ArticleInsights into imaging2023
A deep learning model using chest X-ray for identifying TB and NTM-LD patients: a cross-sectional study.
Article in Insights into imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 38 citations in OpenAlex.
- Convolutional neural networks for medical imaging in resource-constrained settings: a scoping review of architectures, performance, and deployment challenges.Frontiers in artificial intelligence · 2026Pooled it
- Diagnostic Performance of Artificial Intelligence-Based Methods for Tuberculosis Detection: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Clinical impact of chronic pulmonary aspergillosis in patients with pulmonary nontuberculous mycobacterial disease and colonization: a multicentre prospective cohort study.Annals of medicine · 2026Article
- Radiographic severity and treatment optimisation inERJ open research · 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
- AI-assisted differentiation of nontuberculous mycobacterial pulmonary disease from colonization: a multi-center study.Insights into imaging · 2025Article
- CXR-MultiTaskNet a unified deep learning framework for joint disease localization and classification in chest radiographs.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
- The innovative diagnostic model facilitates the differentiation between non - tuberculous mycobacterial lung disease and pulmonary tuberculosis.Frontiers in cellular and infection microbiology · 2025Article
- Multimodal machine learning-based model for differentiating nontuberculous mycobacteria fromFrontiers in public health · 2025Article
- Comparing the Output of an Artificial Intelligence Algorithm in Detecting Radiological Signs of Pulmonary Tuberculosis in Digital Chest X-Rays and Their Smartphone-Captured Photos of X-Ray Films: Retrospective Study.JMIR formative research · 2024Article
- Self-Trained Convolutional Neural Network (CNN) for Tuberculosis Diagnosis in Medical Imaging.Cureus · 2024Article
- Cross-site validation of lung cancer diagnosis by electronic nose with deep learning: a multicenter prospective study.Respiratory research · 2024Article
- Deep learning with test-time augmentation for radial endobronchial ultrasound image differentiation: a multicentre verification study.BMJ open respiratory research · 2023Article
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Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTimely differentiating between pulmonary tuberculosis (TB) and nontuberculous mycobacterial lung disease (NTM-LD), which are radiographically similar, is important because infectiousness and treatment differ. This study aimed to evaluate whether artificial intelligence could distinguish between TB or NTM-LD patients by chest X-rays (CXRs) from suspects of mycobacterial lung disease.
methodsA total of 1500 CXRs, including 500 each from patients with pulmonary TB, NTM-LD, and patients with clinical suspicion but negative mycobacterial culture (Imitator) from two hospitals, were retrospectively collected and evaluated in this study. We developed a deep neural network (DNN) and evaluated model performance using the area under the receiver operating characteristic curves (AUC) in both internal and external test sets. Furthermore, we conducted a reader study and tested our model under three scenarios of different mycobacteria prevalence.
resultsAmong the internal and external test sets, the AUCs of our DNN model were 0.83 ± 0.005 and 0.76 ± 0.006 for pulmonary TB, 0.86 ± 0.006 and 0.64 ± 0.017 for NTM-LD, and 0.77 ± 0.007 and 0.74 ± 0.005 for Imitator. The DNN model showed higher performance on the internal test set in classification accuracy (66.5 ± 2.5%) than senior (50.8 ± 3.0%, p < 0.001) and junior pulmonologists (47.5 ± 2.8%, p < 0.001). Among different prevalence scenarios, the DNN model has stable performance in terms of AUC to detect TB and mycobacterial lung disease.
conclusionDNN model had satisfactory performance and a higher accuracy than pulmonologists on classifying patients with presumptive mycobacterial lung diseases. DNN model could be a complementary first-line screening tool.
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