ArticleScientific reports2020
Digital Image Analysis of Heterogeneous Tuberculosis Pulmonary Pathology in Non-Clinical Animal Models using Deep Convolutional Neural Networks.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 25 citations in OpenAlex.
- Artificial intelligence-enabled histological analysis in pre-clinical respiratory disease models: a scoping review.European respiratory review : an official journal of the European Respiratory Society · 2026Article
- Closed-loop artificial intelligence agents for animal epidemic prediction and decision support in livestock farming: a review.Frontiers in veterinary science · 2026Review
- Detection, Isolation, and Identification of Mycobacteria That Cause Nontuberculous Mycobacterial Disease and Tuberculosis.Pathogens (Basel, Switzerland) · 2025Review
- Encapsulin protein MAV2054 enhances Mycobacterium avium virulence by promoting Cdc42-dependent epithelial cell invasion.Journal of microbiology (Seoul, Korea) · 2025Article
- Report of the AI Pathology Task Force, Non-clinical Evaluation Expert Committee, Japan Pharmaceutical Manufacturers Association: an overview of recent publications about AI pathology.Journal of toxicologic pathology · 2025Review
- Spectinamide MBX-4888A exhibits favorable lesion and tissue distribution and promotes treatment shortening in advanced murine models of tuberculosis.Antimicrobial agents and chemotherapy · 2024Article
- Prediction of Tuberculosis From Lung Tissue Images of Diversity Outbred Mice Using Jump Knowledge Based Cell Graph Neural Network.IEEE access : practical innovations, open solutions · 2024Article
- Drug distribution and efficacy of the DprE1 inhibitor BTZ-043 in the C3HeB/FeJ mouse tuberculosis model.Antimicrobial agents and chemotherapy · 2023Article
- Quantifying acute kidney injury in an Ischaemia-Reperfusion Injury mouse model using deep-learning-based semantic segmentation in histology.Biology open · 2023Article
- Application of convolutional neural network for analyzing hepatic fibrosis in mice.Journal of toxicologic pathology · 2023Article
- Reduced SARS-CoV-2 disease outcomes in Syrian hamsters receiving immune sera: Quantitative image analysis in pathologic assessments.Veterinary pathology · 2022Article
- Deep-Learning-Aided Detection of Mycobacteria in Pathology Specimens Increases the Sensitivity in Early Diagnosis of Pulmonary Tuberculosis Compared with Bacteriology Tests.Diagnostics (Basel, Switzerland) · 2022Article
- Artificial Intelligence-based Tumor Segmentation in Mouse Models of Lung Adenocarcinoma.Journal of pathology informatics · 2022Article
- Comparative Analysis of Pharmacodynamics in the C3HeB/FeJ Mouse Tuberculosis Model for DprE1 Inhibitors TBA-7371, PBTZ169, and OPC-167832.Antimicrobial agents and chemotherapy · 2021Article
- Subunit vaccine protects against a clinical isolate of Mycobacterium avium in wild type and immunocompromised mouse models.Scientific reports · 2021Article
- Deep Learning Approaches and Applications in Toxicologic Histopathology: Current Status and Future Perspectives.Journal of pathology informatics · 2021Review
Corrections and comments
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Authors and funding
13 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 pulmonary pathology and disease progression are monitored by using histopathology images from these infected animals. To compare the severity of disease across treatment cohorts, pathologists have historically assigned a semi-quantitative histopathology score that may be subjective in terms of their training, experience, and personal bias. Manual histopathology therefore has limitations regarding reproducibility between studies and pathologists, potentially masking successful treatments. This report describes a pathologist-assistive software tool that reduces these user limitations, while providing a rapid, quantitative scoring system for digital histopathology image analysis. The software, called 'Lesion Image Recognition and Analysis' (LIRA), employs convolutional neural networks to classify seven different pathology features, including three different lesion types from pulmonary tissues of the C3HeB/FeJ tuberculosis mouse model. LIRA was developed to improve the efficiency of histopathology analysis for mouse tuberculosis infection models, this approach has also broader applications to other disease models and tissues. The full source code and documentation is available from https://Github.com/TB-imaging/LIRA.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.