autonomous, problem-adaptive nature. The brain operating system (BrainOS) explicated here is an introduction to ongoing work aiming to create a robust, integrated model, combining the connectionist paradigm underlying neural networks and the symbolic paradigm underlying much else of AI. BrainOS is an automatic approach that selects the most appropriate model based on the (a) input at hand, (b) prior experience (a history of results of prior problem solving attempts), and (c) world knowledge (represented in the symbolic way and used as a means to explain its approach). It is able to accept diverse and mixed input data types, process histories and objectives, extract knowledge and infer a situational context. BrainOS is designed to be efficient through its ability to not only choose the most suitable learning model but to effectively calibrate it based on the task at hand.

Howard Newton, Chouikhi Naima, Adeel Ahsan, Dial Katelyn, Howard Adam, Hussain Amir

2020

BrainOS, architecture design, artificial intelligence, automatic machine learning, human brain, hyperparameters

General General

Performance Improvement of Near-Infrared Spectroscopy-Based Brain-Computer Interface Using Regularized Linear Discriminant Analysis Ensemble Classifier Based on Bootstrap Aggregating.

In Frontiers in neuroscience ; h5-index 72.0

Ensemble classifiers have been proven to result in better classification accuracy than that of a single strong learner in many machine learning studies. Although many studies on electroencephalography-brain-computer interface (BCI) used ensemble classifiers to enhance the BCI performance, ensemble classifiers have hardly been employed for near-infrared spectroscopy (NIRS)-BCIs. In addition, since there has not been any systematic and comparative study, the efficacy of ensemble classifiers for NIRS-BCIs remains unknown. In this study, four NIRS-BCI datasets were employed to evaluate the efficacy of linear discriminant analysis ensemble classifiers based on the bootstrap aggregating. From the analysis results, significant (or marginally significant) increases in the bitrate as well as the classification accuracy were found for all four NIRS-BCI datasets employed in this study. Moreover, significant bitrate improvements were found in two of the four datasets.

Shin Jaeyoung, Im Chang-Hwan

2020

bootstrap aggregating, brain-computer interface, ensemble learning, near-infrared spectroscopy, pattern classification

General General

Latent Factor Decoding of Multi-Channel EEG for Emotion Recognition Through Autoencoder-Like Neural Networks.

In Frontiers in neuroscience ; h5-index 72.0

Robust cross-subject emotion recognition based on multichannel EEG has always been hard work. In this work, we hypothesize that there exist default brain variables across subjects in emotional processes. Hence, the states of the latent variables that relate to emotional processing must contribute to building robust recognition models. Specifically, we propose to utilize an unsupervised deep generative model (e.g., variational autoencoder) to determine the latent factors from the multichannel EEG. Through a sequence modeling method, we examine the emotion recognition performance based on the learnt latent factors. The performance of the proposed methodology is verified on two public datasets (DEAP and SEED) and compared with traditional matrix factorization-based (ICA) and autoencoder-based approaches. Experimental results demonstrate that autoencoder-like neural networks are suitable for unsupervised EEG modeling, and our proposed emotion recognition framework achieves an inspiring performance. As far as we know, it is the first work that introduces variational autoencoder into multichannel EEG decoding for emotion recognition. We think the approach proposed in this work is not only feasible in emotion recognition but also promising in diagnosing depression, Alzheimer's disease, mild cognitive impairment, etc., whose specific latent processes may be altered or aberrant compared with the normal healthy control.

Li Xiang, Zhao Zhigang, Song Dawei, Zhang Yazhou, Pan Jingshan, Wu Lu, Huo Jidong, Niu Chunyang, Wang Di

2020

EEG, deep learning, emotion recognition, latent factor decoding, variational autoencoder

Public Health Public Health

An interdisciplinary review of digital technologies to facilitate anti-corruption, transparency and accountability in medicines procurement.

In Global health action ; h5-index 41.0

Background: Pharmaceutical corruption is a serious challenge in global health. Digital technologies that can detect and prevent fraud and corruption are particularly important to address barriers to access to medicines, such as medicines availability and affordability, stockouts, shortages, diversion, and infiltration of substandard and falsified medicines.Objectives: To better understand how digital technologies are used to combat corruption, increase transparency, and detect fraud in pharmaceutical procurement systems to improve population health outcomes.Methods: We conducted a multidisciplinary review of the health/medicine, engineering, and computer science literature. Our search queries included keywords associated with medicines procurement and digital technology in combination with terms associated with transparency and anti-corruption initiatives. Our definition of 'digital technology' focused on Internet-based communications, including online portals and management systems, supply chain tools, and electronic databases.Results: We extracted 37 articles for in-depth review based on our inclusion criteria focused on the utilization of digital technology to improve medicines procurement. The vast majority of articles focused on electronic data transfer and/or e-procurement systems with fewer articles discussing emerging technologies such as machine learning and blockchain distributed ledger solutions. In the context of e-procurement, slow adoption, justifying cost-savings, and need for technical standards setting were identified as key challenges for current and future utilization.Conclusions: Though there is a significant promise for digital technologies, particularly e-procurement, overall adoption of solutions that can enhance transparency, accountability and concomitantly combat corruption, is still underdeveloped. Future efforts should focus on tying cost-saving measurements with anti-corruption indicators, prioritizing centralization of e-procurement systems, establishing regulatory harmonization with standards setting, and incorporating additional anti-corruption technologies into procurement processes for improving access to medicines and to reach the overall goal of Universal Health Coverage.

Mackey Tim K, Cuomo Raphael E

2020

Anti-Corruption, Transparency and Accountability, Medicines procurement, access to medicines, corruption, e-procurement, global health, technology, transparency

Radiology Radiology

Machine Learning Methods Predict Individual Upper-Limb Motor Impairment Following Therapy in Chronic Stroke.

In Neurorehabilitation and neural repair ; h5-index 48.0

Background. Accurate prediction of clinical impairment in upper-extremity motor function following therapy in chronic stroke patients is a difficult task for clinicians but is key in prescribing appropriate therapeutic strategies. Machine learning is a highly promising avenue with which to improve prediction accuracy in clinical practice. Objectives. The objective was to evaluate the performance of 5 machine learning methods in predicting postintervention upper-extremity motor impairment in chronic stroke patients using demographic, clinical, neurophysiological, and imaging input variables. Methods. A total of 102 patients (female: 31%, age 61 ± 11 years) were included. The upper-extremity Fugl-Meyer Assessment (UE-FMA) was used to assess motor impairment of the upper limb before and after intervention. Elastic net (EN), support vector machines, artificial neural networks, classification and regression trees, and random forest were used to predict postintervention UE-FMA. The performances of methods were compared using cross-validated R2. Results. EN performed significantly better than other methods in predicting postintervention UE-FMA using demographic and baseline clinical data (median R EN 2 = 0 . 91 , R RF 2 = 0 . 88 , R ANN 2 = 0 . 83 , R SVM 2 = 0 . 79 , R CART 2 = 0 . 70 ; P < .05). Preintervention UE-FMA and the difference in motor threshold (MT) between the affected and unaffected hemispheres were the strongest predictors. The difference in MT had greater importance than the absence or presence of a motor-evoked potential (MEP) in the affected hemisphere. Conclusion. Machine learning methods may enable clinicians to accurately predict a chronic stroke patient's postintervention UE-FMA. Interhemispheric difference in the MT is an important predictor of chronic stroke patients' response to therapy and, therefore, could be included in prospective studies.

Tozlu Ceren, Edwards Dylan, Boes Aaron, Labar Douglas, Tsagaris K Zoe, Silverstein Joshua, Pepper Lane Heather, Sabuncu Mert R, Liu Charles, Kuceyeski Amy

2020-Mar-20

Fugl-Meyer Assessment, chronic stroke, machine learning, predictive models, white matter disconnectivity

Radiology Radiology

Radiomics and Deep Learning: Hepatic Applications.

In Korean journal of radiology

Radiomics and deep learning have recently gained attention in the imaging assessment of various liver diseases. Recent research has demonstrated the potential utility of radiomics and deep learning in staging liver fibroses, detecting portal hypertension, characterizing focal hepatic lesions, prognosticating malignant hepatic tumors, and segmenting the liver and liver tumors. In this review, we outline the basic technical aspects of radiomics and deep learning and summarize recent investigations of the application of these techniques in liver disease.

Park Hyo Jung, Park Bumwoo, Lee Seung Soo

2020-Apr

Artificial intelligence, Computer-assisted, Deep learning, Liver, Radiomics

Radiology Radiology

Predicting the Response to FOLFOX-Based Chemotherapy Regimen from Untreated Liver Metastases on Baseline CT: a Deep Neural Network Approach.

In Journal of digital imaging

In developed countries, colorectal cancer is the second cause of cancer-related mortality. Chemotherapy is considered a standard treatment for colorectal liver metastases (CLM). Among patients who develop CLM, the assessment of patient response to chemotherapy is often required to determine the need for second-line chemotherapy and eligibility for surgery. However, while FOLFOX-based regimens are typically used for CLM treatment, the identification of responsive patients remains elusive. Computer-aided diagnosis systems may provide insight in the classification of liver metastases identified on diagnostic images. In this paper, we propose a fully automated framework based on deep convolutional neural networks (DCNN) which first differentiates treated and untreated lesions to identify new lesions appearing on CT scans, followed by a fully connected neural networks to predict from untreated lesions in pre-treatment computed tomography (CT) for patients with CLM undergoing chemotherapy, their response to a FOLFOX with Bevacizumab regimen as first-line of treatment. The ground truth for assessment of treatment response was histopathology-determined tumor regression grade. Our DCNN approach trained on 444 lesions from 202 patients achieved accuracies of 91% for differentiating treated and untreated lesions, and 78% for predicting the response to FOLFOX-based chemotherapy regimen. Experimental results showed that our method outperformed traditional machine learning algorithms and may allow for the early detection of non-responsive patients.

Maaref Ahmad, Romero Francisco Perdigon, Montagnon Emmanuel, Cerny Milena, Nguyen Bich, Vandenbroucke Franck, Soucy Geneviève, Turcotte Simon, Tang An, Kadoury Samuel

2020-Mar-19

CT scans, Chemotherapy, Colorectal liver metastases, Deep convolutional neural network, FOLFOX-based regimen, Prediction response

Radiology Radiology

Diagnostic accuracy and potential covariates for machine learning to identify IDH mutations in glioma patients: evidence from a meta-analysis.

In European radiology ; h5-index 62.0

OBJECTIVES : To assess the diagnostic accuracy of machine learning (ML) in predicting isocitrate dehydrogenase (IDH) mutations in patients with glioma and to identify potential covariates that could influence the diagnostic performance of ML.

METHODS : A systematic search of PubMed, Web of Science, and the Cochrane library up to 1 August 2019 was conducted to collect all the articles investigating the diagnostic performance of ML for prediction of IDH mutation in glioma. The search strategy combined synonyms for 'machine learning', 'glioma', and 'IDH'. Pooled sensitivity, specificity, and their 95% confidence intervals (CIs) were calculated, and the area under the receiver operating characteristic curve (AUC) was obtained.

RESULTS : Nine original articles assessing a total of 996 patients with glioma were included. Among these studies, five divided the participants into training and validation sets, while the remaining four studies only had a training set. The AUC of ML for predicting IDH mutation in the training and validation sets was 93% (95% CI 91-95%) and 89% (95% CI 86-92%), respectively. The pooled sensitivity and specificity were, respectively, 87% (95% CI 82-91%) and 88% (95% CI 83-92%) in the training set and 87% (95% CI 76-93%) and 90% (95% CI 72-97%) in the validation set. In subgroup analyses in the training set, the combined use of clinical and imaging features with ML yielded higher sensitivity (90% vs. 83%) and specificity (90% vs. 82%) than the use of imaging features alone. In addition, ML performed better for high-grade gliomas than for low-grade gliomas, and ML that used conventional MRI sequences demonstrated higher specificity for predicting IDH mutation than ML using conventional and advanced MRI sequences.

CONCLUSIONS : ML demonstrated an excellent diagnostic performance in predicting IDH mutation of glioma. Clinical information, MRI sequences, and glioma grade were the main factors influencing diagnostic specificity.

KEY POINTS : • Machine learning demonstrated an excellent diagnostic performance for prediction of IDH mutation in glioma (the pooled sensitivity and specificity were 88% and 87%, respectively). • Machine learning that used conventional MRI sequences demonstrated higher specificity in predicting IDH mutation than that based on conventional and advanced MRI sequences (89% vs. 85%). • Integration of clinical and imaging features in machine learning yielded a higher sensitivity (90% vs. 83%) and specificity (90% vs. 82%) than that achieved by using imaging features alone.

Zhao Jing, Huang Yingqian, Song Yukun, Xie Dingxiang, Hu Manshi, Qiu Haishan, Chu Jianping

2020-Mar-19

Glioma, Isocitrate dehydrogenase (IDH), MRI, Machine learning

Pathology Pathology

Nanoscale imaging using differential expansion microscopy.

In Histochemistry and cell biology

Expensive and time-consuming approaches of immunoelectron microscopy of biopsy tissues continues to serve as the gold-standard for diagnostic pathology. The recent development of the new approach of expansion microscopy (ExM) capable of fourfold lateral expansion of biological specimens for their morphological examination at approximately 70 nm lateral resolution using ordinary diffraction limited optical microscopy, is a major advancement in cellular imaging. Here we report (1) an optimized fixation protocol for retention of cellular morphology while obtaining optimal expansion, (2) an ExM procedure for up to eightfold lateral and over 500-fold volumetric expansion, (3) demonstrate that ExM is anisotropic or differential between tissues, cellular organelles and domains within organelles themselves, and (4) apply image analysis and machine learning (ML) approaches to precisely assess differentially expanded cellular structures. We refer to this enhanced ExM approach combined with ML as differential expansion microscopy (DiExM), applicable to profiling biological specimens at the nanometer scale. DiExM holds great promise for the precise, rapid and inexpensive diagnosis of disease from pathological specimen slides.

Pernal Sebastian P, Liyanaarachchi Asiri, Gatti Domenico L, Formosa Brent, Pulvender Rishika, Kuhn Eric R, Ramos Rafael, Naik Akshata R, George Kathleen, Arslanturk Suzan, Taatjes Douglas J, Jena Bhanu P

2020-Mar-19

Differential expansion, Machine learning, Nanoscale imaging, Optical microscopy

General General

Comparison of Night, Day and 24 h Motor Activity Data for the Classification of Depressive Episodes.

In Diagnostics (Basel, Switzerland)

Major Depression Disease has been increasing in the last few years, affecting around 7 percent of the world population, but nowadays techniques to diagnose it are outdated and inefficient. Motor activity data in the last decade is presented as a better way to diagnose, treat and monitor patients suffering from this illness, this is achieved through the use of machine learning algorithms. Disturbances in the circadian rhythm of mental illness patients increase the effectiveness of the data mining process. In this paper, a comparison of motor activity data from the night, day and full day is carried out through a data mining process using the Random Forest classifier to identified depressive and non-depressive episodes. Data from Depressjon dataset is split into three different subsets and 24 features in time and frequency domain are extracted to select the best model to be used in the classification of depression episodes. The results showed that the best dataset and model to realize the classification of depressive episodes is the night motor activity data with 99.37% of sensitivity and 99.91% of specificity.

Rodríguez-Ruiz Julieta G, Galván-Tejada Carlos E, Zanella-Calzada Laura A, Celaya-Padilla José M, Galván-Tejada Jorge I, Gamboa-Rosales Hamurabi, Luna-García Huizilopoztli, Magallanes-Quintanar Rafael, Soto-Murillo Manuel A

2020-Mar-17

data mining, depression, depressive episodes, motor activity, night, random forest

Ophthalmology Ophthalmology

Automatic optic nerve head localization and cup-to-disc ratio detection using state-of-the-art deep-learning architectures.

In Scientific reports ; h5-index 158.0

Computer vision has greatly advanced recently. Since AlexNet was first introduced, many modified deep learning architectures have been developed and they are still evolving. However, there are few studies comparing these architectures in the field of ophthalmology. This study compared the performance of various state-of-the-art deep-learning architectures for detecting the optic nerve head and vertical cup-to-disc ratio in fundus images. Three different architectures were compared: YOLO V3, ResNet, and DenseNet. We compared various aspects of performance, which were not confined to the accuracy of detection but included, as well, the processing time, diagnostic performance, effect of the graphic processing unit (GPU), and image resolution. In general, as the input image resolution increased, the classification accuracy, localization error, and diagnostic performance all improved, but the optimal architecture differed depending on the resolution. The processing time was significantly accelerated with GPU assistance; even at the high resolution of 832 × 832, it was approximately 170 ms, which was at least 26 times slower without GPU. The choice of architecture may depend on the researcher's purpose when balancing between speed and accuracy. This study provides a guideline to determine deep learning architecture, optimal image resolution, and the appropriate hardware.

Park Keunheung, Kim Jinmi, Lee Jiwoong

2020-Mar-19

General General

Corticomuscular interactions during different movement periods in a multi-joint compound movement.

In Scientific reports ; h5-index 158.0

While much is known about motor control during simple movements, corticomuscular communication profiles during compound movement control remain largely unexplored. Here, we aimed at examining frequency band related interactions between brain and muscles during different movement periods of a bipedal squat (BpS) task utilizing regression corticomuscular coherence (rCMC), as well as partial directed coherence (PDC) analyses. Participants performed 40 squats, divided into three successive movement periods (Eccentric (ECC), Isometric (ISO) and Concentric (CON)) in a standardized manner. EEG was recorded from 32 channels specifically-tailored to cover bilateral sensorimotor areas while bilateral EMG was recorded from four main muscles of BpS. We found both significant CMC and PDC (in beta and gamma bands) during BpS execution, where CMC was significantly elevated during ECC and CON when compared to ISO. Further, the dominant direction of information flow (DIF) was most prominent in EEG-EMG direction for CON and EMG-EEG direction for ECC. Collectively, we provide novel evidence that motor control during BpS is potentially achieved through central motor commands driven by a combination of directed inputs spanning across multiple frequency bands. These results serve as an important step toward a better understanding of brain-muscle relationships during multi joint compound movements.

Kenville Rouven, Maudrich Tom, Vidaurre Carmen, Maudrich Dennis, Villringer Arno, Nikulin Vadim V, Ragert Patrick

2020-Mar-19

General General

Test-time augmentation for deep learning-based cell segmentation on microscopy images.

In Scientific reports ; h5-index 158.0

Recent advancements in deep learning have revolutionized the way microscopy images of cells are processed. Deep learning network architectures have a large number of parameters, thus, in order to reach high accuracy, they require a massive amount of annotated data. A common way of improving accuracy builds on the artificial increase of the training set by using different augmentation techniques. A less common way relies on test-time augmentation (TTA) which yields transformed versions of the image for prediction and the results are merged. In this paper we describe how we have incorporated the test-time argumentation prediction method into two major segmentation approaches utilized in the single-cell analysis of microscopy images. These approaches are semantic segmentation based on the U-Net, and instance segmentation based on the Mask R-CNN models. Our findings show that even if only simple test-time augmentations (such as rotation or flipping and proper merging methods) are applied, TTA can significantly improve prediction accuracy. We have utilized images of tissue and cell cultures from the Data Science Bowl (DSB) 2018 nuclei segmentation competition and other sources. Additionally, boosting the highest-scoring method of the DSB with TTA, we could further improve prediction accuracy, and our method has reached an ever-best score at the DSB.

Moshkov Nikita, Mathe Botond, Kertesz-Farkas Attila, Hollandi Reka, Horvath Peter

2020-Mar-19

General General

Improving detection of protein-ligand binding sites with 3D segmentation.

In Scientific reports ; h5-index 158.0

In recent years machine learning (ML) took bio- and cheminformatics fields by storm, providing new solutions for a vast repertoire of problems related to protein sequence, structure, and interactions analysis. ML techniques, deep neural networks especially, were proven more effective than classical models for tasks like predicting binding affinity for molecular complex. In this work we investigated the earlier stage of drug discovery process - finding druggable pockets on protein surface, that can be later used to design active molecules. For this purpose we developed a 3D fully convolutional neural network capable of binding site segmentation. Our solution has high prediction accuracy and provides intuitive representations of the results, which makes it easy to incorporate into drug discovery projects. The model's source code, together with scripts for most common use-cases is freely available at http://gitlab.com/cheminfIBB/kalasanty.

Stepniewska-Dziubinska Marta M, Zielenkiewicz Piotr, Siedlecki Pawel

2020-Mar-19

General General

Multi-modular AI Approach to Streamline Autism Diagnosis in Young Children.

In Scientific reports ; h5-index 158.0

Autism has become a pressing healthcare challenge. The instruments used to aid diagnosis are time and labor expensive and require trained clinicians to administer, leading to long wait times for at-risk children. We present a multi-modular, machine learning-based assessment of autism comprising three complementary modules for a unified outcome of diagnostic-grade reliability: A 4-minute, parent-report questionnaire delivered via a mobile app, a list of key behaviors identified from 2-minute, semi-structured home videos of children, and a 2-minute questionnaire presented to the clinician at the time of clinical assessment. We demonstrate the assessment reliability in a blinded, multi-site clinical study on children 18-72 months of age (n = 375) in the United States. It outperforms baseline screeners administered to children by 0.35 (90% CI: 0.26 to 0.43) in AUC and 0.69 (90% CI: 0.58 to 0.81) in specificity when operating at 90% sensitivity. Compared to the baseline screeners evaluated on children less than 48 months of age, our assessment outperforms the most accurate by 0.18 (90% CI: 0.08 to 0.29 at 90%) in AUC and 0.30 (90% CI: 0.11 to 0.50) in specificity when operating at 90% sensitivity.

