ArticleQuantitative imaging in medicine and surgery2022
Differentiating between drug-sensitive and drug-resistant tuberculosis with machine learning for clinical and radiological features.
Article in Quantitative imaging in medicine and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Interpretable multimodal machine learning for diagnosis of drug-resistant tuberculosis.Frontiers in digital health · 2026Article
- A Nomogram Predictive Model for Drug-Resistant Tuberculosis Detection by GeneXpert MTB/RIF Assay in Paediatric Patients.Infection and drug resistance · 2026Article
- Cost-effectiveness of BPaL/BPaLM as compared to mixed standard of care bedaquiline containing regimen for MDR/RR-TB.The Indian journal of medical research · 2025Article
- Accuracy of Machine Learning in Identifying Drug Resistance in Tuberculosis: A Systematic Review and Meta-Analysis.Health science reports · 2025Review
- Post-TB sequelae in adolescent pulmonary TB survivors.IJTLD open · 2025Article
- Explainable machine learning for early predicting treatment failure risk among patients with TB-diabetes comorbidity.Scientific reports · 2024Article
- Chinese expert consensus on imaging diagnosis of drug-resistant pulmonary tuberculosis.Quantitative imaging in medicine and surgery · 2024Review
- Differences in pulmonary nodular consolidation and pulmonary cavity among drug-sensitive, rifampicin-resistant and multi-drug resistant tuberculosis patients: the Guangzhou computerized tomography study.Quantitative imaging in medicine and surgery · 2024Article
- From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning.Military Medical Research · 2023Review
- Combining Radiological and Genomic TB Portals Data for Drug Resistance Analysis.IEEE access : practical innovations, open solutions · 2023Article
- Data Characterization for Reliable AI in Medicine.Recent trends in image processing and pattern recognition : 5th International Conference, RTIP2R 2022, Kingsville, TX, USA, December 01-02, 2022, revised selected papers. International Conference on Recent Trends in Image Processing and... · 2023Article
- Clinical and Computed Tomography Features Associated with Multidrug-Resistant Pulmonary Tuberculosis: A Retrospective Study in China.Infection and drug resistance · 2023Article
- Drug-Resistant Tuberculosis Treatment Recommendation, and Multi-Class Tuberculosis Detection and Classification Using Ensemble Deep Learning-Based System.Pharmaceuticals (Basel, Switzerland) · 2022Article
- Patterns of genomic interrelatedness of publicly available samples in the TB portals database.Tuberculosis (Edinburgh, Scotland) · 2022Article
- Generalization Challenges in Drug-Resistant Tuberculosis Detection from Chest X-rays.Diagnostics (Basel, Switzerland) · 2022Article
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9 authors.
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Abstract
backgroundTuberculosis (TB) drug resistance is a worldwide public health problem that threatens progress made in TB care and control. Early detection of drug resistance is important for disease control, with discrimination between drug-resistant TB (DR-TB) and drug-sensitive TB (DS-TB) still being an open problem. The objective of this work is to investigate the relevance of readily available clinical data and data derived from chest X-rays (CXRs) in DR-TB prediction and to investigate the possibility of applying machine learning techniques to selected clinical and radiological features for discrimination between DR-TB and DS-TB. We hypothesize that the number of sextants affected by abnormalities such as nodule, cavity, collapse and infiltrate may serve as a radiological feature for DR-TB identification, and that both clinical and radiological features are important factors for machine classification of DR-TB and DS-TB.
methodsWe use data from the NIAID TB Portals program (https://tbportals.niaid.nih.gov), 1,455 DR-TB cases and 782 DS-TB cases from 11 countries. We first select three clinical features and 26 radiological features from the dataset. Then, we perform Pearson's chi-squared test to analyze the significance of the selected clinical and radiological features. Finally, we train machine classifiers based on different features and evaluate their ability to differentiate between DR-TB and DS-TB.
resultsPearson's chi-squared test shows that two clinical features and 23 radiological features are statistically significant regarding DR-TB
conclusionsOur study suggests that the number of affected lung sextants can be used for predicting DR-TB, and that automatic discrimination between DR-TB and DS-TB is possible, with a combination of clinical features and radiological features providing the best performance.
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