ArticleMedComm2024
Deep learning for precise diagnosis and subtype triage of drug-resistant tuberculosis on chest computed tomography.
Article in MedComm, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Revolutionizing diagnosis of pulmonaryFrontiers in microbiology · 2024Pooled it
- Clinical differentiation between pediatric pulmonary tuberculosis andExperimental and therapeutic medicine · 2026Review
- Artificial intelligence-driven precision treatment of reproductive medicine-related diseases: the optimal protocol choice for IVF-ET.Journal of advanced research · 2026Article
- Convolutional neural network analysis of cervical CT images: classification of Kikuchi disease, lymphoma, lymphadenitis, and tuberculosis.Biomedical engineering online · 2026Article
- Explainable prediction of MDR/RR-TB in tuberculosis-diabetes mellitus multimorbidity: a machine learning model developed and validated in a dual-center study.BMC infectious diseases · 2026Article
- Divergent Evolution of Tuberculosis Lesions During Treatment: A Longitudinal CT-Based Analysis of Progression and Regression Patterns.Diagnostics (Basel, Switzerland) · 2026Article
- Application of Clinical-Radiomics Fusion Model in Predicting Drug Resistance of Cavitary Pulmonary Tuberculosis.Infection and drug resistance · 2026Article
- Review
- Chest CT findings in drug-resistant pulmonary tuberculosis: a comparative analysis of elderly and non-elderly patients.BMC infectious diseases · 2025Article
- Accuracy of Machine Learning in Identifying Drug Resistance in Tuberculosis: A Systematic Review and Meta-Analysis.Health science reports · 2025Review
- Risk factors associated with morbidity and unfavorable treatment outcome in drug-resistant pulmonary tuberculosis: a case-control study.Precision clinical medicine · 2025Article
- Comparison of Al[Frontiers in oncology · 2025Article
- Deep learning for precise diagnosis and subtype triage of drug-resistant tuberculosis on chest computed tomography.MedComm · 2024Article
- Targeting exercise-related genes and placental growth factor for therapeutic development in head and neck squamous cell carcinoma.Frontiers in pharmacology · 2024Article
- Paromomycin targets HDAC1-mediated SUMOylation and IGF1R translocation in glioblastoma.Frontiers in pharmacology · 2024Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning, transforming input data into target prediction through intricate network structures, has inspired novel exploration in automated diagnosis based on medical images. The distinct morphological characteristics of chest abnormalities between drug-resistant tuberculosis (DR-TB) and drug-sensitive tuberculosis (DS-TB) on chest computed tomography (CT) are of potential value in differential diagnosis, which is challenging in the clinic. Hence, based on 1176 chest CT volumes from the equal number of patients with tuberculosis (TB), we presented a Deep learning-based system for TB drug resistance identification and subtype classification (DeepTB), which could automatically diagnose DR-TB and classify crucial subtypes, including rifampicin-resistant tuberculosis, multidrug-resistant tuberculosis, and extensively drug-resistant tuberculosis. Moreover, chest lesions were manually annotated to endow the model with robust power to assist radiologists in image interpretation and the Circos revealed the relationship between chest abnormalities and specific types of DR-TB. Finally, DeepTB achieved an area under the curve (AUC) up to 0.930 for thoracic abnormality detection and 0.943 for DR-TB diagnosis. Notably, the system demonstrated instructive value in DR-TB subtype classification with AUCs ranging from 0.880 to 0.928. Meanwhile, class activation maps were generated to express a human-understandable visual concept. Together, showing a prominent performance, DeepTB would be impactful in clinical decision-making for DR-TB.
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