Abbas Halim, Garberson Ford, Liu-Mayo Stuart, Glover Eric, Wall Dennis P

2020-Mar-19

General General

Machine Learning Techniques for Classifying the Mutagenic Origins of Point Mutations.

In Genetics ; h5-index 66.0

There is increasing interest in developing diagnostics that discriminate individual mutagenic mechanisms in a range of applications that include identifying population specific mutagenesis and resolving distinct mutation signatures in cancer samples. Analyses for these applications assume that mutagenic mechanisms have a distinct relationship with neighboring bases that allows them to be distinguished. Direct support for this assumption is limited to a small number of simple cases, e.g. CpG hypermutability. We have evaluated whether the mechanistic origin of a point mutation can be resolved using only sequence context for a more complicated case. We contrasted single nucleotide variants originating from the multitude of mutagenic processes that normally operate in the mouse germline with those induced by the potent mutagen N-ethyl-N-nitrosourea (ENU). The considerable overlap in the mutation spectra of these two samples make this a challenging problem. Employing a new, robust log-linear modeling method, we demonstrate that neighboring bases contain information regarding point mutation direction that differs between the ENU-induced and spontaneous mutation variant classes. A logistic regression classifier exhibited strong performance at discriminating between the different mutation classes. Concordance between the feature set of the best classifier and information content analyses suggest our results can be generalized to other mutation classification problems. We conclude that machine learning can be used to build a practical classification tool to identify the mutation mechanism for individual genetic variants. Software implementing our approach is freely available under an open source license.

Zhu Yicheng, Ong Cheng Soon, Huttley Gavin A

2020-Mar-19

bioinformatics, context dependent mutation, germline mutation, log-linear model, machine learning, mutagenesis, mutation spectrum, sequence motif analysis

Surgery Surgery

Identification of elders at higher risk for fall with statewide electronic health records and a machine learning algorithm.

In International journal of medical informatics ; h5-index 49.0

OBJECTIVE : Predicting the risk of falls in advance can benefit the quality of care and potentially reduce mortality and morbidity in the older population. The aim of this study was to construct and validate an electronic health record-based fall risk predictive tool to identify elders at a higher risk of falls.

METHODS : The one-year fall prediction model was developed using the machine-learning-based algorithm, XGBoost, and tested on an independent validation cohort. The data were collected from electronic health records (EHR) of Maine from 2016 to 2018, comprising 265,225 older patients (≥65 years of age).

RESULTS : This model attained a validated C-statistic of 0.807, where 50 % of the identified high-risk true positives were confirmed to fall during the first 94 days of next year. The model also captured in advance 58.01 % and 54.93 % of falls that happened within the first 30 and 30-60 days of next year. The identified high-risk patients of fall showed conditions of severe disease comorbidities, an enrichment of fall-increasing cardiovascular and mental medication prescriptions and increased historical clinical utilization, revealing the complexity of the underlying fall etiology. The XGBoost algorithm captured 157 impactful predictors into the final predictive model, where cognitive disorders, abnormalities of gait and balance, Parkinson's disease, fall history and osteoporosis were identified as the top-5 strongest predictors of the future fall event.

CONCLUSIONS : By using the EHR data, this risk assessment tool attained an improved discriminative ability and can be immediately deployed in the health system to provide automatic early warnings to older adults with increased fall risk and identify their personalized risk factors to facilitate customized fall interventions.

Ye Chengyin, Li Jinmei, Hao Shiying, Liu Modi, Jin Hua, Zheng Le, Xia Minjie, Jin Bo, Zhu Chunqing, Alfreds Shaun T, Stearns Frank, Kanov Laura, Sylvester Karl G, Widen Eric, McElhinney Doff, Ling Xuefeng Bruce

2020-Mar-03

Accidental falls, Aged, Electronic health records, Supervised machine learning

General General

Automated detection and classification of diabetes disease based on Bangladesh demographic and health survey data, 2011 using machine learning approach.

In Diabetes & metabolic syndrome

BACKGROUND AND AIMS : Diabetes has been recognized as a continuing health challenge for the twenty-first century, both in developed and developing countries including Bangladesh. The main objective of this study is to use machine learning (ML) based classifiers for automated detection and classification of diabetes.

METHODS : The diabetes dataset have taken from Bangladesh demographic and health survey, 2011 data having 1569 respondents are 127 diabetes. Two statistical tests as independent t for continuous and chi-square for categorical variables are used to determine the risk factors of diabetes. Six ML-based classifiers as support vector machine, random forest, linear discriminant analysis, logistic regression, k-nearest neighborhood, bagged classification and regression tree (Bagged CART) have been adopted to predict and classify of diabetes.

RESULTS : Our findings show that 11 factors out of 15 factors are significantly associated with diabetes. Bagged CART provides the highest accuracy and area under the curve of 94.3% and 0.600.

CONCLUSIONS : Bagged CART anticipates a very supportive computational resource for classification of diabetes and it would be very helpful to the doctors for making a decision to control diabetes disease in Bangladesh.

Islam Md Merajul, Rahman Md Jahanur, Chandra Roy Dulal, Maniruzzaman Md

2020-Mar-10

Diabetes, Machine learning and Bangladesh

General General

Wearable sensor-based detection of stress and craving in patients during treatment for substance use disorder: A mixed methods pilot study.

In Drug and alcohol dependence ; h5-index 64.0

AIMS : To determine the accuracy of a wearable sensor to detect and differentiate episodes of self-reported craving and stress in individuals with substance use disorders, and to assess acceptability, barriers, and facilitators to sensor-based monitoring in this population.

METHODS : This was an observational mixed methods pilot study. Adults enrolled in an outpatient treatment program for a substance use disorder wore a non-invasive wrist-mounted sensor for four days and self-reported episodes of stress and craving. Continuous physiologic data (accelerometry, skin conductance, skin temperature, and heart rate) were extracted from the sensors and analyzed via various machine learning algorithms. Semi-structured interviews were conducted upon study completion, and thematic analysis was conducted on qualitative data from semi-structured interviews.

RESULTS : Thirty individuals completed the protocol, and 43 % (N = 13) were female. A total of 41 craving and 104 stress events were analyzed. The differentiation accuracies of the top performing models were as follows: stress vs. non-stress states 74.5 % (AUC 0.82), craving vs. no-craving 75.7 % (AUC 0.82), and craving vs. stress 76.8 % (AUC 0.8). Overall participant perception was positive, and acceptability was high. Emergent themes from the exit interviews included a perception of connectedness and increased mindfulness related to wearing the sensor, both of which were reported as helpful to recovery. Barriers to engagement included interference with other daily wear items, and perceived stigma.

CONCLUSIONS : Wearable sensors can be used to objectively differentiate episodes of craving and stress, and individuals in recovery from substance use disorder are accepting of continuous monitoring with these devices.

Carreiro Stephanie, Chintha Keerthi Kumar, Shrestha Sloke, Chapman Brittany, Smelson David, Indic Premananda

2020-Mar-03

Craving, Sensor, Stress, Substance use disorder, Wearable, mHealth

Radiology Radiology

Detection and localization of distal radius fractures: Deep learning system versus radiologists.

In European journal of radiology ; h5-index 47.0

PURPOSE : To evaluate a deep learning based image analysis software for the detection and localization of distal radius fractures.

METHOD : A deep learning system (DLS) was trained on 524 wrist radiographs (166 showing fractures). Performance was tested on internal (100 radiographs, 42 showing fractures) and external test sets (200 radiographs, 100 showing fractures). Single and combined views of the radiographs were shown to DLS and three readers. Readers were asked to indicate fracture location with regions of interest (ROI). The DLS yielded scores (range 0-1) and a heatmap. Detection performance was expressed as AUC, sensitivity and specificity at the optimal threshold and compared to radiologists' performance. Heatmaps were compared to radiologists' ROIs.

RESULTS : The DLS showed excellent performance on the internal test set (AUC 0.93 (95% confidence interval (CI) 0.82-0.98) - 0.96 (0.87-1.00), sensitivity 0.81 (0.58-0.95) - 0.90 (0.70-0.99), specificity 0.86 (0.68-0.96) - 1.0 (0.88-1.0)). DLS performance decreased on the external test set (AUC 0.80 (0.71-0.88) - 0.89 (0.81-0.94), sensitivity 0.64 (0.49-0.77) - 0.92 (0.81-0.98), specificity 0.60 (0.45-0.74) - 0.90 (0.78-0.97)). Radiologists' performance was comparable on internal data (sensitivity 0.71 (0.48-0.89) - 0.95 (0.76-1.0), specificity 0.52 (0.32-0.71) - 0.97 (0.82-1.0)) and better on external data (sensitivity 0.88 (0.76-0.96) - 0.98 (0.89-1.0), specificities 0.66 (0.51-0.79) - 1.0 (0.93-1.0), p < 0.05). In over 90%, the areas of peak activation aligned with radiologists' annotations.

CONCLUSIONS : The DLS was able to detect and localize wrist fractures with a performance comparable to radiologists, using only a small dataset for training.

Blüthgen Christian, Becker Anton S, Vittoria de Martini Ilaria, Meier Andreas, Martini Katharina, Frauenfelder Thomas

2020-Mar-09

General General

A neural network approach for real-time particle/cell characterization in microfluidic impedance cytometry.

In Analytical and bioanalytical chemistry

Microfluidic applications such as active particle sorting or selective enrichment require particle classification techniques that are capable of working in real time. In this paper, we explore the use of neural networks for fast label-free particle characterization during microfluidic impedance cytometry. A recurrent neural network is designed to process data from a novel impedance chip layout for enabling real-time multiparametric analysis of the measured impedance data streams. As demonstrated with both synthetic and experimental datasets, the trained network is able to characterize with good accuracy size, velocity, and cross-sectional position of beads, red blood cells, and yeasts, with a unitary prediction time of 0.4 ms. The proposed approach can be extended to other device designs and cell types for electrical parameter extraction. This combination of microfluidic impedance cytometry and machine learning can serve as a stepping stone to real-time single-cell analysis and sorting. Graphical Abstract.

Honrado Carlos, McGrath John S, Reale Riccardo, Bisegna Paolo, Swami Nathan S, Caselli Frederica

2020-Mar-18

Microfluidic impedance cytometry, Multiparametric characterization, Neural networks, Real-time processing, Single-cell analysis

General General

Identifying predictive features of autism spectrum disorders in a clinical sample of adolescents and adults using machine learning.

In Scientific reports ; h5-index 158.0

Diagnosing autism spectrum disorders (ASD) is a complicated, time-consuming process which is particularly challenging in older individuals. One of the most widely used behavioral diagnostic tools is the Autism Diagnostic Observation Schedule (ADOS). Previous work using machine learning techniques suggested that ASD detection in children can be achieved with substantially fewer items than the original ADOS. Here, we expand on this work with a specific focus on adolescents and adults as assessed with the ADOS Module 4. We used a machine learning algorithm (support vector machine) to examine whether ASD detection can be improved by identifying a subset of behavioral features from the ADOS Module 4 in a routine clinical sample of N = 673 high-functioning adolescents and adults with ASD (n = 385) and individuals with suspected ASD but other best-estimate or no psychiatric diagnoses (n = 288). We identified reduced subsets of 5 behavioral features for the whole sample as well as age subgroups (adolescents vs. adults) that showed good specificity and sensitivity and reached performance close to that of the existing ADOS algorithm and the full ADOS, with no significant differences in overall performance. These results may help to improve the complicated diagnostic process of ASD by encouraging future efforts to develop novel diagnostic instruments for ASD detection based on the identified constructs as well as aiding clinicians in the difficult question of differential diagnosis.

Küpper Charlotte, Stroth Sanna, Wolff Nicole, Hauck Florian, Kliewer Natalia, Schad-Hansjosten Tanja, Kamp-Becker Inge, Poustka Luise, Roessner Veit, Schultebraucks Katharina, Roepke Stefan

2020-Mar-18

General General

A quantitative super-resolution imaging toolbox for diagnosis of motile ciliopathies.

In Science translational medicine ; h5-index 138.0

Airway clearance of pathogens and particulates relies on motile cilia. Impaired cilia motility can lead to reduction in lung function, lung transplant, or death in some cases. More than 50 proteins regulating cilia motility are linked to primary ciliary dyskinesia (PCD), a heterogeneous, mainly recessive genetic lung disease. Accurate PCD molecular diagnosis is essential for identifying therapeutic targets and for initiating therapies that can stabilize lung function, thereby reducing socioeconomic impact of the disease. To date, PCD diagnosis has mainly relied on nonquantitative methods that have limited sensitivity or require a priori knowledge of the genes involved. Here, we developed a quantitative super-resolution microscopy workflow: (i) to increase sensitivity and throughput, (ii) to detect structural defects in PCD patients' cells, and (iii) to quantify motility defects caused by yet to be found PCD genes. Toward these goals, we built a localization map of PCD proteins by three-dimensional structured illumination microscopy and implemented quantitative image analysis and machine learning to detect protein mislocalization, we analyzed axonemal structure by stochastic optical reconstruction microscopy, and we developed a high-throughput method for detecting motile cilia uncoordination by rotational polarity. Together, our data show that super-resolution methods are powerful tools for improving diagnosis of motile ciliopathies.

Liu Zhen, Nguyen Quynh P H, Guan Qingxu, Albulescu Alexandra, Erdman Lauren, Mahdaviyeh Yasaman, Kang Jasmine, Ouyang Hong, Hegele Richard G, Moraes Theo, Goldenberg Anna, Dell Sharon D, Mennella Vito

2020-Mar-18

General General

Artificial intelligence in fixed implant prosthodontics: a retrospective study of 106 implant-supported monolithic zirconia crowns inserted in the posterior jaws of 90 patients.

In BMC oral health ; h5-index 40.0

BACKGROUND : Artificial intelligence (AI) is a branch of computer science concerned with building smart software or machines capable of performing tasks that typically require human intelligence. We present a protocol for the use of AI to fabricate implant-supported monolithic zirconia crowns (MZCs) cemented on customized hybrid abutments.

METHODS : The study protocol consisted of: (1) intraoral scan of the implant position; (2) design of the individual abutment and temporary crown using computer-aided design (CAD) software; (3) milling of the zirconia abutment and the temporary polymethyl-methacrylate (PMMA) crown, with extraoral cementation of the zirconia abutment on the relative titanium bonding base, to generate an individual hybrid abutment; (4) clinical application of the hybrid abutment and the temporary PMMA crown; (5) intraoral scan of the hybrid abutment; (6) CAD of the final crown with automated margin line design using AI; (7) milling, sintering and characterisation of the final MZC; and (8) clinical application of the MZC. The outcome variables were mathematical (quality of the fabrication of the individual zirconia abutment) and clinical, such as (1) quality of the marginal adaptation, (2) of interproximal contact points and (3) of occlusal contacts, (4) chromatic integration, (5) survival and (6) success of MZCs. A careful statistical analysis was performed.

RESULTS : 90 patients (35 males, 55 females; mean age 53.3 ± 13.7 years) restored with 106 implant-supported MZCs were included in the study. The follow-up varied from 6 months to 3 years. The quality of the fabrication of individual hybrid abutments revealed a mean deviation of 44 μm (± 6.3) between the original CAD design of the zirconia abutment, and the mesh of the zirconia abutment captured intraorally at the end of the provisionalization. At the delivery of the MZCs, the marginal adaptation, quality of interproximal and occlusal contacts, and aesthetic integration were excellent. The three-year cumulative survival and success of the MZCs were 99.0% and 91.3%, respectively.

CONCLUSIONS : AI seems to represent a reliable tool for the restoration of single implants with MZCs cemented on customised hybrid abutments via a full digital workflow. Further studies are needed to confirm these positive results.

Lerner Henriette, Mouhyi Jaafar, Admakin Oleg, Mangano Francesco

2020-Mar-19

Artificial intelligence, Full digital workflow, Individual hybrid abutments, Marginal adaptation, Monolithic zirconia crowns, Success, Survival

Cardiology Cardiology

Substrate Spatial Complexity Analysis for the Prediction of Ventricular Arrhythmias in Patients with Ischemic Cardiomyopathy.

In Circulation. Arrhythmia and electrophysiology

Background - Transition zones between healthy myocardium and scar form a spatially complex substrate that may give rise to reentrant ventricular arrhythmias (VA). We sought to assess the utility of a novel machine learning (ML) approach for quantifying 3D spatial complexity of grayscale patterns on late gadolinium enhanced cardiac magnetic resonance images (LGE-CMR) to predict VA in patients with ischemic cardiomyopathy (ICM). Methods - 122 consecutive ICM patients with left ventricular ejection fraction ≤35% without prior history of VA underwent LGE-CMR. From raw grayscale data, we generated graphs encoding the 3D geometry of the left ventricle (LV). A novel technique, adapted to these graphs, assessed global regularity of signal intensity patterns using Fourier-like analysis and generated a substrate spatial complexity (SSC) profile for each patient. An ML statistical algorithm was employed to discern which SSC profiles correlated with VA events (appropriate ICD firings and arrhythmic sudden cardiac death) at 5 years of follow-up. From the statistical ML results, a complexity score (CS) ranging from 0-1 was calculated for each patient and tested using multivariable Cox regression models. Results - At 5 years of follow-up, 40 patients had VA events. The ML algorithm classified with 81% overall accuracy and correctly classified 86% of those without VA. Overall negative predictive value was 91%. Average CS was significantly higher in patients with VA events versus those without (0.5 ± 0.5 vs 0.1 ± 0.2; p<0.0001) and was independently associated with VA events in a multivariable model (hazard ratio = 1.5 [1.2- 2.0]; p=0.002). Conclusions - SSC analysis of LGE-CMR images may be helpful in refining VA risk in patients with ICM, particularly to identify low risk patients who may not benefit from prophylactic ICD therapy.

Okada David R, Miller Jason, Chrispin Jonathan, Prakosa Adityo, Trayanova Natalia, Jones Steven, Maggioni Mauro, Wu Katherine C

2020-Mar-18

spatial complexity; risk stratification

General General

Predicting Pharmaceutical Particle Size Distributions Using Kernel Mean Embedding.

In Pharmaceutics

In the pharmaceutical industry, the transition to continuous manufacturing of solid dosage forms is adopted by more and more companies. For these continuous processes, high-quality process models are needed. In pharmaceutical wet granulation, a unit operation in the ConsiGma TM -25 continuous powder-to-tablet system (GEA Pharma systems, Collette, Wommelgem, Belgium), the product under study presents itself as a collection of particles that differ in shape and size. The measurement of this collection results in a particle size distribution. However, the theoretical basis to describe the physical phenomena leading to changes in this particle size distribution is lacking. It is essential to understand how the particle size distribution changes as a function of the unit operation's process settings, as it has a profound effect on the behavior of the fluid bed dryer. Therefore, we suggest a data-driven modeling framework that links the machine settings of the wet granulation unit operation and the output distribution of granules. We do this without making any assumptions on the nature of the distributions under study. A simulation of the granule size distribution could act as a soft sensor when in-line measurements are challenging to perform. The method of this work is a two-step procedure: first, the measured distributions are transformed into a high-dimensional feature space, where the relation between the machine settings and the distributions can be learnt. Second, the inverse transformation is performed, allowing an interpretation of the results in the original measurement space. Further, a comparison is made with previous work, which employs a more mechanistic framework for describing the granules. A reliable prediction of the granule size is vital in the assurance of quality in the production line, and is needed in the assessment of upstream (feeding) and downstream (drying, milling, and tableting) issues. Now that a validated data-driven framework for predicting pharmaceutical particle size distributions is available, it can be applied in settings such as model-based experimental design and, due to its fast computation, there is potential in real-time model predictive control.

Van Hauwermeiren Daan, Stock Michiel, De Beer Thomas, Nopens Ingmar

2020-Mar-16

continuous manufacturing, data-driven, granulation, kernel mean embedding, kernel methods, machine learning, particle size distributions, predictive modeling, process modeling, wet granulation

General General

FocalMix: Semi-Supervised Learning for 3D Medical Image Detection

ArXiv Preprint

Applying artificial intelligence techniques in medical imaging is one of the most promising areas in medicine. However, most of the recent success in this area highly relies on large amounts of carefully annotated data, whereas annotating medical images is a costly process. In this paper, we propose a novel method, called FocalMix, which, to the best of our knowledge, is the first to leverage recent advances in semi-supervised learning (SSL) for 3D medical image detection. We conducted extensive experiments on two widely used datasets for lung nodule detection, LUNA16 and NLST. Results show that our proposed SSL methods can achieve a substantial improvement of up to 17.3% over state-of-the-art supervised learning approaches with 400 unlabeled CT scans.

Dong Wang, Yuan Zhang, Kexin Zhang, Liwei Wang

2020-03-20

General General

Compound Characterization and Metabolic Profile Elucidation after In Vitro Gastrointestinal and Hepatic Biotransformation of an Herniaria hirsuta Extract Using Unbiased Dynamic Metabolomic Data Analysis.

In Metabolites

Herniaria hirsuta L. (Caryophyllaceae) is used for treatment of urinary stones and as a diuretic. Little is known about the active compounds and the mechanism of action. The phytochemical composition of H. hirsuta was comprehensively characterized using UHPLC-UV-HRMS (Ultrahigh-Performance Liquid Chromatography-Ultraviolet-High Resolution Mass Spectrometry) data. An in vitro gastrointestinal model was used to simulate biotransformation, which allowed the monitoring of the relative abundances of individual compounds over time. To analyze the longitudinal multiclass LC-MS data, XCMS, a platform that enables online metabolomics data processing and interpretation, and EDGE, a statistical method for time series data, were used to extract significant differential profiles from the raw data. An interactive Shiny app in R was used to rate the quality of the resulting features. These ratings were used to train a random forest model. The most abundant aglycone after gastrointestinal biotransformation was subjected to hepatic biotransformation using human S9 fractions. A diversity of compounds was detected, mainly saponins and flavonoids. Besides the known saponins, 15 new saponins were tentatively identified as glycosides of medicagenic acid, acetylated medicagenic acid and zanhic acid. It is suggested that metabolites of phytochemicals present in H. hirsuta, most likely saponins, are responsible for the pharmaceutical effects. It was observed that the relative abundance of saponin aglycones increased, indicating loss of sugar moieties during colonic biotransformation, with medicagenic acid as the most abundant aglycone. Hepatic biotransformation of this aglycone resulted in different metabolites formed by phase I and II reactions.

