Automatic discovery of clinically interpretable imaging biomarkers for Mycobacterium tuberculosis supersusceptibility using deep learning
Background Identifying which individuals will develop tuberculosis (TB) remains an
unresolved problem due to few animal models and computational approaches that …
unresolved problem due to few animal models and computational approaches that …
Deep learning predicts gene expression as an intermediate data modality to identify susceptibility patterns in Mycobacterium tuberculosis infected Diversity Outbred …
Background Machine learning sustains successful application to many diagnostic and
prognostic problems in computational histopathology. Yet, few efforts have been made to …
prognostic problems in computational histopathology. Yet, few efforts have been made to …
Lung necrosis and neutrophils reflect common pathways of susceptibility to Mycobacterium tuberculosis in genetically diverse, immune-competent mice
MKK Niazi, N Dhulekar, D Schmidt… - Disease models & …, 2015 - journals.biologists.com
Pulmonary tuberculosis (TB) is caused by Mycobacterium tuberculosis in susceptible
humans. Here, we infected Diversity Outbred (DO) mice with∼ 100 bacilli by aerosol to …
humans. Here, we infected Diversity Outbred (DO) mice with∼ 100 bacilli by aerosol to …
Computational and Empirical Studies Predict Mycobacterium tuberculosis-Specific T Cells as a Biomarker for Infection Outcome
Identifying biomarkers for tuberculosis (TB) is an ongoing challenge in developing
immunological correlates of infection outcome and protection. Biomarker discovery is also …
immunological correlates of infection outcome and protection. Biomarker discovery is also …
[HTML][HTML] Tuberculosis conundrum-current and future scenarios: a proposed comprehensive approach combining laboratory, imaging, and computing advances
SA Merchant, MJS Shaikh… - World Journal of Radiology, 2022 - ncbi.nlm.nih.gov
Tuberculosis (TB) remains a global threat, with the rise of multiple and extensively drug
resistant TB posing additional challenges. The International health community has set …
resistant TB posing additional challenges. The International health community has set …
Digital image analysis of heterogeneous tuberculosis pulmonary pathology in non-clinical animal models using deep convolutional neural networks
BC Asay, BB Edwards, J Andrews, ME Ramey… - Scientific reports, 2020 - nature.com
Efforts to develop effective and safe drugs for treatment of tuberculosis require preclinical
evaluation in animal models. Alongside efficacy testing of novel therapies, effects on …
evaluation in animal models. Alongside efficacy testing of novel therapies, effects on …
Evolution of machine learning in tuberculosis diagnosis: a review of deep learning-based medical applications
M Singh, GV Pujar, SA Kumar, M Bhagyalalitha… - Electronics, 2022 - mdpi.com
Tuberculosis (TB) is an infectious disease that has been a major menace to human health
globally, causing millions of deaths yearly. Well-timed diagnosis and treatment are an arch …
globally, causing millions of deaths yearly. Well-timed diagnosis and treatment are an arch …
Automatic detection of granuloma necrosis in pulmonary tuberculosis using a two-phase algorithm: 2D-TB
Granuloma necrosis occurs in hosts susceptible to pathogenic mycobacteria and is a
diagnostic visual feature of pulmonary tuberculosis (TB) in humans and in super-susceptible …
diagnostic visual feature of pulmonary tuberculosis (TB) in humans and in super-susceptible …
Prediction of Tuberculosis From Lung Tissue Images of Diversity Outbred Mice Using Jump Knowledge Based Cell Graph Neural Network
Tuberculosis (TB), primarily affecting the lungs, is caused by the bacterium Mycobacterium
tuberculosis and poses a significant health risk. Detecting acid-fast bacilli (AFB) in stained …
tuberculosis and poses a significant health risk. Detecting acid-fast bacilli (AFB) in stained …
[HTML][HTML] Refining dataset curation methods for deep learning-based automated tuberculosis screening
Background The study objective was to determine whether unlabeled datasets can be used
to further train and improve the accuracy of a deep learning system (DLS) for the detection of …
to further train and improve the accuracy of a deep learning system (DLS) for the detection of …
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