ArticleBioMed research international2020
Machine Learning-Based Differentiation of Nontuberculous Mycobacteria Lung Disease and Pulmonary Tuberculosis Using CT Images.
Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- Diagnostic Performance of Artificial Intelligence-Based Methods for Tuberculosis Detection: Systematic Review.Journal of medical Internet research · 2025Pooled it
- 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
- Development and internal validation of a LASSO-based clinical prediction model for nontuberculous mycobacterial pulmonary disease versus pulmonary tuberculosis.Frontiers in medicine · 2026Article
- Distinguishing nontuberculous mycobacterial lung disease from pulmonary tuberculosis using radiomics machine learning models from CT images.Frontiers in medicine · 2026Article
- Precise discrimination of mycobacterial pulmonary diseases via multimodal machine learning integrating chest CT and clinical markers.Frontiers in medicine · 2026Article
- Development and validation of a clinical risk score nomogram for distinguishing nontuberculous mycobacteria from Mycobacterium tuberculosis pulmonary disease.Journal of clinical tuberculosis and other mycobacterial diseases · 2025Article
- Development of a machine learning model for early pulmonary tuberculosis diagnosis using blood test biomarkers.BMC infectious diseases · 2025Article
- An Integrated Mycobacterial CT Imaging Dataset with Multispecies Information.Scientific data · 2025Article
- 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
- Machine learning of immune-related laboratory indicators enables discrimination between pulmonary and extrapulmonary tuberculosis.Frontiers in medicine · 2025Article
- A novel hematological score (HS) and its related nomogram model to predict nontuberculous mycobacterial pulmonary disease in patients with suspected multidrug-resistant pulmonary tuberculosis.Annals of medicine · 2024Article
- A retrospective study differentiating nontuberculous mycobacterial pulmonary disease from pulmonary tuberculosis on computed tomography using radiomics and machine learning algorithms.Annals of medicine · 2024Article
- Diagnosis of pulmonary tuberculosis with 3D neural network based on multi-scale attention mechanism.Medical & biological engineering & computing · 2024Article
- CT‑based radiomics analysis of consolidation characteristics in differentiating pulmonary disease of non‑tuberculous mycobacterium from pulmonary tuberculosis.Experimental and therapeutic medicine · 2024Article
- From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning.Military Medical Research · 2023Review
- Development of diagnostic algorithm using machine learning for distinguishing between active tuberculosis and latent tuberculosis infection.BMC infectious diseases · 2022Article
- The Role of Chest CT Radiomics in Diagnosis of Lung Cancer or Tuberculosis: A Pilot Study.Diagnostics (Basel, Switzerland) · 2022Article
- Prediction and Diagnosis of Respiratory Disease by Combining Convolutional Neural Network and Bi-directional Long Short-Term Memory Methods.Frontiers in public health · 2022Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An increasing number of patients infected with nontuberculous mycobacteria (NTM) are observed worldwide. However, it is challenging to identify NTM lung diseases from pulmonary tuberculosis (PTB) due to considerable overlap in classic manifestations and clinical and radiographic characteristics. This study quantifies both cavitary and bronchiectasis regions in CT images and explores a machine learning approach for the differentiation of NTM lung diseases and PTB. It involves 116 patients and 103 quantitative features. After the selection of informative features, a linear support vector machine performs disease classification, and simultaneously, discriminative features are recognized. Experimental results indicate that bronchiectasis is relatively more informative, and two features are figured out due to promising prediction performance (area under the curve, 0.84 ± 0.06; accuracy, 0.85 ± 0.06; sensitivity, 0.88 ± 0.07; and specificity, 0.80 ± 0.12). This study provides insight into machine learning-based identification of NTM lung diseases from PTB, and more importantly, it makes early and quick diagnosis of NTM lung diseases possible that can facilitate lung disease management and treatment planning.
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Registered trials
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