Peeters Laura, Van der Auwera Anastasia, Beirnaert Charlie, Bijttebier Sebastiaan, Laukens Kris, Pieters Luc, Hermans Nina, Foubert Kenn

2020-Mar-16

Caryophyllaceae, Herniaria hirsuta, dynamic metabolomics, machine learning, saponins, urolithiasis

General General

Weakly Supervised Context Encoder using DICOM metadata in Ultrasound Imaging

ArXiv Preprint

Modern deep learning algorithms geared towards clinical adaption rely on a significant amount of high fidelity labeled data. Low-resource settings pose challenges like acquiring high fidelity data and becomes the bottleneck for developing artificial intelligence applications. Ultrasound images, stored in Digital Imaging and Communication in Medicine (DICOM) format, have additional metadata data corresponding to ultrasound image parameters and medical exams. In this work, we leverage DICOM metadata from ultrasound images to help learn representations of the ultrasound image. We demonstrate that the proposed method outperforms the non-metadata based approaches across different downstream tasks.

Szu-Yeu Hu, Shuhang Wang, Wei-Hung Weng, JingChao Wang, XiaoHong Wang, Arinc Ozturk, Qian Li, Viksit Kumar, Anthony E. Samir

2020-03-20

Pathology Pathology

Artificial intelligence in glioma imaging: Challenges and advances.

In Journal of neural engineering ; h5-index 52.0

Primary brain tumors including gliomas continue to pose significant management challenges to clinicians. While the presentation, the pathology, and the clinical course of these lesions are variable, the initial investigations are usually similar. Patients who are suspected to have a brain tumor will be assessed with computed tomography (CT) and magnetic resonance imaging (MRI). The imaging findings are used by neurosurgeons to determine the feasibility of surgical resection and plan such an undertaking. Imaging studies are also an indispensable tool in tracking tumor progression or its response to treatment. As these imaging studies are non-invasive, relatively cheap and accessible to patients, there have been many efforts over the past two decades to increase the amount of clinically-relevant information that can be extracted from brain imaging. Most recently, artificial intelligence (AI) techniques have been employed to segment and characterize brain tumors, as well as to detect progression or treatment-response. However, the clinical utility of such endeavours remains limited due to challenges in data collection and annotation, model training, and the reliability of AI-generated information. We provide a review of recent advances in addressing the above challenges. First, to overcome the challenge of data paucity, different image imputation and synthesis techniques along with annotation collection efforts are summarized. Next, various training strategies are presented to meet multiple desiderata, such as model performance, generalization ability, data privacy protection, and learning with sparse annotations. Finally, standardized performance evaluation and model interpretability methods have been reviewed. We believe that these technical approaches will facilitate the development of a fully-functional AI tool in the clinical care of patients with gliomas.

Jin Weina, Fatehi Mostafa, Abhishek Kumar, Mallya Mayur, Toyota Brian, Hamarneh Ghassan

2020-Mar-19

Brain radiomics, Deep learning, Glioma imaging, Machine learning

General General

Deep Neural Network for Respiratory Sound Classification in Wearable Devices Enabled by Patient Specific Model Tuning.

In IEEE transactions on biomedical circuits and systems ; h5-index 39.0

The primary objective of this paper is to build classification models and strategies to identify breathing sound anomalies (wheeze, crackle) for automated diagnosis of respiratory and pulmonary diseases. In this work we propose a deep CNN-RNN model that classifies respiratory sounds based on Mel-spectrograms. We also implement a patient specific model tuning strategy that first screens respiratory patients and then builds patient specific classification models using limited patient data for reliable anomaly detection. Moreover, we devise a local log quantization strategy for model weights to reduce the memory footprint for deployment in memory constrained systems such as wearable devices. The proposed hybrid CNN-RNN model achieves a score of 66.31% on four-class classification of breathing cycles for ICBHI'17 scientific challenge respiratory sound database. When the model is re-trained with patient specific data, it produces a score of 71.81% for leave-one-out validation. The proposed weight quantization technique achieves ≍ 4× reduction in total memory cost without loss of performance. The main contribution of the paper is as follows: Firstly, the proposed model is able to achieve state of the art score on the ICBHI'17 dataset. Secondly, deep learning models are shown to successfully learn domain specific knowledge when pre-trained with breathing data and produce significantly superior performance compared to generalized models. Finally, local log quantization of trained weights is shown to be able to reduce the memory requirement significantly. This type of patient-specific re-training strategy can be very useful in developing reliable long-term automated patient monitoring systems particularly in wearable healthcare solutions.

Acharya Jyotibdha, Basu Arindam

2020-Mar-18

General General

Improved Predicting of The Sequence Specificities of RNA Binding Proteins by Deep Learning.

In IEEE/ACM transactions on computational biology and bioinformatics

RNA-binding proteins (RBPs) have a significant role in various regulatory tasks. However, the mechanism by which RBPs identify the subsequence target RNAs is still not clear. In recent years, several machine and deep learning-based computational models have been proposed for understanding the binding preferences of RBPs. These methods required integrating multiple features with raw RNA sequences such as secondary structure and their performances can be further improved. In this paper, we propose an efficient and simple convolution neural network, RBPCNN, that relies on the combination of the raw RNA sequence and evolutionary information. We show that conservation scores (evolutionary information) for the RNA sequences can significantly improve the overall performance of the proposed predictor. In addition, the automatic extraction of the binding sequence motifs can enhance our understanding of the binding specificities of RBPs. The experimental results show that RBPCNN outperforms significantly the current state-of-the-art methods. More specifically, the average area under the receiver operator curve was improved by 2.67% and the mean average precision was improved by 8.03%. The datasets, and results can be downloaded from https://home.jbnu.ac.kr/NSCL/RBPCNN.htm.

Tayara Hilal, Chong Kil

2020-Mar-18

General General

Brain-Controlled Robotic Arm System based on Multi-Directional CNN-BiLSTM Network using EEG Signals.

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

Brain-machine interfaces (BMIs) can be used to decode brain activity into commands to control external devices. This paper presents the decoding of intuitive upper extremity imagery for multi-directional arm reaching tasks in three-dimensional (3D) environments. We designed and implemented an experimental environment in which electroencephalogram (EEG) signals can be acquired for movement execution and imagery. Fifteen subjects participated in our experiments. We proposed a multi-directional convolution neural network-bidirectional long short-term memory network (MDCBN)-based deep learning framework. The decoding performances for six directions in 3D space were measured by the correlation coefficient (CC) and the normalized root mean square error (NRMSE) between predicted and baseline velocity profiles. The grand-averaged CCs of multi-direction were 0.47 and 0.45 for the execution and imagery sessions, respectively, across all subjects. The NRMSE values were below 0.2 for both sessions. Furthermore, in this study, the proposed MDCBN was evaluated by two online experiments for real-time robotic arm control, and the grand-averaged success rates were approximately 0.60 (±0.14) and 0.43 (±0.09), respectively. Hence, we demonstrate the feasibility of intuitive robotic arm control based on EEG signals for real-world environments.

Jeong Ji-Hoon, Shim Kyung-Hwan, Kim Dong-Joo, Lee Seong-Whan

2020-Mar-18

General General

Small Object Augmentation of Urban Scenes for Real-Time Semantic Segmentation.

In IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

Semantic segmentation is a key step in scene understanding for autonomous driving. Although deep learning has significantly improved the segmentation accuracy, current highquality models such as PSPNet and DeepLabV3 are inefficient given their complex architectures and reliance on multi-scale inputs. Thus, it is difficult to apply them to real-time or practical applications. On the other hand, existing real-time methods cannot yet produce satisfactory results on small objects such as traffic lights, which are imperative to safe autonomous driving. In this paper, we improve the performance of real-time semantic segmentation from two perspectives, methodology and data. Specifically, we propose a real-time segmentation model coined Narrow Deep Network (NDNet) and build a synthetic dataset by inserting additional small objects into the training images. The proposed method achieves 65.7% mean intersection over union (mIoU) on the Cityscapes test set with only 8.4G floatingpoint operations (FLOPs) on 1024×2048 inputs. Furthermore, by re-training the existing PSPNet and DeepLabV3 models on our synthetic dataset, we obtained an average 2% mIoU improvement on small objects.

Yang Zhengeng, Yu Hongshan, Feng Mingtao, Sun Wei, Lin Xuefei, Sun Mingui, Mao Zhi-Hong, Mian Ajmal

2020-Mar-18

General General

Academic Emotion Classification and Recognition Method for Large-scale Online Learning Environment-Based on A-CNN and LSTM-ATT Deep Learning Pipeline Method.

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

Subjective well-being is a comprehensive psychological indicator for measuring quality of life. Studies have found that emotional measurement methods and measurement accuracy are important for well-being-related research. Academic emotion is an emotion description in the field of education. The subjective well-being of learners in an online learning environment can be studied by analyzing academic emotions. However, in a large-scale online learning environment, it is extremely challenging to classify learners' academic emotions quickly and accurately for specific comment aspects. This study used literature analysis and data pre-analysis to build a dimensional classification system of academic emotion aspects for students' comments in an online learning environment, as well as to develop an aspect-oriented academic emotion automatic recognition method, including an aspect-oriented convolutional neural network (A-CNN) and an academic emotion classification algorithm based on the long short-term memory with attention mechanism (LSTM-ATT) and the attention mechanism. The experiments showed that this model can provide quick and effective identification. The A-CNN model accuracy on the test set was 89%, and the LSTM-ATT model accuracy on the test set was 71%. This research provides a new method for the measurement of large-scale online academic emotions, as well as support for research related to students' well-being in online learning environments.

Feng Xiang, Wei Yaojia, Pan Xianglin, Qiu Longhui, Ma Yongmei

2020-Mar-16

academic emotion, academic emotion classification algorithm, academic emotion classification method, subjective well-being

Radiology Radiology

Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT.

In Radiology ; h5-index 91.0

Background Coronavirus disease has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT. Purpose To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performances. Materials and Methods In this retrospective and multi-center study, a deep learning model, COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT exams for the detection of COVID-19. Community acquired pneumonia (CAP) and other non-pneumonia CT exams were included to test the robustness of the model. The datasets were collected from 6 hospitals between August 2016 and February 2020. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity and specificity. Results The collected dataset consisted of 4356 chest CT exams from 3,322 patients. The average age is 49±15 years and there were slightly more male patients than female (1838 vs 1484; p-value=0.29). The per-exam sensitivity and specificity for detecting COVID-19 in the independent test set was 114 of 127 (90% [95% CI: 83%, 94%]) and 294 of 307 (96% [95% CI: 93%, 98%]), respectively, with an AUC of 0.96 (p-value<0.001). The per-exam sensitivity and specificity for detecting CAP in the independent test set was 87% (152 of 175) and 92% (239 of 259), respectively, with an AUC of 0.95 (95% CI: 0.93, 0.97). Conclusions A deep learning model can accurately detect COVID-19 and differentiate it from community acquired pneumonia and other lung diseases.

Li Lin, Qin Lixin, Xu Zeguo, Yin Youbing, Wang Xin, Kong Bin, Bai Junjie, Lu Yi, Fang Zhenghan, Song Qi, Cao Kunlin, Liu Daliang, Wang Guisheng, Xu Qizhong, Fang Xisheng, Zhang Shiqin, Xia Juan, Xia Jun

2020-Mar-19

General General

RGB-Topography and X-rays Image Registration for Idiopathic Scoliosis Children Patient Follow-up

ArXiv Preprint

Children diagnosed with a scoliosis pathology are exposed during their follow up to ionic radiations in each X-rays diagnosis. This exposure can have negative effects on the patient's health and cause diseases in the adult age. In order to reduce X-rays scanning, recent systems provide diagnosis of scoliosis patients using solely RGB images. The output of such systems is a set of augmented images and scoliosis related angles. These angles, however, confuse the physicians due to their large number. Moreover, the lack of X-rays scans makes it impossible for the physician to compare RGB and X-rays images, and decide whether to reduce X-rays exposure or not. In this work, we exploit both RGB images of scoliosis captured during clinical diagnosis, and X-rays hard copies provided by patients in order to register both images and give a rich comparison of diagnoses. The work consists in, first, establishing the monomodal (RGB topography of the back) and multimodal (RGB and Xrays) image database, then registering images based on patient landmarks, and finally blending registered images for a visual analysis and follow up by the physician. The proposed registration is based on a rigid transformation that preserves the topology of the patient's back. Parameters of the rigid transformation are estimated using a proposed angle minimization of Cervical vertebra 7, and Posterior Superior Iliac Spine landmarks of a source and target diagnoses. Experiments conducted on the constructed database show a better monomodal and multimodal registration using our proposed method compared to registration using an Equation System Solving based registration.

Insaf Setitra, Noureddine Aouaa, Abdelkrim Meziane, Afef Benrabia, Houria Kaced, Hanene Belabassi, Sara Ait Ziane, Nadia Henda Zenati, Oualid Djekkoune

2020-03-20

General General

Autoencoder - a new method for keeping data privacy when analyzing videos of patients with motor dysfunction - a proof of concept study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : In chronical neurological diseases, especially in multiple sclerosis (MS), clinical assessment of motor dysfunction is crucial to monitor patients' disease. Traditional scales are not sensitive enough to detect slight changes. Video recordings of patient performance are more accurate and increase reliability in severity ratings. With these recordings automated, quantitative disability assessments by machine learning algorithms (MLA) may be created. Creation of these algorithms involves non-healthcare professionals, which is a challenge for keeping data privacy. Autoencoders may overcome this issue.

OBJECTIVE : The aim of this proof of concept study was to test whether coded frame vectors of autoencoders contain relevant information for analysing videos of motor performance of MS patients.

METHODS : In this study, twenty pre-rated videos of patients performing the finger-to-nose test (FNT) were recorded. An autoencoder created encoded frame vectors from the original videos and decoded the videos again. Original and decoded videos were shown to 10 neurologists of an academic MS centre in Basel, Switzerland. Neurologists tested whether these 200 videos in total were human-readable after decoding and rated the severity grade of each original and decoded video according to the Neurostatus-EDSS definitions of limb ataxia. Furthermore, the neurologists tested whether ratings were equivalent between original and decoded videos.

RESULTS : In total, 172 from 200 (86%) videos had sufficient quality to be ratable. The intra-rater agreement between the original and decoded videos was 0.317 (Cohen's weighted kappa). The average difference of ratings between original and decoded videos was 0.26 , in which the original videos were rated as more severe. The inter-rater agreement between the original and decoded videos was 0.459 (Cohen's weighted kappa) and 0.302 (Cohen's weighted kappa), respectively. The agreement was higher when no deficits or very severe deficits were present.

CONCLUSIONS : The vast majority of videos decoded by an auto-encoder contained clinically relevant information and had a fair intra-rater agreement with the original video. Autoencoders are a potential method for enabling the use of patient videos while preserving data privacy, especially when non-healthcare professionals are involved.

CLINICALTRIAL :

D’Souza Marcus, Van Munster Caspar E P, Dorn Jonas, Dorier Alexis, Kamm Christian P, Steinheimer Saskia, Dahlke Frank, Uitdehaag Bernard M J, Kappos Ludwig, Johnson Matthew

2020-Mar-19

Radiology Radiology

Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT.

In Radiology ; h5-index 91.0

Background Coronavirus disease has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT. Purpose To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performances. Materials and Methods In this retrospective and multi-center study, a deep learning model, COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT exams for the detection of COVID-19. Community acquired pneumonia (CAP) and other non-pneumonia CT exams were included to test the robustness of the model. The datasets were collected from 6 hospitals between August 2016 and February 2020. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity and specificity. Results The collected dataset consisted of 4356 chest CT exams from 3,322 patients. The average age is 49±15 years and there were slightly more male patients than female (1838 vs 1484; p-value=0.29). The per-exam sensitivity and specificity for detecting COVID-19 in the independent test set was 114 of 127 (90% [95% CI: 83%, 94%]) and 294 of 307 (96% [95% CI: 93%, 98%]), respectively, with an AUC of 0.96 (p-value<0.001). The per-exam sensitivity and specificity for detecting CAP in the independent test set was 87% (152 of 175) and 92% (239 of 259), respectively, with an AUC of 0.95 (95% CI: 0.93, 0.97). Conclusions A deep learning model can accurately detect COVID-19 and differentiate it from community acquired pneumonia and other lung diseases.

Li Lin, Qin Lixin, Xu Zeguo, Yin Youbing, Wang Xin, Kong Bin, Bai Junjie, Lu Yi, Fang Zhenghan, Song Qi, Cao Kunlin, Liu Daliang, Wang Guisheng, Xu Qizhong, Fang Xisheng, Zhang Shiqin, Xia Juan, Xia Jun

2020-Mar-19

Public Health Public Health

Russian Twitter Accounts and the Partisan Polarization of Vaccine Discourse, 2015-2017.

In American journal of public health ; h5-index 90.0

Objectives. To understand how Twitter accounts operated by the Russian Internet Research Agency (IRA) discussed vaccines to increase the credibility of their manufactured personas.Methods. We analyzed 2.82 million tweets published by 2689 IRA accounts between 2015 and 2017. Combining unsupervised machine learning and network analysis to identify "thematic personas" (i.e., accounts that consistently share the same topics), we analyzed the ways in which each discussed vaccines.Results. We found differences in volume and valence of vaccine-related tweets among 9 thematic personas. Pro-Trump personas were more likely to express antivaccine sentiment. Anti-Trump personas expressed support for vaccination. Others offered a balanced valence, talked about vaccines neutrally, or did not tweet about vaccines.Conclusions. IRA-operated accounts discussed vaccines in manners consistent with fabricated US identities.Public Health Implications. IRA accounts discussed vaccines online in ways that evoked political identities. This could exacerbate recently emerging partisan gaps relating to vaccine misinformation, as differently valenced messages were targeted at different segments of the US public. These sophisticated targeting efforts, if repeated and increased in reach, could reduce vaccination rates and magnify health disparities. (Am J Public Health. Published online ahead of print March 19, 2020: e1-e7. doi:10.2105/AJPH.2019.305564).

Walter Dror, Ophir Yotam, Jamieson Kathleen Hall

2020-Mar-19

General General

Practical Deep-Learning Representation for Fast Heterogeneous Catalyst Screening.

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

The binding site and energy is an invaluable descriptor in high-throughput screening of catalysts, as it is accessible and correlates with the activity and selectivity. Recently, comprehensive binding energy prediction machine-learning models have been demonstrated and promise to accelerate the catalyst screening. Here, we present a simple and versatile representation, applicable to any deep-learning models, to further accelerate such process. Our approach involves labeling the binding site atoms of the unrelaxed bare surface geometry, hence, for the model application, DFT calculations can be completely removed if the optimized bulk structure is available as is the case when using the Materials Project database. In addition, we present ensemble learning, where a set of predictions are used together to form a predictive distribution which reduces the model bias. We apply the labeled site approach and ensemble to crystal graph convolutional neural network and the ~40,000 data set of alloy catalysts for CO2 reduction. The proposed model applied to the latter data set of unrelaxed structures shows 0.116 and 0.085 eV mean absolute error respectively for CO and H binding energy, better than the best method (0.13 and 0.13 eV) in the literature that requires costly geometry relaxations. The analysis of the model parameters demonstrates that the model can effectively learn the chemical information related to the binding site.

Gu Geun Ho, Noh Juhwan, Kim Sungwon, Back Seoin, Ulissi Zachary W, Jung Yousung

2020-Mar-19

oncology Oncology

Real world study for the concordance between IBM Watson for Oncology and clinical practice in advanced non-small cell lung cancer patients at a lung cancer center in China.

In Thoracic cancer

BACKGROUND : IBM Watson for Oncology (WFO) provides physicians with evidence-based treatment options. This study was designed to explore the concordance of the suggested therapeutic regimen for advanced non-small cell lung (NSCLC) cancer patients between the updated version of WFO and physicians in our department, in order to reflect the differences of cancer treatment between China and the United States.

METHODS : Retrospective data from 165 patients with advanced NSCLC from September 2014 to March 2018 were entered manually into WFO. WFO recommendations were provided in three categories: recommended, for consideration, and not recommended. Concordance was analyzed by comparing the treatment decisions proposed by WFO with the real treatment. Potential influenced factors were also analyzed.

RESULTS : Overall, the treatment recommendations were concordant in 73.3% (121/165) of cases. When two alternative drugs such as icotinib and nedaplatin were included as "for consideration," the total consistency could be elevated from 73.3% to 90.3%(149/165). The logistic regression analysis showed that gender (P = 0.096), ECOG (P = 0.0.502), smoking (P = 0.455), and pathology (P = 0.633) had no effect on consistency, but stages (P = 0.019), including stage ≤III (77.8%, 21/27) and stage IV (93.5%, 129/138) had significant effects on consistency.

CONCLUSIONS : In China, most of the treatment recommendations of WFO are consistent with the real world treatment. Factors such as patient preferences, prices, drug approval and medical insurance are also taken into consideration, and they ultimately affect the inconsistency. To be comprehensively and rapidly applied in China, localization needs to be accelerated by WFO.

