ArticleEuropean journal of nuclear medicine and molecular imaging2021
Distinguishing nontuberculous mycobacteria from Mycobacterium tuberculosis lung disease from CT images using a deep learning framework.
Article in European journal of nuclear medicine and molecular imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 citations in OpenAlex.
- Revolutionizing diagnosis of pulmonaryFrontiers in microbiology · 2024Pooled it
- A Hybrid Swin Transformer and Texture Feature Framework for Histopathological Classification of Paratuberculosis.Bioengineering (Basel, Switzerland) · 2026Article
- Whole-Lung CT Radiomics-Based Machine Learning Classification of Nontuberculous Mycobacterial Lung Disease Across Geographically Distinct Cohorts.medRxiv : the preprint server for health sciences · 2026Article
- Molecular detection of incidental Mycobacterium tuberculosis and nontuberculous mycobacteria co-detections using DNA strip assay: a laboratory-based study.Scientific reports · 2026Article
- Machine learning-based model assists in differentiating Mycobacterium avium Complex Pulmonary Disease from Pulmonary Tuberculosis: A Multicenter Study.Journal of imaging informatics in medicine · 2026Article
- Precise discrimination of mycobacterial pulmonary diseases via multimodal machine learning integrating chest CT and clinical markers.Frontiers in medicine · 2026Article
- Distinguishing nontuberculous mycobacterial lung disease from pulmonary tuberculosis using radiomics machine learning models from CT images.Frontiers in medicine · 2026Article
- Artificial intelligence model outperformed experienced clinicians in differentiating the aetiology of pneumonia on chest computed tomography: a retrospective study.Quantitative imaging in medicine and surgery · 2026Article
- Rapid Molecular Diagnostics of Tuberculosis: What Do We Have, What Do We Need?Pulmonary medicine · 2026Review
- Multimodal-Imaging-Based Interpretable Deep Learning Framework for Distinguishing Brucella from Tuberculosis Spondylitis: A Dual-Center Study.Diagnostics (Basel, Switzerland) · 2025Article
- AI-assisted differentiation of nontuberculous mycobacterial pulmonary disease from colonization: a multi-center study.Insights into imaging · 2025Article
- Enriched lung cancer classification approach using an optimized hybrid deep learning approach.Scientific reports · 2025Article
- Artificial intelligence for tuberculosis control: a scoping review of applications in public health.Journal of global health · 2025Article
- An Integrated Mycobacterial CT Imaging Dataset with Multispecies Information.Scientific data · 2025Article
- Diagnosis of nontuberculous mycobacterial infections using genomics and artificial intelligence-machine learning approaches: scope, progress and challenges.Frontiers in microbiology · 2025Review
- Applications and prospects of artificial intelligence in the auxiliary diagnosis of pediatric pulmonary tuberculosis.Frontiers in microbiology · 2025Review
- A novel circulating miRNA panel for early differential diagnosis of pulmonary tuberculosis from lung cancer with similar radiographic presentations.Frontiers in medicine · 2025Article
- Multimodal machine learning-based model for differentiating nontuberculous mycobacteria fromFrontiers in public health · 2025Article
- A retrospective study differentiating nontuberculous mycobacterial pulmonary disease from pulmonary tuberculosis on computed tomography using radiomics and machine learning algorithms.Annals of medicine · 2024Article
- Tuberculosis Diagnosis: Current, Ongoing, and Future Approaches.Diseases (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo develop and evaluate the effectiveness of a deep learning framework (3D-ResNet) based on CT images to distinguish nontuberculous mycobacterium lung disease (NTM-LD) from Mycobacterium tuberculosis lung disease (MTB-LD).
methodChest CT images of 301 with NTM-LD and 804 with MTB-LD confirmed by pathogenic microbiological examination were retrospectively collected. The differences between the clinical manifestations of the two diseases were analysed. 3D-ResNet was developed to randomly extract data in an 8:1:1 ratio for training, validating, and testing. We also collected external test data (40 with NTM-LD and 40 with MTB-LD) for external validation of the model. The activated region of interest was evaluated using a class activation map. The model was compared with three radiologists in the test set.
resultPatients with NTM-LD were older than those with MTB-LD, patients with MTB-LD had more cough, and those with NTM-LD had more dyspnoea, and the results were statistically significant (p < 0.05). The AUCs of our model on training, validating, and testing datasets were 0.90, 0.88, and 0.86, respectively, while the AUC on the external test set was 0.78. Additionally, the performance of the model was higher than that of the radiologist, and without manual labelling, the model automatically identified lung areas with abnormalities on CT > 1000 times more effectively than the radiologists.
conclusionThis study shows the efficacy of 3D-ResNet as a rapid auxiliary diagnostic tool for NTB-LD and MTB-LD. Its use can help provide timely and accurate treatment strategies to patients with these diseases.
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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.