Yao Shuyang, Wang Ruotian, Qian Kun, Zhang Yi

2020-Mar-19

Advanced disease, Watson for Oncology, artificial intelligence, concordance, non-small cell lung cancer

General General

Prediction of cognitive performance in old age from spatial probability maps of white matter lesions.

In Aging ; h5-index 49.0

The purposes of this study were to explore the association between cognitive performance and white matter lesions (WMLs), and to investigate whether it is possible to predict cognitive impairment using spatial maps of WMLs. These WML maps were produced for 263 elders from the OASIS-3 dataset, and a relevance vector regression (RVR) model was applied to predict neuropsychological performance based on the maps. The association between the spatial distribution of WMLs and cognitive function was examined using diffusion tensor imaging data. WML burden significantly associated with increasing age (r=0.318, p<0.001) and cognitive decline. Eight of 15 neuropsychological measures could be accurately predicted, and the mini-mental state examination (MMSE) test achieved the highest predictive accuracy (CORR=0.28, p<0.003). WMLs located in bilateral tapetum, posterior corona radiata, and thalamic radiation contributed the most prediction power. Diffusion indexes in these regions associated significantly with cognitive performance (axial diffusivity>radial diffusivity>mean diffusivity>fractional anisotropy). These results show that the combination of the extent and location of WMLs exhibit great potential to serve as a generalizable marker of multidomain neurocognitive decline in the aging population. The results may also shed light on the mechanism underlying white matter changes during the progression of cognitive decline and aging.

Zhao Cui, Liang Ying, Chen Ting, Zhong Yihua, Li Xianglong, Wei Jing, Li Chunlin, Zhang Xu

2020-Mar-19

aging, cognition, machine learning, white matter lesions (WMLs)

General General

A predictive model for paediatric autism screening.

In Health informatics journal ; h5-index 25.0

Autism spectrum disorder is an umbrella term for a group of neurodevelopmental disorders that is associated with impairments to social interaction, communication, and behaviour. Typically, autism spectrum disorder is first detected with a screening tool (e.g. modified checklist for autism in toddlers). However, the interpretation of autism spectrum disorder behavioural symptoms varies across cultures: the sensitivity of modified checklist for autism in toddlers is as low as 25 per cent in Sri Lanka. A culturally sensitive screening tool called pictorial autism assessment schedule has overcome this problem. Low- and middle-income countries have a shortage of mental health specialists, which is a key barrier for obtaining an early autism spectrum disorder diagnosis. Early identification of autism spectrum disorder enables intervention before atypical patterns of behaviour and brain function become established. This article proposes a culturally sensitive autism spectrum disorder screening mobile application. The proposed application embeds an intelligent machine learning model and uses a clinically validated symptom checklist to monitor and detect autism spectrum disorder in low- and middle-income countries for the first time. Machine learning models were trained on clinical pictorial autism assessment schedule data and their predictive performance was evaluated, which demonstrated that the random forest was the optimal classifier (area under the receiver operating characteristic (0.98)) for embedding into the mobile screening tool. In addition, feature selection demonstrated that many pictorial autism assessment schedule questions are redundant and can be removed to optimise the screening process.

Wingfield Benjamin, Miller Shane, Yogarajah Pratheepan, Kerr Dermot, Gardiner Bryan, Seneviratne Sudarshi, Samarasinghe Pradeepa, Coleman Sonya

2020-Mar-19

autism spectrum disorder, decision support system, machine learning

General General

Machine learning in mass spectrometry: A MALDI-TOF MS approach to phenotypic antibacterial screening.

In Journal of medicinal chemistry ; h5-index 88.0

Machine learning techniques can be applied to MALDI-TOF mass spectral data of drug-treated cells to obtain classification models which assign the mechanism of action of drugs. Here we present an example application of this concept to the screening of antibacterial drugs that act at the major bacterial target sites, such as the ribosome, penicillin-binding proteins and topoisomerases in a pharmacologically relevant phenotypic setting. We show that antibacterial effects can be identified and classified in a label-free, high-throughput manner using wild-type Escherichia coli and Staphylococcus aureus cells at variable levels of target engagement. This phenotypic approach, which combines mass spectrometry and machine learning, therefore denoted as PhenoMS-ML, may prove useful for the identification and development of novel antibacterial compounds and other pharmacological agents.

van Oosten Luuk Nico, Klein Christian D

2020-Mar-19

Dermatology Dermatology

Early detection of melanoma: a consensus report from the Australian Skin and Skin Cancer Research Centre Melanoma Screening Summit.

In Australian and New Zealand journal of public health

INTRODUCTION : A Melanoma Screening Summit was held in Brisbane, Australia, to review evidence regarding current approaches for early detection of melanomas and explore new opportunities.

RESULTS : Formal population-based melanoma screening is not carried out in Australia, but there is evidence of considerable opportunistic screening as well as early detection. Biopsy rates are rising and most melanomas are now diagnosed when in situ. Based on evidence review and expert opinion, the Summit attendees concluded that there is currently insufficient information in terms of comparative benefits, harms and costs to support change from opportunistic to systematic screening. Assessment of gains in precision and cost-effectiveness of integrating total body imaging, artificial intelligence algorithms and genetic risk information is required, as well as better understanding of clinical and molecular features of thin fatal melanomas.

CONCLUSIONS : Research is needed to understand how to further optimise early detection of melanoma in Australia. Integrating risk-based population stratification and more precise diagnostic tests is likely to improve the balance of benefits and harms of opportunistic screening, pending assessment of cost-effectiveness. Implications for public health: The Summit Group identified that the personal and financial costs to the community of detecting and treating melanoma are rising, and this may be mitigated by developing and implementing a more systematic process for diagnosing melanoma.

Janda Monika, Cust Anne E, Neale Rachel E, Aitken Joanne F, Baade Peter D, Green Adele C, Khosrotehrani Kiarash, Mar Victoria, Soyer H Peter, Whiteman David C

2020-Mar-19

early detection, melanoma, prevention, screening, skin cancer

Radiology Radiology

Emerging MRI Techniques to Redefine Treatment Response in Patients With Glioblastoma.

In Journal of magnetic resonance imaging : JMRI

Glioblastoma is the most common and most malignant primary brain tumor. Despite aggressive multimodal treatment, its prognosis remains poor. Even with continuous developments in MRI, which has provided us with newer insights into the diagnosis and understanding of tumor biology, response assessment in the posttherapy setting remains challenging. We believe that the integration of additional information from advanced neuroimaging techniques can further improve the diagnostic accuracy of conventional MRI. In this article, we review the utility of advanced neuroimaging techniques such as diffusion-weighted imaging, diffusion tensor imaging, perfusion-weighted imaging, proton magnetic resonance spectroscopy, and chemical exchange saturation transfer in characterizing and evaluating treatment response in patients with glioblastoma. We will also discuss the existing challenges and limitations of using these techniques in clinical settings and possible solutions to avoiding pitfalls in study design, data acquisition, and analysis for future studies. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 3.

Gonçalves Fabrício Guimarães, Chawla Sanjeev, Mohan Suyash

2020-Mar-19

arterial spin labeling, artificial intelligence, chemical exchange saturation transfer, diffusion weighted imaging, dynamic contrast enhancement, dynamic susceptibility contrast, glioblastoma, perfusion weighted imaging, proton MR spectroscopy

General General

The Impact of Natural Compounds on S-Shaped Aβ42 Fibril: From Molecular Docking to Biophysical Characterization.

In International journal of molecular sciences ; h5-index 102.0

The pursuit for effective strategies inhibiting the amyloidogenic process in neurodegenerative disorders, such as Alzheimer's disease (AD), remains one of the main unsolved issues, and only a few drugs have demonstrated to delay the degeneration of the cognitive system. Moreover, most therapies induce severe side effects and are not effective at all stages of the illness. The need to find novel and reliable drugs appears therefore of primary importance. In this context, natural compounds have shown interesting beneficial effects on the onset and progression of neurodegenerative diseases, exhibiting a great inhibitory activity on the formation of amyloid aggregates and proving to be effective in many preclinical and clinical studies. However, their inhibitory mechanism is still unclear. In this work, ensemble docking and molecular dynamics simulations on S-shaped Aβ42 fibrils have been carried out to evaluate the influence of several natural compounds on amyloid conformational behaviour. A deep understanding of the interaction mechanisms between natural compounds and Aβ aggregates may play a key role to pave the way for design, discovery and optimization strategies toward an efficient destabilization of toxic amyloid assemblies.

Muscat Stefano, Pallante Lorenzo, Stojceski Filip, Danani Andrea, Grasso Gianvito, Deriu Marco Agostino

2020-Mar-16

Alzheimer’s disease, Amyloid β, S-shape, ensemble docking, molecular dynamics, natural compounds

General General

AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data.

In Journal of medical systems ; h5-index 48.0

The novel coronavirus (COVID-19) outbreak, which was identified in late 2019, requires special attention because of its future epidemics and possible global threats. Beside clinical procedures and treatments, since Artificial Intelligence (AI) promises a new paradigm for healthcare, several different AI tools that are built upon Machine Learning (ML) algorithms are employed for analyzing data and decision-making processes. This means that AI-driven tools help identify COVID-19 outbreaks as well as forecast their nature of spread across the globe. However, unlike other healthcare issues, for COVID-19, to detect COVID-19, AI-driven tools are expected to have active learning-based cross-population train/test models that employs multitudinal and multimodal data, which is the primary purpose of the paper.

Santosh K C

2020-Mar-18

Active learning, Artificial intelligence, COVID-19, Cross-population train/test models, Machine learning, Multitudinal and multimodal data

General General

Improving Irregularly Sampled Time Series Learning with Dense Descriptors of Time

ArXiv Preprint

Supervised learning with irregularly sampled time series have been a challenge to Machine Learning methods due to the obstacle of dealing with irregular time intervals. Some papers introduced recently recurrent neural network models that deals with irregularity, but most of them rely on complex mechanisms to achieve a better performance. This work propose a novel method to represent timestamps (hours or dates) as dense vectors using sinusoidal functions, called Time Embeddings. As a data input method it and can be applied to most machine learning models. The method was evaluated with two predictive tasks from MIMIC III, a dataset of irregularly sampled time series of electronic health records. Our tests showed an improvement to LSTM-based and classical machine learning models, specially with very irregular data.

Rafael T. Sousa, Lucas A. Pereira, Anderson S. Soares

2020-03-20

Public Health Public Health

Reducing False-Positive Results in Newborn Screening Using Machine Learning.

In International journal of neonatal screening

Newborn screening (NBS) for inborn metabolic disorders is a highly successful public health program that by design is accompanied by false-positive results. Here we trained a Random Forest machine learning classifier on screening data to improve prediction of true and false positives. Data included 39 metabolic analytes detected by tandem mass spectrometry and clinical variables such as gestational age and birth weight. Analytical performance was evaluated for a cohort of 2777 screen positives reported by the California NBS program, which consisted of 235 confirmed cases and 2542 false positives for one of four disorders: glutaric acidemia type 1 (GA-1), methylmalonic acidemia (MMA), ornithine transcarbamylase deficiency (OTCD), and very long-chain acyl-CoA dehydrogenase deficiency (VLCADD). Without changing the sensitivity to detect these disorders in screening, Random Forest-based analysis of all metabolites reduced the number of false positives for GA-1 by 89%, for MMA by 45%, for OTCD by 98%, and for VLCADD by 2%. All primary disease markers and previously reported analytes such as methionine for MMA and OTCD were among the top-ranked analytes. Random Forest's ability to classify GA-1 false positives was found similar to results obtained using Clinical Laboratory Integrated Reports (CLIR). We developed an online Random Forest tool for interpretive analysis of increasingly complex data from newborn screening.

Peng Gang, Tang Yishuo, Cowan Tina M, Enns Gregory M, Zhao Hongyu, Scharfe Curt

2020-Mar

Random Forest, false positive, inborn metabolic disorders, machine learning, newborn screening, second-tier testing, tandem mass spectrometry

General General

ADAPTIVE-NET: deep computed tomography reconstruction network with analytical domain transformation knowledge.

In Quantitative imaging in medicine and surgery

Background : Recently, the paradigm of computed tomography (CT) reconstruction has shifted as the deep learning technique evolves. In this study, we proposed a new convolutional neural network (called ADAPTIVE-NET) to perform CT image reconstruction directly from a sinogram by integrating the analytical domain transformation knowledge.

Methods : In the proposed ADAPTIVE-NET, a specific network layer with constant weights was customized to transform the sinogram onto the CT image domain via analytical back-projection. With this new framework, feature extractions were performed simultaneously on both the sinogram domain and the CT image domain. The Mayo low dose CT (LDCT) data was used to validate the new network. In particular, the new network was compared with the previously proposed residual encoder-decoder (RED)-CNN network. For each network, the mean square error (MSE) loss with and without VGG-based perceptual loss was compared. Furthermore, to evaluate the image quality with certain metrics, the noise correlation was quantified via the noise power spectrum (NPS) on the reconstructed LDCT for each method.

Results : CT images that have clinically relevant dimensions of 512×512 can be easily reconstructed from a sinogram on a single graphics processing unit (GPU) with moderate memory size (e.g., 11 GB) by ADAPTIVE-NET. With the same MSE loss function, the new network is able to generate better results than the RED-CNN. Moreover, the new network is able to reconstruct natural looking CT images with enhanced image quality if jointly using the VGG loss.

Conclusions : The newly proposed end-to-end supervised ADAPTIVE-NET is able to reconstruct high-quality LDCT images directly from a sinogram.

Ge Yongshuai, Su Ting, Zhu Jiongtao, Deng Xiaolei, Zhang Qiyang, Chen Jianwei, Hu Zhanli, Zheng Hairong, Liang Dong

2020-Feb

CT reconstruction, Computed tomography (CT), convolutional neural network (CNN), domain transformation, low dose CT reconstruction

Radiology Radiology

Development and assessment of an individualized nomogram to predict colorectal cancer liver metastases.

In Quantitative imaging in medicine and surgery

Background : This article aims to develop and assess the radiomics paradigm for predicting colorectal cancer liver metastasis (CRLM) from the primary tumor.

Methods : This retrospective study included 100 patients from the First Hospital of Jilin University from June 2017 to December 2017. The 100 patients comprised 50 patients with and 50 without CRLM. The maximum-level enhanced computed tomography (CT) image of primary cancer in the portal venous phase of each patient was selected as the original image data. To automatically implement radiomics-related paradigms, we developed a toolkit called Radiomics Intelligent Analysis Toolkit (RIAT).

Results : With RIAT, the model based on logistic regression (LR) using both the radiomics and clinical information signatures showed the maximum net benefit. The area under the curve (AUC) value was 0.90±0.02 (sensitivity =0.85±0.02, specificity =0.79±0.04) for the training set, 0.86±0.11 (sensitivity =0.85±0.09, specificity =0.75±0.19) for the verification set, 0.906 (95% CI, 0.840-0.971; sensitivity =0.81, specificity =0.84) for the cross-validation set, and 0.899 (95% CI, 0.761-1.000; sensitivity =0.78, specificity =0.91) for the test set.

Conclusions : The radiomics nomogram-based LR with clinical risk and radiomics features allows for a more accurate classification of CRLM using CT images with RIAT.

Li Mingyang, Li Xueyan, Guo Yu, Miao Zheng, Liu Xiaoming, Guo Shuxu, Zhang Huimao

2020-Feb

Radiomics, colorectal cancer liver metastasis (CRLM), machine learning, nomogram

Radiology Radiology

Radiomics prediction model for the improved diagnosis of clinically significant prostate cancer on biparametric MRI.

In Quantitative imaging in medicine and surgery

Background : To evaluate the potential of clinical-based model, a biparametric MRI-based radiomics model and a clinical-radiomics combined model for predicting clinically significant prostate cancer (PCa).

Methods : In total, 381 patients with clinically suspicious PCa were included in this retrospective study; of those, 199 patients did not have PCa upon biopsy, while 182 patients had PCa. All patients underwent 3.0-T MRI examinations with the same acquisition parameters, and clinical risk factors associated with PCa (age, prostate volume, serum PSA, etc.) were collected. We randomly stratified the training and test sets using a 6:4 ratio. The radiomic features included gradient-based histogram features, grey-level co-occurrence matrix (GLCM), run-length matrix (RLM), and grey-level size zone matrix (GLSZM). Three models were developed using multivariate logistic regression analysis to predict clinically significant PCa: a clinical model, a radiomics model and a clinical-radiomics combined model. The diagnostic performance and clinical net benefit of each model were compared via receiver operating characteristic (ROC) curve analysis and decision curves, respectively.

Results : Both the radiomics model (AUC: 0.98) and the clinical-radiomics combined model (AUC: 0.98) achieved greater predictive efficacy than the clinical model (AUC: 0.79). The decision curve analysis also showed that the radiomics model and combined model had higher net benefits than the clinical model.

Conclusions : Compared with the evaluation of clinical risk factors associated with PCa only, the radiomics-based machine learning model can improve the predictive accuracy for clinically significant PCa, in terms of both diagnostic performance and clinical net benefit.

Li Mengjuan, Chen Tong, Zhao Wenlu, Wei Chaogang, Li Xiaobo, Duan Shaofeng, Ji Libiao, Lu Zhihua, Shen Junkang

2020-Feb

Prostate cancer, classification, clinical risk factors, machine learning, radiomics

Pathology Pathology

MyoNet: A Transfer-Learning-Based LRCN for Lower Limb Movement Recognition and Knee Joint Angle Prediction for Remote Monitoring of Rehabilitation Progress From sEMG.

In IEEE journal of translational engineering in health and medicine

The clinical assessment technology such as remote monitoring of rehabilitation progress for lower limb related ailments rely on the automatic evaluation of movement performed along with an estimation of joint angle information. In this paper, we introduce a transfer-learning based Long-term Recurrent Convolution Network (LRCN) named as 'MyoNet' for the classification of lower limb movements, along with the prediction of the corresponding knee joint angle. The model consists of three blocks- (i) feature extractor block, (ii) joint angle prediction block, and (iii) movement classification block. Initially, the model is end-to-end trained for knee joint angle prediction followed by transferring the knowledge of a trained model to the movement classification through transfer-learning approach making a memory and computationally efficient design. The proposed MyoNet was evaluated on publicly available University of California (UC) Irvine machine learning repository dataset of the lower limb for 11 healthy subjects and 11 subjects with knee pathology for three movements type-walking, standing with knee flexion movements and sitting with knee extension movements. The average mean absolute error (MAE) resulted in the prediction of joint angle for healthy subjects and subjects with knee pathology are 8.1 % and 9.2 % respectively. Subsequently, an average classification accuracy of 98.1 % and 92.4 % were achieved for healthy subjects and subjects with knee pathology, respectively. Interestingly, the significance of this study in itself is promising with substantial improvement in the performance compared to state-of-the-art methodologies. The clinical significance of such surface electromyography signals (sEMG) based movement recognition and prediction of corresponding joint angle system could be beneficial for remote monitoring of rehabilitation progress by the physiotherapist using wearables.

Gautam Arvind, Panwar Madhuri, Biswas Dwaipayan, Acharyya Amit

2020

CNN, LSTM, joint angle prediction, movement classification, sEMG, signal processing, transfer learning

General General

The Mortality and Medical Costs of Air Pollution: Evidence from Changes in Wind Direction.

In The American economic review

We estimate the causal effects of acute fine particulate matter exposure on mortality, health care use, and medical costs among the US elderly using Medicare data. We instrument for air pollution using changes in local wind direction and develop a new approach that uses machine learning to estimate the life-years lost due to pollution exposure. Finally, we characterize treatment effect heterogeneity using both life expectancy and generic machine learning inference. Both approaches find that mortality effects are concentrated in about 25 percent of the elderly population.

Deryugina Tatyana, Heutel Garth, Miller Nolan H, Molitor David, Reif Julian

2019-Dec

I12, J14, Q51, Q53

General General

Childhood Maltreatment, Automatic Negative Thoughts, and Resilience: The Protective Roles of Culture and Genes.

In Journal of interpersonal violence

Resilience, a psychological trait conceptualized as the ability to recover from setbacks, can be weakened by childhood maltreatment, which comprises childhood abuse and childhood neglect. The current study aimed to investigate whether childhood maltreatment could increase automatic negative thoughts (ANT), thus weakening resilience. Furthermore, as psychological characteristics are commonly subject to the moderating effects of cultural context and biology, the study also explored whether and how cultural and genetic factors separately interact with childhood maltreatment to predict resilience. In study 1, the participants comprised 237 American and 347 Chinese individuals; study 2 included 428 genotyped Chinese individuals. We combined regression, mediation, moderation, and machine learning methods to test the mediating effect of ANT on the link between childhood maltreatment and resilience as well as the moderating roles of culture and genetics. Study 1 found that both childhood abuse and childhood neglect increased ANT and thus weakened resilience. In addition, the ANT-mediating effects of childhood neglect were stronger in American than Chinese participants. In Study 2, using the leave-one-out approach, we constructed two separate prediction models based on 22 and 16 important single nucleotide polymorphisms (SNPs), and we found that the interaction between childhood abuse/neglect and polygenic scores based on important SNPs could predict ANT. The mediating effects of ANT on the relationship between childhood abuse/neglect and resilience were significant for participants with low polygenic scores but not for those with high polygenic scores. In conclusion, both the cultural environment and individual genetic makeup moderated the mediating effects of ANT on the association between childhood maltreatment and resilience. These findings indicated the roles of culture and genetics in protecting against the adverse effects of childhood maltreatment on resilience.

Yu Meihua, Huang Lingling, Mao Jiaqi, Dna Gese, Luo Siyang

2020-Mar-19

automatic negative thoughts, childhood maltreatment, culture, genetics, resilience

Cardiology Cardiology

The relationship between atypical antipsychotics drugs and cardiac arrhythmias: implications for clinical use.

In Expert opinion on drug safety

Introduction: Increased mortality has been observed in patients with mental health disorders. Specifically, exposure to antipsychotic medications conveys a greater than 2 fold risk of sudden death, thought to be mediated through effects on QT prolongation and risk of torsades de pointes.Areas Covered: Authors review the association between antipsychotic drugs and sudden cardiac death, the physiologic basis for these associations, assessment of patients at risk, and strategies to minimize risk of sudden cardiac death.Expert Opinion: Despite the prevalence of antipsychotic medication use for many decades, there remain considerable challenges in reducing the associated risk of sudden cardiac death. A structured algorithm that incorporates patient clinical factors and antipsychotic drug factors may improve risk assessment and reduce the risk of adverse cardiac events. Future advancements in genetics and artificial intelligence may allow for enhanced risk stratification and predicting response (efficacy and adverse effects) to therapy.

Ruiz Diaz Juan Carlos, Frenkel Daniel, Aronow Wilbert S

2020-Mar-19

Antipsychotics, QT interval prolongation, arrhythmias, atypical antipsychotics, first generation, second generation, sudden cardiac death, torsades de pointes, typical antipsychotics

General General

Combining rapid 2D NMR experiments with novel pre-processing workflows and MIC quality measures for metabolomics.

In Metabolomics : Official journal of the Metabolomic Society

INTRODUCTION : The use of 2D NMR data sources (COSY in this paper) allows to reach general metabolomics results which are at least as good as the results obtained with 1D NMR data, and this with a less advanced and less complex level of pre-processing. But a major issue still exists and can largely slow down a generalized use of 2D data sources in metabolomics: the experiment duration.

OBJECTIVE : The goal of this paper is to overcome the experiment duration issue in our recently published MIC strategy by considering faster 2D COSY acquisition techniques: a conventional COSY with a reduced number of transients and the use of the Non-Uniform Sampling (NUS) method. These faster alternatives are all submitted to novel 2D pre-processing workflows and to Metabolomic Informative Content analyses. Eventually, results are compared to those obtained with conventional COSY spectra.

METHODS : To pre-process the 2D data sources, the Global Peak List (GPL) workflow and the Vectorization workflow are used. To compare this data sources and to detect the more informative one(s), MIC (Metabolomic Informative Content) indexes are used, based on clustering and inertia measures of quality.

RESULTS : Results are discussed according to a multi-factor experimental design (which is unsupervised and based on human urine samples). Descriptive PCA results and MIC indexes are shown, leading to the direct and objective comparison of the different data sets.

CONCLUSION : In conclusion, it is demonstrated that conventional COSY spectra recorded with only one transient per increment and COSY spectra recorded with 50% of non-uniform sampling provide very similar MIC results as the initial COSY recorded with four transients, but in a much shorter time. Consequently, using techniques like the reduction of the number of transients or NUS can really open the door to a potential high-throughput use of 2D COSY spectra in metabolomics.

Féraud Baptiste, Martineau Estelle, Leenders Justine, Govaerts Bernadette, de Tullio Pascal, Giraudeau Patrick

2020-Mar-18

2D NMR, 2D pre-processing workflows, COSY spectra, Global peak list, Metabolomic informative content (MIC), Non-uniform sampling (NUS), Vectorization

General General

AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data.

In Journal of medical systems ; h5-index 48.0

The novel coronavirus (COVID-19) outbreak, which was identified in late 2019, requires special attention because of its future epidemics and possible global threats. Beside clinical procedures and treatments, since Artificial Intelligence (AI) promises a new paradigm for healthcare, several different AI tools that are built upon Machine Learning (ML) algorithms are employed for analyzing data and decision-making processes. This means that AI-driven tools help identify COVID-19 outbreaks as well as forecast their nature of spread across the globe. However, unlike other healthcare issues, for COVID-19, to detect COVID-19, AI-driven tools are expected to have active learning-based cross-population train/test models that employs multitudinal and multimodal data, which is the primary purpose of the paper.

Santosh K C

2020-Mar-18

Active learning, Artificial intelligence, COVID-19, Cross-population train/test models, Machine learning, Multitudinal and multimodal data

oncology Oncology

A convolutional neural network-based system to classify patients using FDG PET/CT examinations.

In BMC cancer

BACKGROUND : As the number of PET/CT scanners increases and FDG PET/CT becomes a common imaging modality for oncology, the demands for automated detection systems on artificial intelligence (AI) to prevent human oversight and misdiagnosis are rapidly growing. We aimed to develop a convolutional neural network (CNN)-based system that can classify whole-body FDG PET as 1) benign, 2) malignant or 3) equivocal.

METHODS : This retrospective study investigated 3485 sequential patients with malignant or suspected malignant disease, who underwent whole-body FDG PET/CT at our institute. All the cases were classified into the 3 categories by a nuclear medicine physician. A residual network (ResNet)-based CNN architecture was built for classifying patients into the 3 categories. In addition, we performed a region-based analysis of CNN (head-and-neck, chest, abdomen, and pelvic region).

RESULTS : There were 1280 (37%), 1450 (42%), and 755 (22%) patients classified as benign, malignant and equivocal, respectively. In the patient-based analysis, CNN predicted benign, malignant and equivocal images with 99.4, 99.4, and 87.5% accuracy, respectively. In region-based analysis, the prediction was correct with the probability of 97.3% (head-and-neck), 96.6% (chest), 92.8% (abdomen) and 99.6% (pelvic region), respectively.

CONCLUSION : The CNN-based system reliably classified FDG PET images into 3 categories, indicating that it could be helpful for physicians as a double-checking system to prevent oversight and misdiagnosis.

Kawauchi Keisuke, Furuya Sho, Hirata Kenji, Katoh Chietsugu, Manabe Osamu, Kobayashi Kentaro, Watanabe Shiro, Shiga Tohru

2020-Mar-17

Convolutional neural network, Deep learning, FDG, PET

General General

MethylNet: an automated and modular deep learning approach for DNA methylation analysis.

In BMC bioinformatics

BACKGROUND : DNA methylation (DNAm) is an epigenetic regulator of gene expression programs that can be altered by environmental exposures, aging, and in pathogenesis. Traditional analyses that associate DNAm alterations with phenotypes suffer from multiple hypothesis testing and multi-collinearity due to the high-dimensional, continuous, interacting and non-linear nature of the data. Deep learning analyses have shown much promise to study disease heterogeneity. DNAm deep learning approaches have not yet been formalized into user-friendly frameworks for execution, training, and interpreting models. Here, we describe MethylNet, a DNAm deep learning method that can construct embeddings, make predictions, generate new data, and uncover unknown heterogeneity with minimal user supervision.

RESULTS : The results of our experiments indicate that MethylNet can study cellular differences, grasp higher order information of cancer sub-types, estimate age and capture factors associated with smoking in concordance with known differences.

CONCLUSION : The ability of MethylNet to capture nonlinear interactions presents an opportunity for further study of unknown disease, cellular heterogeneity and aging processes.

Levy Joshua J, Titus Alexander J, Petersen Curtis L, Chen Youdinghuan, Salas Lucas A, Christensen Brock C

2020-Mar-17

DNA methylation, Deep learning, Embedding, High performance computing, Supervised, Transfer learning, Unsupervised, Workflow automation

General General

A deep learning-based framework for lung cancer survival analysis with biomarker interpretation.

In BMC bioinformatics

BACKGROUND : Lung cancer is the leading cause of cancer-related deaths in both men and women in the United States, and it has a much lower five-year survival rate than many other cancers. Accurate survival analysis is urgently needed for better disease diagnosis and treatment management.

RESULTS : In this work, we propose a survival analysis system that takes advantage of recently emerging deep learning techniques. The proposed system consists of three major components. 1) The first component is an end-to-end cellular feature learning module using a deep neural network with global average pooling. The learned cellular representations encode high-level biologically relevant information without requiring individual cell segmentation, which is aggregated into patient-level feature vectors by using a locality-constrained linear coding (LLC)-based bag of words (BoW) encoding algorithm. 2) The second component is a Cox proportional hazards model with an elastic net penalty for robust feature selection and survival analysis. 3) The third commponent is a biomarker interpretation module that can help localize the image regions that contribute to the survival model's decision. Extensive experiments show that the proposed survival model has excellent predictive power for a public (i.e., The Cancer Genome Atlas) lung cancer dataset in terms of two commonly used metrics: log-rank test (p-value) of the Kaplan-Meier estimate and concordance index (c-index).

CONCLUSIONS : In this work, we have proposed a segmentation-free survival analysis system that takes advantage of the recently emerging deep learning framework and well-studied survival analysis methods such as the Cox proportional hazards model. In addition, we provide an approach to visualize the discovered biomarkers, which can serve as concrete evidence supporting the survival model's decision.

Cui Lei, Li Hansheng, Hui Wenli, Chen Sitong, Yang Lin, Kang Yuxin, Bo Qirong, Feng Jun

2020-Mar-18

Cell detection, Deep learning, Feature learning, Survival analysis

General General

Ancient mitogenomes show plateau populations from last 5200 years partially contributed to present-day Tibetans.

In Proceedings. Biological sciences

The clarification of the genetic origins of present-day Tibetans requires an understanding of their past relationships with the ancient populations of the Tibetan Plateau. Here we successfully sequenced 67 complete mitochondrial DNA genomes of 5200 to 300-year-old humans from the plateau. Apart from identifying two ancient plateau lineages (haplogroups D4j1b and M9a1a1c1b1a) that suggest some ancestors of Tibetans came from low-altitude areas 4750 to 2775 years ago and that some were involved in an expansion of people moving between high-altitude areas 2125 to 1100 years ago, we found limited evidence of recent matrilineal continuity on the plateau. Furthermore, deep learning of the ancient data incorporated into simulation models with an accuracy of 97% supports that present-day Tibetan matrilineal ancestry received partial contribution rather than complete continuity from the plateau populations of the last 5200 years.

Ding Manyu, Wang Tianyi, Ko Albert Min-Shan, Chen Honghai, Wang Hui, Dong Guanghui, Lu Hongliang, He Wei, Wangdue Shargan, Yuan Haibing, He Yuanhong, Cai Linhai, Chen Zujun, Hou Guangliang, Zhang Dongju, Zhang Zhaoxia, Cao Peng, Dai Qingyan, Feng Xiaotian, Zhang Ming, Wang Hongru, Yang Melinda A, Fu Qiaomei

2020-Mar-25

Tibetan prehistory, ancient DNA, population genetics of humans

General General

Evaluation of Prevalence of the Sarcopenia Level Using Machine Learning Techniques: Case Study in Tijuana Baja California, Mexico.

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

The article presents a study based on timeline data analysis of the level of sarcopenia in older patients in Baja California, Mexico. Information was examined at the beginning of the study (first event), three months later (second event), and six months later (third event). Sarcopenia is defined as the loss of muscle mass quality and strength. The study was conducted with 166 patients. A total of 65% were women and 35% were men. The mean age of the enrolled patients was 77.24 years. The research included 99 variables that consider medical history, pharmacology, psychological tests, comorbidity (Charlson), functional capacity (Barthel and Lawton), undernourishment (mini nutritional assessment (MNA) validated test), as well as biochemical and socio-demographic data. Our aim was to evaluate the prevalence of the level of sarcopenia in a population of chronically ill patients assessed at the Tijuana General Hospital. We used machine learning techniques to assess and identify the determining variables to focus on the patients' evolution. The following classifiers were used: Support Vector Machines, Linear Support Vector Machines, Radial Basis Function, Gaussian process, Decision Tree, Random Forest, multilayer perceptron, AdaBoost, Gaussian Naive Bayes, and Quadratic Discriminant Analysis. In order of importance, we found that the following variables determine the level of sarcopenia: Age, Systolic arterial hypertension, mini nutritional assessment (MNA), Number of chronic diseases, and Sodium. They are therefore considered relevant in the decision-making process of choosing treatment or prevention. Analysis of the relationship between the presence of the variables and the classifiers used to measure sarcopenia revealed that the Decision Tree classifier, with the Age, Systolic arterial hypertension, MNA, Number of chronic diseases, and Sodium variables, showed a precision of 0.864, accuracy of 0.831, and an F1 score of 0.900 in the first and second events. Precision of 0.867, accuracy of 0.825, and an F1 score of 0.867 were obtained in event three with the same variables. We can therefore conclude that the Decision Tree classifier yields the best results for the assessment of the determining variables and suggests that the study population's sarcopenia did not change from moderate to severe.

Castillo-Olea Cristián, Garcia-Zapirain Soto Begonya, Zuñiga Clemente

2020-Mar-15

machine learning, prevalence, sarcopenia level

Pathology Pathology

Overinterpretation reveals image classification model pathologies

ArXiv Preprint

Image classifiers are typically scored on their test set accuracy, but high accuracy can mask a subtle type of model failure. We find that high scoring convolutional neural networks (CNN) exhibit troubling pathologies that allow them to display high accuracy even in the absence of semantically salient features. When a model provides a high-confidence decision without salient supporting input features we say that the classifier has overinterpreted its input, finding too much class-evidence in patterns that appear nonsensical to humans. Here, we demonstrate that state of the art neural networks for CIFAR-10 and ImageNet suffer from overinterpretation, and find CIFAR-10 trained models make confident predictions even when 95% of an input image has been masked and humans are unable to discern salient features in the remaining pixel subset. Although these patterns portend potential model fragility in real-world deployment, they are in fact valid statistical patterns of the image classification benchmark that alone suffice to attain high test accuracy. We find that ensembling strategies can help mitigate model overinterpretation, and classifiers which rely on more semantically meaningful features can improve accuracy over both the test set and out-of-distribution images from a different source than the training data.

Brandon Carter, Siddhartha Jain, Jonas Mueller, David Gifford

2020-03-19

General General

The Effects of Daily Sleep Condition on Performances of Physical Fitness among Taiwanese Adults: A Cross-Sectional Study.

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

Physical fitness is a powerful indicator of health. Sleep condition plays an essential role in maintaining quality of life and is an important marker that predicts physical fitness. This study aimed to determine the relationship between sleep conditions (sleep quality, sleep duration, bedtime) and multiple physical fitness indicators (body mass index (BMI), flexibility, abdominal muscle strength and endurance, cardiopulmonary endurance) in a well-characterized population of Taiwanese adults aged 23 to 65. The applied data were obtained from the National Physical Fitness Examination Survey 2014 conducted in Taiwan. We assessed the association of the sleep conditions with physical fitness performances in Taiwanese adults by using the multivariate adaptive regression spline (MARS) method with a total of 69,559 samples. The results show that sleep duration, sleep quality, and bedtime were statistically significant influence factors on physical fitness performances with different degrees. Gender was an important factor that affects the effects of daily sleep conditions on performances of physical fitness. Sleep duration was the most important factor as it was simultaneously correlated with BMI, sit-ups, and sit-and-reach indicators in both genders. Bedtime and sleep quality were only associated with sit-ups performance in both genders.

Hsu Chi-Chieh, Gu Ming, Lee Tian-Shyug, Lu Chi-Jie

2020-Mar-15

BMI, CEI, MARS, Sit-and-reach, Sit-ups, Sleep duration, Sleep quality

Surgery Surgery

VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation

ArXiv Preprint

Robotic fabric manipulation has applications in cloth and cable management, senior care, surgery and more. Existing fabric manipulation techniques, however, are designed for specific tasks, making it difficult to generalize across different but related tasks. We address this problem by extending the recently proposed Visual Foresight framework to learn fabric dynamics, which can be efficiently reused to accomplish a variety of different fabric manipulation tasks with a single goal-conditioned policy. We introduce VisuoSpatial Foresight (VSF), which extends prior work by learning visual dynamics on domain randomized RGB images and depth maps simultaneously and completely in simulation. We experimentally evaluate VSF on multi-step fabric smoothing and folding tasks both in simulation and on the da Vinci Research Kit (dVRK) surgical robot without any demonstrations at train or test time. Furthermore, we find that leveraging depth significantly improves performance for cloth manipulation tasks, and results suggest that leveraging RGBD data for video prediction and planning yields an 80% improvement in fabric folding success rate over pure RGB data. Supplementary material is available at https://sites.google.com/view/fabric-vsf/.

Ryan Hoque, Daniel Seita, Ashwin Balakrishna, Aditya Ganapathi, Ajay Kumar Tanwani, Nawid Jamali, Katsu Yamane, Soshi Iba, Ken Goldberg

2020-03-19

General General

Machine Learning to Identify Flexibility Signatures of Class A GPCR Inhibition.

In Biomolecules

We show that machine learning can pinpoint features distinguishing inactive from active states in proteins, in particular identifying key ligand binding site flexibility transitions in GPCRs that are triggered by biologically active ligands. Our analysis was performed on the helical segments and loops in 18 inactive and 9 active class A G protein-coupled receptors (GPCRs). These three-dimensional (3D) structures were determined in complex with ligands. However, considering the flexible versus rigid state identified by graph-theoretic ProFlex rigidity analysis for each helix and loop segment with the ligand removed, followed by feature selection and k-nearest neighbor classification, was sufficient to identify four segments surrounding the ligand binding site whose flexibility/rigidity accurately predicts whether a GPCR is in an active or inactive state. GPCRs bound to inhibitors were similar in their pattern of flexible versus rigid regions, whereas agonist-bound GPCRs were more flexible and diverse. This new ligand-proximal flexibility signature of GPCR activity was identified without knowledge of the ligand binding mode or previously defined switch regions, while being adjacent to the known transmission switch. Following this proof of concept, the ProFlex flexibility analysis coupled with pattern recognition and activity classification may be useful for predicting whether newly designed ligands behave as activators or inhibitors in protein families in general, based on the pattern of flexibility they induce in the protein.

Bemister-Buffington Joseph, Wolf Alex J, Raschka Sebastian, Kuhn Leslie A

2020-Mar-14

GPCR activity determinants, MLxtend, ProFlex, allostery, coupled residues, feature selection, flexibility analysis, pattern classification

Surgery Surgery

Stereo Endoscopic Image Super-Resolution Using Disparity-Constrained Parallel Attention

ArXiv Preprint

With the popularity of stereo cameras in computer assisted surgery techniques, a second viewpoint would provide additional information in surgery. However, how to effectively access and use stereo information for the super-resolution (SR) purpose is often a challenge. In this paper, we propose a disparity-constrained stereo super-resolution network (DCSSRnet) to simultaneously compute a super-resolved image in a stereo image pair. In particular, we incorporate a disparity-based constraint mechanism into the generation of SR images in a deep neural network framework with an additional atrous parallax-attention modules. Experiment results on laparoscopic images demonstrate that the proposed framework outperforms current SR methods on both quantitative and qualitative evaluations. Our DCSSRnet provides a promising solution on enhancing spatial resolution of stereo image pairs, which will be extremely beneficial for the endoscopic surgery.

Tianyi Zhang, Yun Gu, Xiaolin Huang, Enmei Tu, Jie Yang

2020-03-19

General General

Denoising of multi b-value diffusion-weighted MR images using deep image prior.

In Physics in medicine and biology

The clinical value of multiple b-value diffusion-weighted (DW) magnetic resonance imaging (MRI) has been shown in many studies. However, DW-MRI often suffers from low signal-to-noise ratio, especially at high b-values. To address this limitation, we present an image denoising method based on the concept of deep image prior (DIP). In this method, high-quality prior images obtained from the same patient were used as the network input, and all noisy DW images were used as the network output. Our aim is to denoise all b-value DW images simultaneously. By using early stopping, we expect the DIP-based model to learn the content of images instead of the noise. The performance of the proposed DIP method was evaluated using both simulated and real DW-MRI data. We simulated a digital phantom and generated noise-free DW-MRI data according to the intravoxel incoherent motion model. Different levels of Rician noise were then simulated. The proposed DIP method was compared with the image denoising method using local principal component analysis (LPCA). The simulation results show that the proposed DIP method outperforms the LPCA method in terms of mean-squared error and parameter estimation. The results of real DW-MRI data show that the proposed DIP method can improve the quality of IVIM parametric images. DIP is a feasible method for denoising multiple b-value DW-MRI data.

Lin Yu-Chun, Huang Hsuan-Ming

2020-Mar-18

deep learning, diffusion-weighted magnetic resonance imaging, image denoising

General General

Machine Learning Uncovers Food- and Excipient-Drug Interactions.

In Cell reports ; h5-index 119.0

Inactive ingredients and generally recognized as safe compounds are regarded by the US Food and Drug Administration (FDA) as benign for human consumption within specified dose ranges, but a growing body of research has revealed that many inactive ingredients might have unknown biological effects at these concentrations and might alter treatment outcomes. To speed up such discoveries, we apply state-of-the-art machine learning to delineate currently unknown biological effects of inactive ingredients-focusing on P-glycoprotein (P-gp) and uridine diphosphate-glucuronosyltransferase-2B7 (UGT2B7), two proteins that impact the pharmacokinetics of approximately 20% of FDA-approved drugs. Our platform identifies vitamin A palmitate and abietic acid as inhibitors of P-gp and UGT2B7, respectively; in silico, in vitro, ex vivo, and in vivo validations support these interactions. Our predictive framework can elucidate biological effects of commonly consumed chemical matter with implications on food- and excipient-drug interactions and functional drug formulation development.

Reker Daniel, Shi Yunhua, Kirtane Ameya R, Hess Kaitlyn, Zhong Grace J, Crane Evan, Lin Chih-Hsin, Langer Robert, Traverso Giovanni

2020-Mar-17

data science, drug delivery, excipient-drug interactions, food-drug interactions, inactive ingredients, machine learning, pharmacokinetics, pharmacology, virtual screening

General General

Precision non-implantable neuromodulation therapies: a perspective for the depressed brain.

In Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999)

Current first-line treatments for major depressive disorder (MDD) include pharmacotherapy and cognitive-behavioral therapy. However, one-third of depressed patients do not achieve remission after multiple medication trials, and psychotherapy can be costly and time-consuming. Although non-implantable neuromodulation (NIN) techniques such as transcranial magnetic stimulation, transcranial direct current stimulation, electroconvulsive therapy, and magnetic seizure therapy are gaining momentum for treating MDD, the efficacy of non-convulsive techniques is still modest, whereas use of convulsive modalities is limited by their cognitive side effects. In this context, we propose that NIN techniques could benefit from a precision-oriented approach. In this review, we discuss the challenges and opportunities in implementing such a framework, focusing on enhancing NIN effects via a combination of individualized cognitive interventions, using closed-loop approaches, identifying multimodal biomarkers, using computer electric field modeling to guide targeting and quantify dosage, and using machine learning algorithms to integrate data collected at multiple biological levels and identify clinical responders. Though promising, this framework is currently limited, as previous studies have employed small samples and did not sufficiently explore pathophysiological mechanisms associated with NIN response and side effects. Moreover, cost-effectiveness analyses have not been performed. Nevertheless, further advancements in clinical trials of NIN could shift the field toward a more "precision-oriented" practice.

Borrione Lucas, Bellini Helena, Razza Lais Boralli, Avila Ana G, Baeken Chris, Brem Anna-Katharine, Busatto Geraldo, Carvalho Andre F, Chekroud Adam, Daskalakis Zafiris J, Deng Zhi-De, Downar Jonathan, Gattaz Wagner, Loo Colleen, Lotufo Paulo A, Martin Maria da Graça M, McClintock Shawn M, O’Shea Jacinta, Padberg Frank, Passos Ives C, Salum Giovanni A, Vanderhasselt Marie-Anne, Fraguas Renerio, Benseñor Isabela, Valiengo Leandro, Brunoni Andre R

2020-Mar-16

General General

Prediction Model of Aryl Hydrocarbon Receptor Activation by a Novel QSAR Approach, DeepSnap-Deep Learning.

In Molecules (Basel, Switzerland)

The aryl hydrocarbon receptor (AhR) is a ligand-dependent transcription factor that senses environmental exogenous and endogenous ligands or xenobiotic chemicals. In particular, exposure of the liver to environmental metabolism-disrupting chemicals contributes to the development and propagation of steatosis and hepatotoxicity. However, the mechanisms for AhR-induced hepatotoxicity and tumor propagation in the liver remain to be revealed, due to the wide variety of AhR ligands. Recently, quantitative structure-activity relationship (QSAR) analysis using deep neural network (DNN) has shown superior performance for the prediction of chemical compounds. Therefore, this study proposes a novel QSAR analysis using deep learning (DL), called the DeepSnap-DL method, to construct prediction models of chemical activation of AhR. Compared with conventional machine learning (ML) techniques, such as the random forest, XGBoost, LightGBM, and CatBoost, the proposed method achieves high-performance prediction of AhR activation. Thus, the DeepSnap-DL method may be considered a useful tool for achieving high-throughput in silico evaluation of AhR-induced hepatotoxicity.

Matsuzaka Yasunari, Hosaka Takuomi, Ogaito Anna, Yoshinari Kouichi, Uesawa Yoshihiro

2020-Mar-13

DeepSnap, QSAR, aryl hydrocarbon receptor, chemical structure, deep learning, machine learning

General General

AQPDBJUT Dataset: Picture-Based PM Monitoring in the Campus of BJUT

ArXiv Preprint

Ensuring the students in good physical levels is imperative for their future health. In recent years, the continually growing concentration of Particulate Matter (PM) has done increasingly serious harm to student health. Hence, it is highly required to prevent and control PM concentrations in the campus. As the source of PM prevention and control, developing a good model for PM monitoring is extremely urgent and has posed a big challenge. It has been found in prior works that photobased methods are available for PM monitoring. To verify the effectiveness of existing PM monitoring methods in the campus, we establish a new dataset which includes 1,500 photos collected in the Beijing University of Technology. Experiments show that stated-of-the-art methods are far from ideal for PM monitoring in the campus.

Yonghui Zhang, Ke Gu

2020-03-19

General General

Crater detection from commercial satellite imagery to estimate unexploded ordnance in Cambodian agricultural land.

In PloS one ; h5-index 176.0

Unexploded ordnance (UXO) pose a significant threat to post-conflict communities, and current efforts to locate bombs rely on time-intensive and dangerous in-person enumeration. Very high resolution (VHR) sub-meter satellite images may offer a low-cost and high-efficiency approach to automatically detect craters and estimate UXO density. Machine-learning methods from the meteor crater literature are ill-suited to find bomb craters, which are smaller than meteor craters and have high appearance variation, particularly in spectral reflectance and shape, due to the complex terrain environment. A two-stage learning-based framework is created to address these challenges. First, a simple and loose statistical classifier based on histogram of oriented gradient (HOG) and spectral information is used for a first pass of crater recognition. In a second stage, a patch-dependent novel spatial feature is developed through dynamic mean-shift segmentation and SIFT descriptors. We apply the model to a multispectral WorldView-2 image of a Cambodian village, which was heavily bombed during the Vietnam War. The proposed method increased true bomb crater detection by over 160 percent. Comparative analysis demonstrates that our method significantly outperforms typical object-recognition algorithms and can be used for wide-area bomb crater detection. Our model, combined with declassified records and demining reports, suggests that 44 to 50 percent of the bombs in the vicinity of this particular Cambodian village may remain unexploded.

Lin Erin, Qin Rongjun, Edgerton Jared, Kong Deren

2020

General General

The Stochastic Delta Rule: Faster and More Accurate Deep Learning through Adaptive Weight Noise.

In Neural computation

Multilayer neural networks have led to remarkable performance on many kinds of benchmark tasks in text, speech, and image processing. Nonlinear parameter estimation in hierarchical models is known to be subject to overfitting and misspecification. One approach to these estimation and related problems (e.g., saddle points, colinearity, feature discovery) is called Dropout. The Dropout algorithm removes hidden units according to a binomial random variable with probability p prior to each update, creating random "shocks" to the network that are averaged over updates (thus creating weight sharing). In this letter, we reestablish an older parameter search method and show that Dropout is a special case of this more general model, stochastic delta rule (SDR), published originally in 1990. Unlike Dropout, SDR redefines each weight in the network as a random variable with mean μ w i j and standard deviation σ w i j . Each weight random variable is sampled on each forward activation, consequently creating an exponential number of potential networks with shared weights (accumulated in the mean values). Both parameters are updated according to prediction error, thus resulting in weight noise injections that reflect a local history of prediction error and local model averaging. SDR therefore implements a more sensitive local gradient-dependent simulated annealing per weight converging in the limit to a Bayes optimal network. We run tests on standard benchmarks (CIFAR and ImageNet) using a modified version of DenseNet and show that SDR outperforms standard Dropout in top-5 validation error by approximately 13% with DenseNet-BC 121 on ImageNet and find various validation error improvements in smaller networks. We also show that SDR reaches the same accuracy that Dropout attains in 100 epochs in as few as 40 epochs, as well as improvements in training error by as much as 80%.

Frazier-Logue Noah, Hanson Stephen José

2020-Mar-18

General General

Equivalence Projective Simulation as a Framework for Modeling Formation of Stimulus Equivalence Classes.

In Neural computation

Stimulus equivalence (SE) and projective simulation (PS) study complex behavior, the former in human subjects and the latter in artificial agents. We apply the PS learning framework for modeling the formation of equivalence classes. For this purpose, we first modify the PS model to accommodate imitating the emergence of equivalence relations. Later, we formulate the SE formation through the matching-to-sample (MTS) procedure. The proposed version of PS model, called the equivalence projective simulation (EPS) model, is able to act within a varying action set and derive new relations without receiving feedback from the environment. To the best of our knowledge, it is the first time that the field of equivalence theory in behavior analysis has been linked to an artificial agent in a machine learning context. This model has many advantages over existing neural network models. Briefly, our EPS model is not a black box model, but rather a model with the capability of easy interpretation and flexibility for further modifications. To validate the model, some experimental results performed by prominent behavior analysts are simulated. The results confirm that the EPS model is able to reliably simulate and replicate the same behavior as real experiments in various settings, including formation of equivalence relations in typical participants, nonformation of equivalence relations in language-disabled children, and nodal effect in a linear series with nodal distance five. Moreover, through a hypothetical experiment, we discuss the possibility of applying EPS in further equivalence theory research.

Mofrad Asieh Abolpour, Yazidi Anis, Hammer Hugo L, Arntzen Erik

2020-Mar-18

General General

A Survey on Deep Learning for Multimodal Data Fusion.

In Neural computation

With the wide deployments of heterogeneous networks, huge amounts of data with characteristics of high volume, high variety, high velocity, and high veracity are generated. These data, referred to multimodal big data, contain abundant intermodality and cross-modality information and pose vast challenges on traditional data fusion methods. In this review, we present some pioneering deep learning models to fuse these multimodal big data. With the increasing exploration of the multimodal big data, there are still some challenges to be addressed. Thus, this review presents a survey on deep learning for multimodal data fusion to provide readers, regardless of their original community, with the fundamentals of multimodal deep learning fusion method and to motivate new multimodal data fusion techniques of deep learning. Specifically, representative architectures that are widely used are summarized as fundamental to the understanding of multimodal deep learning. Then the current pioneering multimodal data fusion deep learning models are summarized. Finally, some challenges and future topics of multimodal data fusion deep learning models are described.

Gao Jing, Li Peng, Chen Zhikui, Zhang Jianing

2020-Mar-18

General General

Redundancy-Weighting the PDB for Detailed Secondary Structure Prediction Using Deep-Learning Models.

In Bioinformatics (Oxford, England)

MOTIVATION : The Protein Data Bank (PDB), the ultimate source for data in structural biology, is inherently imbalanced. To alleviate biases, virtually all structural biology studies use non-redundant subsets of the PDB, which include only a fraction of the available data. An alternative approach, dubbed redundancy-weighting, down-weights redundant entries rather than discarding them. This approach may be particularly helpful for Machine Learning (ML) methods that use the PDB as their source for data.Methods for Secondary Structure Prediction (SSP) have greatly improved over the years with recent studies achieving above 70% accuracy for 8-class (DSSP) prediction. As these methods typically incorporate machine learning techniques, training on redundancy-weighted datasets might improve accuracy, as well as pave the way toward larger and more informative secondary structure alphabets.

RESULTS : This article compares the SSP performances of Deep Learning (DL) models trained on either redundancy-weighted or non-redundant datasets. We show that training on redundancy-weighted sets consistently results in better prediction of 3-class (HCE), 8-class (DSSP) and 13-class (STR2) secondary structures.

AVAILABILITY : Data and DL models are available in http://meshi1.cs.bgu.ac.il/rw.

Sidi Tomer, Keasar Chen

2020-Mar-18

Cardiology Cardiology

Development of a Real-Time Risk Prediction Model for In-Hospital Cardiac Arrest in Critically Ill Patients Using Deep Learning: Retrospective Study.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : Cardiac arrest is the most serious death-related event in intensive care units (ICUs), but it is not easily predicted because of the complex and time-dependent data characteristics of intensive care patients. Given the complexity and time dependence of ICU data, deep learning-based methods are expected to provide a good foundation for developing risk prediction models based on large clinical records.

OBJECTIVE : This study aimed to implement a deep learning model that estimates the distribution of cardiac arrest risk probability over time based on clinical data and assesses its potential.

METHODS : A retrospective study of 759 ICU patients was conducted between January 2013 and July 2015. A character-level gated recurrent unit with a Weibull distribution algorithm was used to develop a real-time prediction model. Fivefold cross-validation testing (training set: 80% and validation set: 20%) determined the consistency of model accuracy. The time-dependent area under the curve (TAUC) was analyzed based on the aggregation of 5 validation sets.

RESULTS : The TAUCs of the implemented model were 0.963, 0.942, 0.917, 0.875, 0.850, 0.842, and 0.761 before cardiac arrest at 1, 8, 16, 24, 32, 40, and 48 hours, respectively. The sensitivity was between 0.846 and 0.909, and specificity was between 0.923 and 0.946. The distribution of risk between the cardiac arrest group and the non-cardiac arrest group was generally different, and the difference rapidly increased as the time left until cardiac arrest reduced.

CONCLUSIONS : A deep learning model for forecasting cardiac arrest was implemented and tested by considering the cumulative and fluctuating effects of time-dependent clinical data gathered from a large medical center. This real-time prediction model is expected to improve patient's care by allowing early intervention in patients at high risk of unexpected cardiac arrests.

Kim Junetae, Park Yu Rang, Lee Jeong Hoon, Lee Jae-Ho, Kim Young-Hak, Huh Jin Won

2020-Mar-18

Weibull distribution, cardiac arrest, deep learning, forecasting, gated recurrent unit, intensive care units

General General

Three-Dimensional Nanoscale Flexible Memristor Networks with Ultralow Power for Information Transmission and Processing Application.

In Nano letters ; h5-index 188.0

To construct an artificial intelligence system with high efficient information integration and computing capability like the human brain, it is necessary to realize the biological neurotransmission and information processing in artificial neural network (ANN), rather than a single electronic synapse as most reports. Because the power consumption of single synaptic event is ∼10 fJ in biology, designing an intelligent memristors-based 3D ANN with energy consumption lower than femtojoule-level (e.g., attojoule-level) and faster operating speed than millisecond-level makes it possible for constructing a higher energy efficient and higher speed computing system than the human brain. In this paper, a flexible 3D crossbar memristor array is presented, exhibiting the multilevel information transmission functionality with the power consumption of 4.28 aJ and the response speed of 50 ns per synaptic event. This work is a significant step toward the development of an ultrahigh efficient and ultrahigh-speed wearable 3D neuromorphic computing system.

Wang Tian-Yu, Meng Jia-Lin, Rao Ming-Yi, He Zhen-Yu, Chen Lin, Zhu Hao, Sun Qing-Qing, Ding Shi-Jin, Bao Wen-Zhong, Zhou Peng, Zhang David Wei

2020-Mar-23

artificial neural network, information transmission, memristor array, wearable electronics

Pathology Pathology

Banff Digital Pathology Working Group: Going Digital in Transplant Pathology.

In American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons

The Banff Digital Pathology Working Group (DPWG) was formed in the time leading up to and during the joint American Society for Histocompatibility and Immunogenetics (ASHI)/Banff Meeting, September 23-27, 2019, held in Pittsburgh, Pennsylvania. At the meeting, the 14th Banff Conference, presentations directly and peripherally related to the topic of "digital pathology" were presented; and discussions before, during, and after the meeting have resulted in a list of issues to address for the DPWG. Included are practice standardization, integrative approaches for study classification, scoring of histologic parameters (e.g., interstitial fibrosis and tubular atrophy and inflammation), algorithm classification, and precision diagnosis (e.g., molecular pathways and therapeutics). Since the meeting, a survey with international participation of mostly pathologists (81%) was conducted, showing that whole slide imaging (WSI) is available at the majority of centers (71%) but that artificial intelligence (AI)/machine learning was only used in around 12% of centers, with a wide variety of programs/algorithms employed. Digitalization is not just an end in itself. It also is a necessary precondition for AI and other approaches. Discussions at the meeting and the survey highlight the unmet need for a Banff DPWG and point the way toward future contributions that can be made.

Farris Alton B, Moghe Ishita, Wu Simon, Hogan Julien, Cornell Lynn D, Alexander Mariam P, Kers Jesper, Demetris Anthony J, Levenson Richard M, Tomaszewski John, Barisoni Laura, Yagi Yukako, Solez Kim

2020-Mar-17

General General

The Parasitic Nature of Social AI: Sharing Minds with the Mindless.

In Integrative psychological & behavioral science

Can artificial intelligence (AI) develop the potential to be our partner, and will we be as sensitive to its social signals as we are to those of human beings? I examine both of these questions and how cultural psychology might add such questions to its research agenda. There are three areas in which I believe there is a need for both a better understanding and added perspective. First, I will present some important concepts and ideas from the world of AI that might be beneficial for pursuing research topics focused on AI within the cultural psychology research agenda. Second, there are some very interesting questions that must be answered with respect to central notions in cultural psychology as these are tested through human interactions with AI. Third, I claim that social robots are parasitic to deeply ingrained human social behaviour, in the sense that they exploit and feed upon processes and mechanisms that evolved for purposes that were originally completely alien to human-computer interactions.

Sætra Henrik Skaug

2020-Mar-17

Artificial intelligence, Cooperation, Cultural psychology, Deception, Social robots

General General

Integrative analysis of DNA methylation and gene expression in papillary renal cell carcinoma.

In Molecular genetics and genomics : MGG

Patterns of DNA methylation are significantly altered in cancers. Interpreting the functional consequences of DNA methylation requires the integration of multiple forms of data. The recent advancement in the next-generation sequencing can help to decode this relationship and in biomarker discovery. In this study, we investigated the methylation patterns of papillary renal cell carcinoma (PRCC) and its relationship with the gene expression using The Cancer Genome Atlas (TCGA) multi-omics data. We found that the promoter and body of tumor suppressor genes, microRNAs and gene clusters and families, including cadherins, protocadherins, claudins and collagens, are hypermethylated in PRCC. Hypomethylated genes in PRCC are associated with the immune function. The gene expression of several novel candidate genes, including interleukin receptor IL17RE and immune checkpoint genes HHLA2, SIRPA and HAVCR2, shows a significant correlation with DNA methylation. We also developed machine learning models using features extracted from single and multi-omics data to distinguish early and late stages of PRCC. A comparative study of different feature selection algorithms, predictive models, data integration techniques and representations of methylation data was performed. Integration of both gene expression and DNA methylation features improved the performance of models in distinguishing tumor stages. In summary, our study identifies PRCC driver genes and proposes predictive models based on both DNA methylation and gene expression. These results on PRCC will aid in targeted experiments and provide a strategy to improve the classification accuracy of tumor stages.

Singh Noor Pratap, Vinod P K

2020-Mar-17

Data integration, Epigenetic regulation, Multi-omics, Multiple kernel learning, RNASeq, Renal cell carcinoma, Tumor stage prediction

General General

Machine Learning-Guided Prediction of Antigen-Reactive In Silico Clonotypes Based on Changes in Clonal Abundance through Bio-Panning.

In Biomolecules

c-Met is a promising target in cancer therapy for its intrinsic oncogenic properties. However, there are currently no c-Met-specific inhibitors available in the clinic. Antibodies blocking the interaction with its only known ligand, hepatocyte growth factor, and/or inducing receptor internalization have been clinically tested. To explore other therapeutic antibody mechanisms like Fc-mediated effector function, bispecific T cell engagement, and chimeric antigen T cell receptors, a diverse panel of antibodies is essential. We prepared a chicken immune scFv library, performed four rounds of bio-panning, obtained 641 clones using a high-throughput clonal retrieval system (TrueRepertoireTM, TR), and found 149 antigen-reactive scFv clones. We also prepared phagemid DNA before the start of bio-panning (round 0) and, after each round of bio-panning (round 1-4), performed next-generation sequencing of these five sets of phagemid DNA, and identified 860,207 HCDR3 clonotypes and 443,292 LCDR3 clonotypes along with their clonal abundance data. We then established a TR data set consisting of antigen reactivity for scFv clones found in TR analysis and the clonal abundance of their HCDR3 and LCDR3 clonotypes in five sets of phagemid DNA. Using the TR data set, a random forest machine learning algorithm was trained to predict the binding properties of in silico HCDR3 and LCDR3 clonotypes. Subsequently, we synthesized 40 HCDR3 and 40 LCDR3 clonotypes predicted to be antigen reactive (AR) and constructed a phage-displayed scFv library called the AR library. In parallel, we also prepared an antigen non-reactive (NR) library using 10 HCDR3 and 10 LCDR3 clonotypes predicted to be NR. After a single round of bio-panning, we screened 96 randomly-selected phage clones from the AR library and found out 14 AR scFv clones consisting of 5 HCDR3 and 11 LCDR3 AR clonotypes. We also screened 96 randomly-selected phage clones from the NR library, but did not identify any AR clones. In summary, machine learning algorithms can provide a method for identifying AR antibodies, which allows for the characterization of diverse antibody libraries inaccessible by traditional methods.

Yoo Duck Kyun, Lee Seung Ryul, Jung Yushin, Han Haejun, Lee Hwa Kyoung, Han Jerome, Kim Soohyun, Chae Jisu, Ryu Taehoon, Chung Junho

2020-Mar-08

antibody discovery, c-Met, machine learning, next-generation sequencing, phage display, random forest

Ophthalmology Ophthalmology

Microvasculature Segmentation and Inter-capillary Area Quantification of the Deep Vascular Complex using Transfer Learning

ArXiv Preprint

Purpose: Optical Coherence Tomography Angiography (OCT-A) permits visualization of the changes to the retinal circulation due to diabetic retinopathy (DR), a microvascular complication of diabetes. We demonstrate accurate segmentation of the vascular morphology for the superficial capillary plexus and deep vascular complex (SCP and DVC) using a convolutional neural network (CNN) for quantitative analysis. Methods: Retinal OCT-A with a 6x6mm field of view (FOV) were acquired using a Zeiss PlexElite. Multiple-volume acquisition and averaging enhanced the vessel network contrast used for training the CNN. We used transfer learning from a CNN trained on 76 images from smaller FOVs of the SCP acquired using different OCT systems. Quantitative analysis of perfusion was performed on the automated vessel segmentations in representative patients with DR. Results: The automated segmentations of the OCT-A images maintained the hierarchical branching and lobular morphologies of the SCP and DVC, respectively. The network segmented the SCP with an accuracy of 0.8599, and a Dice index of 0.8618. For the DVC, the accuracy was 0.7986, and the Dice index was 0.8139. The inter-rater comparisons for the SCP had an accuracy and Dice index of 0.8300 and 0.6700, respectively, and 0.6874 and 0.7416 for the DVC. Conclusions: Transfer learning reduces the amount of manually-annotated images required, while producing high quality automatic segmentations of the SCP and DVC. Using high quality training data preserves the characteristic appearance of the capillary networks in each layer. Translational Relevance: Accurate retinal microvasculature segmentation with the CNN results in improved perfusion analysis in diabetic retinopathy.

Julian Lo, Morgan Heisler, Vinicius Vanzan, Sonja Karst, Ivana Zadro Matovinovic, Sven Loncaric, Eduardo V. Navajas, Mirza Faisal Beg, Marinko V. Sarunic

2020-03-19

Public Health Public Health

Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine.

In Database : the journal of biological databases and curation

Precision medicine is one of the recent and powerful developments in medical care, which has the potential to improve the traditional symptom-driven practice of medicine, allowing earlier interventions using advanced diagnostics and tailoring better and economically personalized treatments. Identifying the best pathway to personalized and population medicine involves the ability to analyze comprehensive patient information together with broader aspects to monitor and distinguish between sick and relatively healthy people, which will lead to a better understanding of biological indicators that can signal shifts in health. While the complexities of disease at the individual level have made it difficult to utilize healthcare information in clinical decision-making, some of the existing constraints have been greatly minimized by technological advancements. To implement effective precision medicine with enhanced ability to positively impact patient outcomes and provide real-time decision support, it is important to harness the power of electronic health records by integrating disparate data sources and discovering patient-specific patterns of disease progression. Useful analytic tools, technologies, databases, and approaches are required to augment networking and interoperability of clinical, laboratory and public health systems, as well as addressing ethical and social issues related to the privacy and protection of healthcare data with effective balance. Developing multifunctional machine learning platforms for clinical data extraction, aggregation, management and analysis can support clinicians by efficiently stratifying subjects to understand specific scenarios and optimize decision-making. Implementation of artificial intelligence in healthcare is a compelling vision that has the potential in leading to the significant improvements for achieving the goals of providing real-time, better personalized and population medicine at lower costs. In this study, we focused on analyzing and discussing various published artificial intelligence and machine learning solutions, approaches and perspectives, aiming to advance academic solutions in paving the way for a new data-centric era of discovery in healthcare.

Ahmed Zeeshan, Mohamed Khalid, Zeeshan Saman, Dong XinQi

2020-Jan-01

General General

Use of Computational Modeling to Study Joint Degeneration: A Review.

In Frontiers in bioengineering and biotechnology

Osteoarthritis (OA), a degenerative joint disease, is the most common chronic condition of the joints, which cannot be prevented effectively. Computational modeling of joint degradation allows to estimate the patient-specific progression of OA, which can aid clinicians to estimate the most suitable time window for surgical intervention in osteoarthritic patients. This paper gives an overview of the different approaches used to model different aspects of joint degeneration, thereby focusing mostly on the knee joint. The paper starts by discussing how OA affects the different components of the joint and how these are accounted for in the models. Subsequently, it discusses the different modeling approaches that can be used to answer questions related to OA etiology, progression and treatment. These models are ordered based on their underlying assumptions and technologies: musculoskeletal models, Finite Element models, (gene) regulatory models, multiscale models and data-driven models (artificial intelligence/machine learning). Finally, it is concluded that in the future, efforts should be made to integrate the different modeling techniques into a more robust computational framework that should not only be efficient to predict OA progression but also easily allow a patient's individualized risk assessment as screening tool for use in clinical practice.

Mukherjee Satanik, Nazemi Majid, Jonkers Ilse, Geris Liesbet

2020

bone remodeling, cartilage degeneration, data driven approach, finite element modeling, gene regulatory network, in silico modeling

Radiology Radiology

Identifying BAP1 Mutations in Clear-Cell Renal Cell Carcinoma by CT Radiomics: Preliminary Findings.

In Frontiers in oncology

To evaluate the potential application of computed tomography (CT) radiomics in the prediction of BRCA1-associated protein 1 (BAP1) mutation status in patients with clear-cell renal cell carcinoma (ccRCC). In this retrospective study, clinical and CT imaging data of 54 patients were retrieved from The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma database. Among these, 45 patients had wild-type BAP1 and nine patients had BAP1 mutation. The texture features of tumor images were extracted using the Matlab-based IBEX package. To produce class-balanced data and improve the stability of prediction, we performed data augmentation for the BAP1 mutation group during cross validation. A model to predict BAP1 mutation status was constructed using Random Forest Classification algorithms, and was evaluated using leave-one-out-cross-validation. Random Forest model of predict BAP1 mutation status had an accuracy of 0.83, sensitivity of 0.72, specificity of 0.87, precision of 0.65, AUC of 0.77, F-score of 0.68. CT radiomics is a potential and feasible method for predicting BAP1 mutation status in patients with ccRCC.

Feng Zhan, Zhang Lixia, Qi Zhong, Shen Qijun, Hu Zhengyu, Chen Feng

2020

BAP1 mutation, CT, clear cell renal cell carcinoma, machine learning, radiomics

General General

Enabling automated herbarium sheet image post-processing using neural network models for color reference chart detection.

In Applications in plant sciences

Premise : Large-scale efforts to digitize herbaria have resulted in more than 18 million publicly available Plantae images on sites such as iDigBio. The automation of image post-processing will lead to time savings in the digitization of biological specimens, as well as improvements in data quality. Here, new and modified neural network methodologies were developed to automatically detect color reference charts (CRC), enabling the future automation of various post-processing tasks.

Methods and Results : We used 1000 herbarium specimen images from 52 herbaria to test our novel neural network model, ColorNet, which was developed to identify CRCs smaller than 4 cm2, resulting in a 30% increase in accuracy over the performance of other state-of-the-art models such as Faster R-CNN. For larger CRCs, we propose modifications to Faster R-CNN to increase inference speed.

Conclusions : Our proposed neural networks detect a range of CRCs, which may enable the automation of post-processing tasks found in herbarium digitization workflows, such as image orientation or white balance correction.

Ledesma Dakila A, Powell Caleb A, Shaw Joey, Qin Hong

2020-Mar

automation, digitization, herbarium, machine learning, natural history collections, specimen images

General General

FaceSync: Open source framework for recording facial expressions with head-mounted cameras.

In F1000Research

Advances in computer vision and machine learning algorithms have enabled researchers to extract facial expression data from face video recordings with greater ease and speed than standard manual coding methods, which has led to a dramatic increase in the pace of facial expression research. However, there are many limitations in recording facial expressions in laboratory settings.  Conventional video recording setups using webcams, tripod-mounted cameras, or pan-tilt-zoom cameras require making compromises between cost, reliability, and flexibility. As an alternative, we propose the use of a mobile head-mounted camera that can be easily constructed from our open-source instructions and blueprints at a fraction of the cost of conventional setups. The head-mounted camera framework is supported by the open source Python toolbox FaceSync, which provides an automated method for synchronizing videos. We provide four proof-of-concept studies demonstrating the benefits of this recording system in reliably measuring and analyzing facial expressions in diverse experimental setups, including group interaction experiments.

Cheong Jin Hyun, Brooks Sawyer, Chang Luke J

2019

Python toolbox, affective computing, facial expressions, head-mounted camera, synchronization

General General

EXP2SL: A Machine Learning Framework for Cell-Line-Specific Synthetic Lethality Prediction.

In Frontiers in pharmacology

Synthetic lethality (SL), an important type of genetic interaction, can provide useful insight into the target identification process for the development of anticancer therapeutics. Although several well-established SL gene pairs have been verified to be conserved in humans, most SL interactions remain cell-line specific. Here, we demonstrated that the cell-line-specific gene expression profiles derived from the shRNA perturbation experiments performed in the LINCS L1000 project can provide useful features for predicting SL interactions in human. In this paper, we developed a semi-supervised neural network-based method called EXP2SL to accurately identify SL interactions from the L1000 gene expression profiles. Through a systematic evaluation on the SL datasets of three different cell lines, we demonstrated that our model achieved better performance than the baseline methods and verified the effectiveness of using the L1000 gene expression features and the semi-supervise training technique in SL prediction.

Wan Fangping, Li Shuya, Tian Tingzhong, Lei Yipin, Zhao Dan, Zeng Jianyang

2020

L1000 gene expression profiles, machine learning, semi-supervised neural network, synthetic lethality, target identification

General General

Identifying Suicidal Ideation Among Chinese Patients with Major Depressive Disorder: Evidence from a Real-World Hospital-Based Study in China.

In Neuropsychiatric disease and treatment

Background : A growing body of research suggests that major depressive disorder (MDD) is one of the most common psychiatric conditions associated with suicide ideation (SI). However, how a combination of easily accessible variables built a utility clinically model to estimate the probability of an individual patient with SI via machine learning is limited.

Methods : We used the electronic medical record database from a hospital located in western China. A total of 1916 Chinese patients with MDD were included. Easily accessible data (demographic, clinical, and biological variables) were collected at admission (on the first day of admission) and were used to distinguish SI with MDD from non-SI using a machine learning algorithm (neural network).

Results : The neural network algorithm distinguished 1356 out of 1916 patients translating into 70.08% accuracy (70.68% sensitivity and 67.09% specificity) and an area under the curve (AUC) of 0.76. The most relevant predictor variables in identifying SI from non-SI included free thyroxine (FT4), the total scores of Hamilton Depression Scale (HAMD), vocational status, and free triiodothyronine (FT3).

Conclusion : Risk for SI among patients with MDD can be identified at an individual subject level by integrating demographic, clinical, and biological variables as possible as early during hospitalization (at admission).

Ge Fenfen, Jiang Jingwen, Wang Yue, Yuan Cui, Zhang Wei

2020

depression, machine learning, real-world, suicide ideation

General General

Global Transcriptome Analysis Identifies a Diagnostic Signature for Early Disseminated Lyme Disease and Its Resolution.

In mBio

A bioinformatics approach was employed to identify transcriptome alterations in the peripheral blood mononuclear cells of well-characterized human subjects who were diagnosed with early disseminated Lyme disease (LD) based on stringent microbiological and clinical criteria. Transcriptomes were assessed at the time of presentation and also at approximately 1 month (early convalescence) and 6 months (late convalescence) after initiation of an appropriate antibiotic regimen. Comparative transcriptomics identified 335 transcripts, representing 233 unique genes, with significant alterations of at least 2-fold expression in acute- or convalescent-phase blood samples from LD subjects relative to healthy donors. Acute-phase blood samples from LD subjects had the largest number of differentially expressed transcripts (187 induced, 54 repressed). This transcriptional profile, which was dominated by interferon-regulated genes, was sustained during early convalescence. 6 months after antibiotic treatment the transcriptome of LD subjects was indistinguishable from that of healthy controls based on two separate methods of analysis. Return of the LD expression profile to levels found in control subjects was concordant with disease outcome; 82% of subjects with LD experienced at least one symptom at the baseline visit compared to 43% at the early convalescence time point and only a single patient (9%) at the 6-month convalescence time point. Using the random forest machine learning algorithm, we developed an efficient computational framework to identify sets of 20 classifier genes that discriminated LD from other bacterial and viral infections. These novel LD biomarkers not only differentiated subjects with acute disseminated LD from healthy controls with 96% accuracy but also distinguished between subjects with acute and resolved (late convalescent) disease with 97% accuracy.IMPORTANCE Lyme disease (LD), caused by Borrelia burgdorferi, is the most common tick-borne infectious disease in the United States. We examined gene expression patterns in the blood of individuals with early disseminated LD at the time of diagnosis (acute) and also at approximately 1 month and 6 months following antibiotic treatment. A distinct acute LD profile was observed that was sustained during early convalescence (1 month) but returned to control levels 6 months after treatment. Using a computer learning algorithm, we identified sets of 20 classifier genes that discriminate LD from other bacterial and viral infections. In addition, these novel LD biomarkers are highly accurate in distinguishing patients with acute LD from healthy subjects and in discriminating between individuals with active and resolved infection. This computational approach offers the potential for more accurate diagnosis of early disseminated Lyme disease. It may also allow improved monitoring of treatment efficacy and disease resolution.

Petzke Mary M, Volyanskyy Konstantin, Mao Yong, Arevalo Byron, Zohn Raphael, Quituisaca Johanna, Wormser Gary P, Dimitrova Nevenka, Schwartz Ira

2020-Mar-17

Borrelia burgdorferi\n, Lyme disease, diagnostics, random forest, transcriptome

General General

Association of meteorological factors and atmospheric particulate matter with the incidence of pneumonia: an ecological study.

In Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases

OBJECTIVES : Previous studies reported inconsistent results between pneumonia and meteorological factors. We aimed to identify principal meteorological factors associated with pneumonia and to estimate the effect size and lag time.

METHODS : This is a nationwide population-based study using a healthcare claims database merged with a weather database in eight metropolitan cities in Korea. We applied a stepwise approach using the Granger causality test and generalized additive model to elucidate the association between weekly pneumonia incidence (WPI) and meteorological factors/air pollutants (MFAP). Impulse response function was used to examine the time lag.

RESULTS : In total, 2,011,424 cases of pneumonia were identified from 2007 to 2017. Among MFAP, diurnal temperature range (DTR), humidity, and particulate matter ≤2.5 μm in diameter (PM2.5) had the lowest Akaike information criterion (0.40) and showed statistically significant associations with WPI (p<0.001 for all 3 MFAPs). The association of DTR and WPI showed an inverted U pattern for bacterial and unspecified pneumonia, whereas for viral pneumonia, WPI increased gradually in a more linear manner with DTR and no substantial decline. Humidity showed a consistent pattern in all three pneumonia categories. WPI steeply increased up to 10-20 μg/m3 of PM2.5 but did not show a further increase in higher concentrations. Based on the result, we examined the effect of MFAP in different lag times up to 3 weeks.

CONCLUSIONS : DTR, humidity, and PM2.5 were identified as MFAP most closely associated with WPI. With the model, we were able to visualize the effect-time association of MFAP and WPI.

Huh Kyungmin, Hong Jinwook, Jung Jaehun

2020-Mar-14

Public Health Public Health

The International Conference on Intelligent Biology and Medicine 2019: computational methods for drug interactions.

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

In this editorial, we briefly summarize the International Conference on Intelligent Biology and Medicine 2019 (ICIBM 2019) that was held on June 9-11, 2019 at Columbus, Ohio, USA. Then, we introduce the two research articles included in this supplement issue. These two research articles were selected after careful review of 105 articles that were submitted to the conference, and cover topics on deep learning for drug-target interaction prediction and data mining and visualization of high-order drug-drug interactions.

Ning Xia, Zhang Chi, Wang Kai, Zhao Zhongming, Mathé Ewy

2020-Mar-18

General General

Extraction of mechanical properties of materials through deep learning from instrumented indentation.

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

Instrumented indentation has been developed and widely utilized as one of the most versatile and practical means of extracting mechanical properties of materials. This method is particularly desirable for those applications where it is difficult to experimentally determine the mechanical properties using stress-strain data obtained from coupon specimens. Such applications include material processing and manufacturing of small and large engineering components and structures involving the following: three-dimensional (3D) printing, thin-film and multilayered structures, and integrated manufacturing of materials for coupled mechanical and functional properties. Here, we utilize the latest developments in neural networks, including a multifidelity approach whereby deep-learning algorithms are trained to extract elastoplastic properties of metals and alloys from instrumented indentation results using multiple datasets for desired levels of improved accuracy. We have established algorithms for solving inverse problems by recourse to single, dual, and multiple indentation and demonstrate that these algorithms significantly outperform traditional brute force computations and function-fitting methods. Moreover, we present several multifidelity approaches specifically for solving the inverse indentation problem which 1) significantly reduce the number of high-fidelity datasets required to achieve a given level of accuracy, 2) utilize known physical and scaling laws to improve training efficiency and accuracy, and 3) integrate simulation and experimental data for training disparate datasets to learn and minimize systematic errors. The predictive capabilities and advantages of these multifidelity methods have been assessed by direct comparisons with experimental results for indentation for different commercial alloys, including two wrought aluminum alloys and several 3D printed titanium alloys.

Lu Lu, Dao Ming, Kumar Punit, Ramamurty Upadrasta, Karniadakis George Em, Suresh Subra

2020-Mar-16

3D printed materials, machine learning, multifidelity modeling, stress–strain behavior, transfer learning

General General

Sequential classification system for recognition of malaria infection using peripheral blood cell images.

In Journal of clinical pathology

AIMS : Morphological recognition of red blood cells infected with malaria parasites is an important task in the laboratory practice. Nowadays, there is a lack of specific automated systems able to differentiate malaria with respect to other red blood cell inclusions. This study aims to develop a machine learning approach able to discriminate parasitised erythrocytes not only from normal, but also from other erythrocyte inclusions, such as Howell-Jolly and Pappenheimer bodies, basophilic stippling as well as platelets overlying red blood cells.

METHODS : A total of 15 660 erythrocyte images from 87 smears were segmented using histogram thresholding and watershed techniques, which allowed the extraction of 2852 colour and texture features. Dataset was split into a training and assessment sets. Training set was used to develop the whole system, in which several classification approaches were compared with obtain the most accurate recognition. Afterwards, the recognition system was evaluated with the assessment set, performing two steps: (1) classifying each individual cell image to assess the system's recognition ability and (2) analysing whole smears to obtain a malaria infection diagnosis.

RESULTS : The selection of the best classification approach resulted in a final sequential system with an accuracy of 97.7% for the six groups of red blood cell inclusions. The ability of the system to detect patients infected with malaria showed a sensitivity and specificity of 100% and 90%, respectively.

CONCLUSIONS : The proposed method achieves a high diagnostic performance in the recognition of red blood cell infected with malaria, along with other frequent erythrocyte inclusions.

Molina Angel, Alférez Santiago, Boldú Laura, Acevedo Andrea, Rodellar José, Merino Anna

2020-Mar-16

erythrocyte, image analysis, malaria, morphology, peripheral blood

Surgery Surgery

Attitudes of Patients and Their Relatives Towards Artificial Intelligence in Neurosurgery.

In World neurosurgery ; h5-index 47.0

BACKGROUND : Artificial Intelligence (AI) may favorably support surgeons but may result in concern among patients and their relatives.

OBJECTIVE : To evaluate attitudes of patients and their relatives towards the use of AI in neurosurgery.

METHODS : In this two-stage cross-sectional survey, a qualitative survey was administered to a focus group of former patients to investigate their perception of AI and its role in neurosurgery. Five themes were identified and used to generate a case-based quantitative survey administered to inpatients and their relatives over a two-week period. Presented AI platforms were rated appropriate and acceptable using 5-point Likert scales. Demographic data was collected. A Chi Square test was performed to determine whether demographics influenced participants' attitudes.

RESULTS : In the first stage, 20 participants responded. Five themes were identified: interpretation of imaging (4/20; 20%), operative planning (5/20; 25%), real-time alert of potential complications (10/20; 50%), partially autonomous surgery (6/20; 30%), fully autonomous surgery (3/20; 15%). In the second stage, 107 participants responded. The majority felt appropriate and acceptable to use AI for imaging interpretation (76.7%; 66.3%), operative planning (76.7%; 75.8%), real-time alert of potential complications (82.2%; 72.9%), and partially autonomous surgery (58%; 47.7%). Conversely, most did not feel that fully autonomous surgery was appropriate (27.1%) or acceptable (17.7%). Demographics did not have a significant influence on perception.

CONCLUSIONS : The majority of patients and their relatives believed that AI has a role in neurosurgery and found it acceptable. Notable exceptions remain fully autonomous systems, with most wanting the neurosurgeon ultimately to remain in control.

Palmisciano Paolo, Jamjoom Aimun Ab, Taylor Daniel, Stoyanov Danail, Marcus Hani J

2020-Mar-13

Artificial Intelligence, General Surgery, Neurosurgery, Patients, Survey and Questionnaires, Technology

General General

Artificially-generated scenes demonstrate the importance of global scene properties for scene perception.

In Neuropsychologia

Recent electrophysiological research highlights the significance of global scene properties (GSPs) for scene perception. However, since real-world scenes span a range of low-level stimulus properties and high-level contextual semantics, GSP effects may also reflect additional processing of such non-global factors. We examined this question by asking whether Event-Related Potentials (ERPs) to GSPs will still be observed when specific low- and high-level scene properties are absent from the scene. We presented participants with computer-based artificially-manipulated scenes varying in two GSPs (spatial expanse and naturalness) which minimized other sources of scene information (color and semantic object detail). We found that the peak amplitude of the P2 component was sensitive to the spatial expanse and naturalness of the artificially-generated scenes: P2 amplitude was higher to closed than open scenes, and in response to manmade than natural scenes. A control experiment showed that the effect of Naturalness on the P2 is not driven by local texture information, while earlier effects of naturalness, expressed as a modulation of the P1 and N1 amplitudes, are sensitive to texture information. Our results demonstrate that GSPs are processed robustly around 220 ms and that P2 can be used as an index of global scene perception.

Harel Assaf, Mzozoyana Mavuso W, Al Zoubi Hamada, Nador Jeffrey D, Noesen Birken T, Lowe Matthew X, Cant Jonathan S

2020-Mar-13

EEG, ERP, P2, Scene perception, Scene recognition, Vision

Public Health Public Health

Identifying patterns and predictors of lifestyle modification in electronic health record documentation using statistical and machine learning methods.

In Preventive medicine ; h5-index 62.0

Just under half of the 85.7 million US adults with hypertension have uncontrolled blood pressure using a hypertension threshold of systolic pressure ≥ 140 or diastolic pressure ≥ 90. Uncontrolled hypertension increases risks of death, stroke, heart failure, and myocardial infarction. Guidelines on hypertension management include lifestyle modification such as diet and exercise. In order to improve hypertension control, it is important to identify predictors of lifestyle modification assessment or advice to tailor future interventions using these effective, low-risk interventions. Electronic health record data from 14,360 adult hypertension patients at an academic medical center were analyzed using statistical and machine learning methods to identify predictors and timing of lifestyle modification. Multiple variables were statistically significant in analysis of lifestyle modification documentation at multiple time points. Random Forest was the best machine learning method to classify lifestyle modification documentation at any time with Area Under the Receiver Operator Curve (AUROC) 0.831. Logistic regression was the best machine learning method for classifying lifestyle modification documentation at ≤3 months with an AUROC of 0.685. Analyzing narrative and coded data from electronic health records can improve understanding of timing of lifestyle modification and patient, clinic and provider characteristics that are correlated with or predictive of documentation of lifestyle modification for hypertension. This information can inform improvement efforts in hypertension care processes, treatment implementation, and ultimately hypertension control.

Shoenbill Kimberly, Song Yiqiang, Craven Mark, Johnson Heather, Smith Maureen, Mendonca Eneida A

2020-Mar-13

Electronic health records, Health behavior, Hypertension, Life style, Machine learning, MeSH terms

General General

Status epilepticus severity score as a predictor for the length of stay at hospital for acute-phase treatment in convulsive status epilepticus.

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

To date, hospital length of stay (LOS) determinants for convulsive status epilepticus's (CSE) acute-phase treatment have not been sufficiently investigated, as opposed to those for status epilepticus's (SE) outcome predictors, such as status epilepticus severity score (STESS). Here, we aimed at assessing the significance of STESS in the LOS in patients with CSE. We retrospectively reviewed consecutive adult patients with CSE who were transported to the emergency department of our urban tertiary care hospital in Tokyo, Japan. The study period was from August 2010 to September 2015. The primary endpoint was the LOS of patients with CSE who were directly discharged after acute-phase treatment, and survival analysis for LOS until discharge was conducted. As a result, among 132 eligible patients with CSE admitted to our hospital, 96 (72.7%) were directly discharged with a median LOS of 10 days (IQR: 4-19 days). CSE patients with severe seizures, represented by higher STESS (≥3), had a significantly longer LOS after adjustments with multiple covariates (p = 0.016, in restricted mean survival time analysis). Additionally, prediction for the binomial longer/shorter LOS achieved better performance when STESS was incorporated into the prediction model. Our findings indicate that STESS can also be used as a rough predictor of longer LOS at index admission of patients with CSE.

Sato Kenichiro, Arai Noritoshi, Takeuchi Sousuke

2020-Mar-13

Convulsive seizure, Length of stay, Machine learning, Status epilepticus, Status epilepticus severity score

General General

Computational methods in tumor immunology.

In Methods in enzymology

The remarkable success of cancer immunotherapies, especially the checkpoint blocking antibodies, in a subset of patients has reinvigorated the study of tumor-immune crosstalk and its role in heterogeneity of response. High-throughput sequencing and imaging technologies can help recapitulate various aspects of the tumor ecosystem. Computational approaches provide an arsenal of tools to efficiently analyze, quantify and integrate multiple parameters of tumor immunity mined from these diverse but complementary high-throughput datasets. This chapter describes numerous such computational approaches in tumor immunology that leverage high-throughput data from diverse sources (genomic, transcriptomics, epigenomics and digitized histopathology images) to systematically interrogate tumor immunity in context of its microenvironment, and to identify mechanisms that confer resistance or sensitivity to cancer therapies, in particular immunotherapy.

Bhinder Bhavneet, Elemento Olivier

2020

Checkpoint blocking, Deconvolution, Deep learning, Immune clusters, Immune escape, Immune scores, Immunotherapy, Neoantigen prioritization, Neoantigens, Resistance to therapy, Survival, Tumor heterogeneity, Tumor immunity, Tumor microenvironment, Tumor mutation burden

General General

An Analysis of QSAR Research Based on Machine Learning Concepts.

In Current drug discovery technologies

Quantitative Structure-Activity Relationship (QSAR) is a popular approach developed to correlate chemical molecules with their biological activities based on their chemical structures. Machine learning techniques have proved to be promising solutions to QSAR modeling. Due to the significant role of machine learning strategies in QSAR modeling, this area of research has attracted much attention from researchers. A considerable amount of literature has been published on machine learning based QSAR modeling methodologies whilst this domain still suffers from lack of a recent and comprehensive analysis of these algorithms. This study systematically reviews the application of machine learning algorithms in QSAR, aiming to provide an analytical framework. For this purpose, we present a framework called 'ML-QSAR'. This framework has been designed for future research to: a)facilitate the selection of proper strategies among existing algorithms according to the application area requirements, b) help to develop and ameliorate current methods and c) providing a platform to study existing methodologies comparatively. In ML-QSAR, first a structured categorization is depicted which studied the QSAR modeling research based on machine models. Then several criteria are introduced in order to assess the models. Finally, inspired by aforementioned criteria the qualitative analysis is carried out.

Keyvanpour Mohammad Reza, Shirzad Mehrnoush Barani

2020-Mar-15

QSAR modeling, computational intelligence, drug design, drug discovery, machine learning

General General

An Artificial Intelligence-Based System to Assess Nutrient Intake for Hospitalised Patients

arxiv preprint

Regular monitoring of nutrient intake in hospitalised patients plays a critical role in reducing the risk of disease-related malnutrition. Although several methods to estimate nutrient intake have been developed, there is still a clear demand for a more reliable and fully automated technique, as this could improve data accuracy and reduce both the burden on participants and health costs. In this paper, we propose a novel system based on artificial intelligence (AI) to accurately estimate nutrient intake, by simply processing RGB Depth (RGB-D) image pairs captured before and after meal consumption. The system includes a novel multi-task contextual network for food segmentation, a few-shot learning-based classifier built by limited training samples for food recognition, and an algorithm for 3D surface construction. This allows sequential food segmentation, recognition, and estimation of the consumed food volume, permitting fully automatic estimation of the nutrient intake for each meal. For the development and evaluation of the system, a dedicated new database containing images and nutrient recipes of 322 meals is assembled, coupled to data annotation using innovative strategies. Experimental results demonstrate that the estimated nutrient intake is highly correlated (> 0.91) to the ground truth and shows very small mean relative errors (< 20%), outperforming existing techniques proposed for nutrient intake assessment.

Ya Lu, Thomai Stathopoulou, Maria F. Vasiloglou, Stergios Christodoulidis, Zeno Stanga, Stavroula Mougiakakou

2020-03-18

Radiology Radiology

Automatic segmentation and quantification of epicardial adipose tissue from coronary computed tomography angiography.

In Physics in medicine and biology

Epicardial adipose tissue (EAT) is a visceral fat deposit, that's known for its association with factors, such as obesity, diabetes mellitus, age, and hypertension. Segmentation of the EAT in a fast and reproducible way is important for the interpretation of its role as an independent risk marker intricate. However, EAT has a variable distribution, and various diseases may affect the volume of the EAT, which can increase the complexity of the already time-consuming manual segmentation work. We propose a 3D deep attention U-Net method to automatically segment the EAT from coronary computed tomography angiography (CCTA). Five-fold cross-validation and hold-out experiments were used to evaluate the proposed method through a retrospective investigation of 200 patients. The automatically segmented EAT volume was compared with physician-approved clinical contours. Quantitative metrics used were the Dice similarity coefficient (DSC), sensitivity, specificity, Jaccard index (JAC), Hausdorff distance (HD), mean surface distance (MSD), residual mean square distance (RMSD), and the center of mass distance (CMD). For cross-validation, the median DSC, sensitivity, and specificity were 92.7%, 91.1%, and 95.1%, respectively, with JAC, HD, CMD, MSD, and RMSD are 82.9% ± 8.8%, 3.77 ± 1.86mm, 1.98 ± 1.50mm, 0.37 ± 0.24mm, and 0.65 ± 0.37mm, respectively. For the hold-out test, the accuracy of the proposed method remained high. We developed a novel deep learning-based approach for the automated segmentation of the EAT on CCTA images. We demonstrated the high accuracy of the proposed learning-based segmentation method through comparison with ground truth contour of 200 clinical patient cases using 8 quantitative metrics, Pearson correlation, and Bland-Altman analysis. Our automatic EAT segmentation results show the potential of the proposed method to be used in computer-aided diagnosis of coronary artery diseases (CADs) in clinical settings.

He Xiuxiu, Guo Bangjun, Lei Yang, Wang Tonghe, Fu Yabo, Curran Walter J, Zhang Longjiang, Liu Tian, Yang Xiaofeng

2020-Mar-17

CT, Deep Learning, Segmentation

General General

Multimodal neuroimaging-based prediction of adult outcomes in childhood-onset ADHD using ensemble learning techniques.

In NeuroImage. Clinical

Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent and heterogeneous neurodevelopmental disorder, which is diagnosed using subjective symptom reports. Machine learning classifiers have been utilized to assist in the development of neuroimaging-based biomarkers for objective diagnosis of ADHD. However, existing basic model-based studies in ADHD report suboptimal classification performances and inconclusive results, mainly due to the limited flexibility for each type of basic classifier to appropriately handle multi-dimensional source features with varying properties. This study applied ensemble learning techniques (ELTs), a meta-algorithm that combine several basic machine learning models into one predictive model in order to decrease variance, bias, or improve predictions, in multimodal neuroimaging data collected from 72 young adults, including 36 probands (18 remitters and 18 persisters of childhood ADHD) and 36 group-matched controls. All currently available optimization strategies for ELTs (i.e., voting, bagging, boosting and stacking techniques) were tested in a pool of semifinal classification results generated by seven basic classifiers. The high-dimensional neuroimaging features for classification included regional cortical gray matter (GM) thickness and surface area, GM volume of subcortical structures, volume and fractional anisotropy of major white matter fiber tracts, pair-wise regional connectivity and global/nodal topological properties of the functional brain network for cue-evoked attention process. As a result, the bagging-based ELT with the base model of support vector machine achieved the best results, with significant improvement of the area under the receiver of operating characteristic curve (0.89 for ADHD vs. controls and 0.9 for ADHD persisters vs. remitters). Features of nodal efficiency in right inferior frontal gyrus, right middle frontal (MFG)-inferior parietal (IPL) functional connectivity, and right amygdala volume significantly contributed to accurate discrimination between ADHD probands and controls; higher nodal efficiency of right MFG greatly contributed to inattentive and hyperactive/impulsive symptom remission, while higher right MFG-IPL functional connectivity strongly linked to symptom persistence in adults with childhood ADHD. Considering their improved robustness than the commonly implemented basic classifiers, findings suggest that ELTs may have the potential to identify more reliable neurobiological markers for neurodevelopmental disorders.

Luo Yuyang, Alvarez Tara L, Halperin Jeffrey M, Li Xiaobo

2020-Mar-07

ADHD, Classification, Ensemble learning, Machine learning, Persistence, Remission

General General

SuperCYPsPred-a web server for the prediction of cytochrome activity.

In Nucleic acids research ; h5-index 217.0

Cytochrome P450 enzymes (CYPs)-mediated drug metabolism influences drug pharmacokinetics and results in adverse outcomes in patients through drug-drug interactions (DDIs). Absorption, distribution, metabolism, excretion and toxicity (ADMET) issues are the leading causes for the failure of a drug in the clinical trials. As details on their metabolism are known for just half of the approved drugs, a tool for reliable prediction of CYPs specificity is needed. The SuperCYPsPred web server is currently focused on five major CYPs isoenzymes, which includes CYP1A2, CYP2C19, CYP2D6, CYP2C9 and CYP3A4 that are responsible for more than 80% of the metabolism of clinical drugs. The prediction models for classification of the CYPs inhibition are based on well-established machine learning methods. The models were validated both on cross-validation and external validation sets and achieved good performance. The web server takes a 2D chemical structure as input and reports the CYP inhibition profile of the chemical for 10 models using different molecular fingerprints, along with confidence scores, similar compounds, known CYPs information of drugs-published in literature, detailed interaction profile of individual cytochromes including a DDIs table and an overall CYPs prediction radar chart (http://insilico-cyp.charite.de/SuperCYPsPred/).The web server does not require log in or registration and is free to use.

Banerjee Priyanka, Dunkel Mathias, Kemmler Emanuel, Preissner Robert

2020-Mar-17

General General

Creation and Testing of a Deep Learning Algorithm to Automatically Identify and Label Vessels, Nerves, Tendons, and Bones on Cross-sectional Point-of-Care Ultrasound Scans for Peripheral Intravenous Catheter Placement by Novices.

In Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine

OBJECTIVES : We sought to create a deep learning (DL) algorithm to identify vessels, bones, nerves, and tendons on transverse upper extremity (UE) ultrasound (US) images to enable providers new to US-guided peripheral vascular access to identify anatomy.

METHODS : We used publicly available DL architecture (YOLOv3) and deidentified transverse US videos of the UE for algorithm development. Vessels, bones, tendons, and nerves were labeled with bounding boxes. A total of 203,966 images were generated from videos, with corresponding label box coordinates in a YOLOv3 format. Training accuracy, losses, and learning curves were tracked. As a final real-world test, 50 randomly selected images from unrelated UE US videos were used to test the DL algorithm. Four different versions of the YOLOv3 algorithm were tested with varied amounts of training and sensitivity settings. The same 50 images were labeled by 2 blinded point-of-care ultrasound (POCUS) experts. The area under the curve (AUC) was calculated for the DL algorithm and POCUS expert performance.

RESULTS : The algorithm outperformed POCUS experts in detection of all structures in the UE, with an AUC of 0.78 versus 0.69 and 0.71, respectively. When considering vessels, only one of the POCUS experts attained an AUC of 0.85, just ahead of the DL algorithm, with an AUC of 0.83.

CONCLUSIONS : Our DL algorithm proved accurate at identifying 4 common structures on cross-sectional US imaging of the UE, which would allow novice POCUS providers to more confidently and accurately target vessels for cannulation, avoiding other structures. Overall, the algorithm outperformed 2 blinded POCUS experts.

Blaivas Michael, Arntfield Robert, White Matthew

2020-Mar-17

artificial intelligence, deep learning, emergency medicine, peripheral venous access, point-of-care ultrasound, vascular access

General General

Temporal Pattern Detection to Predict Adverse Events in Critical Care: Case Study With Acute Kidney Injury.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : More than 20% of patients admitted to the intensive care unit (ICU) develop an adverse event (AE). No previous study has leveraged patients' data to extract the temporal features using their structural temporal patterns, that is, trends.

OBJECTIVE : This study aimed to improve AE prediction methods by using structural temporal pattern detection that captures global and local temporal trends and to demonstrate these improvements in the detection of acute kidney injury (AKI).

METHODS : Using the Medical Information Mart for Intensive Care dataset, containing 22,542 patients, we extracted both global and local trends using structural pattern detection methods to predict AKI (ie, binary prediction). Classifiers were built on 17 input features consisting of vital signs and laboratory test results using state-of-the-art models; the optimal classifier was selected for comparisons with previous approaches. The classifier with structural pattern detection features was compared with two baseline classifiers that used different temporal feature extraction approaches commonly used in the literature: (1) symbolic temporal pattern detection, which is the most common approach for multivariate time series classification; and (2) the last recorded value before the prediction point, which is the most common approach to extract temporal data in the AKI prediction literature. Moreover, we assessed the individual contribution of global and local trends. Classifier performance was measured in terms of accuracy (primary outcome), area under the curve, and F-measure. For all experiments, we employed 20-fold cross-validation.

RESULTS : Random forest was the best classifier using structural temporal pattern detection. The accuracy of the classifier with local and global trend features was significantly higher than that while using symbolic temporal pattern detection and the last recorded value (81.3% vs 70.6% vs 58.1%; P<.001). Excluding local or global features reduced the accuracy to 74.4% or 78.1%, respectively (P<.001).

CONCLUSIONS : Classifiers using features obtained from structural temporal pattern detection significantly improved the prediction of AKI onset in ICU patients over two baselines based on common previous approaches. The proposed method is a generalizable approach to predict AEs in critical care that may be used to help clinicians intervene in a timely manner to prevent or mitigate AEs.

Morid Mohammad Amin, Sheng Olivia R Liu, Del Fiol Guilherme, Facelli Julio C, Bray Bruce E, Abdelrahman Samir

2020-Mar-17

acute kidney injury, adverse effects, automated pattern recognition, supervised machine learning

General General

Identifying Lung Cancer Risk Factors in the Elderly Using Deep Neural Networks: Quantitative Analysis of Web-Based Survey Data.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Lung cancer is one of the most dangerous malignant tumors, with the fastest-growing morbidity and mortality, especially in the elderly. With a rapid growth of the elderly population in recent years, lung cancer prevention and control are increasingly of fundamental importance, but are complicated by the fact that the pathogenesis of lung cancer is a complex process involving a variety of risk factors.

OBJECTIVE : This study aimed at identifying key risk factors of lung cancer incidence in the elderly and quantitatively analyzing these risk factors' degree of influence using a deep learning method.

METHODS : Based on Web-based survey data, we integrated multidisciplinary risk factors, including behavioral risk factors, disease history factors, environmental factors, and demographic factors, and then preprocessed these integrated data. We trained deep neural network models in a stratified elderly population. We then extracted risk factors of lung cancer in the elderly and conducted quantitative analyses of the degree of influence using the deep neural network models.

RESULTS : The proposed model quantitatively identified risk factors based on 235,673 adults. The proposed deep neural network models of 4 groups (age ≥65 years, women ≥65 years old, men ≥65 years old, and the whole population) achieved good performance in identifying lung cancer risk factors, with accuracy ranging from 0.927 (95% CI 0.223-0.525; P=.002) to 0.962 (95% CI 0.530-0.751; P=.002) and the area under curve ranging from 0.913 (95% CI 0.564-0.803) to 0.931(95% CI 0.499-0.593). Smoking frequency was the leading risk factor for lung cancer in men 65 years and older. Time since quitting and smoking at least 100 cigarettes in their lifetime were the main risk factors for lung cancer in women 65 years and older. Men 65 years and older had the highest lung cancer incidence among the stratified groups, particularly non-small cell lung cancer incidence. Lung cancer incidence decreased more obviously in men than in women with smoking rate decline.

CONCLUSIONS : This study demonstrated a quantitative method to identify risk factors of lung cancer in the elderly. The proposed models provided intervention indicators to prevent lung cancer, especially in older men. This approach might be used as a risk factor identification tool to apply in other cancers and help physicians make decisions on cancer prevention.

Chen Songjing, Wu Sizhu

2020-Mar-17

aged, deep learning, lung cancer, primary prevention, risk factors

General General

Prediction Models for Childhood Asthma: A Systematic Review.

In Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology

BACKGROUND : The inability to objectively diagnose childhood asthma before age five often results in both under- and over-treatment of asthma in preschool children. Prediction tools for estimating a child's risk of developing asthma by school-age could assist physicians in early asthma care for preschool children. This review aimed to systematically identify and critically appraise studies which either developed novel or updated existing prediction models for predicting school-age asthma.

METHODS : Three databases (Medline, Embase and Web of Science Core Collection) were searched up to July 2019 to identify studies utilising information from children ≤5 years of age to predict asthma in school-age children (6-13 years). Validation studies were evaluated as a secondary objective.

RESULTS : Twenty-four studies describing the development of 26 predictive models published between 2000 and 2019 were identified. Models were either regression-based (n=21) or utilised machine learning approaches (n=5). Nine studies conducted validation of six regression-based models. Fifteen (out of 21) models required additional clinical tests. Overall model performance, assessed by Area Under the Receiver-Operator-Curve (AUC), ranged between 0.66-0.87. Models demonstrated moderate ability to either rule in or rule out asthma development, but not both. Where external validation was performed, models demonstrated modest generalisability (AUC range: 0.62-0.83).

CONCLUSION : Existing prediction models demonstrated moderate predictive performance, often with modest generalisability when independently validated. Limitations of traditional methods have shown to impair predictive accuracy and resolution. Exploration of novel methods such as machine learning approaches may address these limitations for future school-age asthma prediction.

Kothalawala Dilini M, Kadalayil Latha, Weiss Veronique B N, Kyyaly M Aref, Arshad Hasan S, Holloway John W, Rezwan Faisal I

2020-Mar-17

Asthma, Childhood, Prediction model, Risk scores, Wheeze

Public Health Public Health

Stroke Prediction with Machine Learning Methods among Older Chinese.

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

Timely stroke diagnosis and intervention are necessary considering its high prevalence. Previous studies have mainly focused on stroke prediction with balanced data. Thus, this study aimed to develop machine learning models for predicting stroke with imbalanced data in an elderly population in China. Data were obtained from a prospective cohort that included 1131 participants (56 stroke patients and 1075 non-stroke participants) in 2012 and 2014, respectively. Data balancing techniques including random over-sampling (ROS), random under-sampling (RUS), and synthetic minority over-sampling technique (SMOTE) were used to process the imbalanced data in this study. Machine learning methods such as regularized logistic regression (RLR), support vector machine (SVM), and random forest (RF) were used to predict stroke with demographic, lifestyle, and clinical variables. Accuracy, sensitivity, specificity, and areas under the receiver operating characteristic curves (AUCs) were used for performance comparison. The top five variables for stroke prediction were selected for each machine learning method based on the SMOTE-balanced data set. The total prevalence of stroke was high in 2014 (4.95%), with men experiencing much higher prevalence than women (6.76% vs. 3.25%). The three machine learning methods performed poorly in the imbalanced data set with extremely low sensitivity (approximately 0.00) and AUC (approximate