ArticleScientific reports2024
Artificial intelligence-based radiographic extent analysis to predict tuberculosis treatment outcomes: a multicenter cohort study.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Comorbidity of chronic obstructive pulmonary disease and pulmonary tuberculosis: a bibliometric analysis (2011-2025) with narrative review.Journal of thoracic disease · 2026Article
- Divergent Evolution of Tuberculosis Lesions During Treatment: A Longitudinal CT-Based Analysis of Progression and Regression Patterns.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence in the Diagnosis and Management of Pulmonary Tuberculosis: A Review of Current Applications and Future Perspectives.Therapeutics and clinical risk management · 2026Review
- Machine Learning-based Prediction of Active Tuberculosis in People With HIV Using Clinical Data.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2025Article
- Artificial intelligence for tuberculosis control: a scoping review of applications in public health.Journal of global health · 2025Article
- Tuberculosis Care Quality Assessment: Evaluating Diagnosis and Treatment Effectiveness in Korea, 2018 to 2022.Tuberculosis and respiratory diseases · 2025Article
- Global Research Trends, Hotspots, Impacts, and Emergence of Artificial Intelligence and Machine Learning in Health and Medicine: A 25-Year Bibliometric Analysis.Healthcare (Basel, Switzerland) · 2025Review
- A Comprehensive Review of the Diagnostics for Pediatric Tuberculosis Based on Assay Time, Ease of Operation, and Performance.Microorganisms · 2025Review
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15 authors.
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Abstract
Predicting outcomes in pulmonary tuberculosis is challenging despite effective treatments. This study aimed to identify factors influencing treatment success and culture conversion, focusing on artificial intelligence (AI)-based chest X-ray analysis and Xpert MTB/RIF assay cycle threshold (Ct) values. In this retrospective study across six South Korean referral centers (January 1 to December 31, 2019), we included adults with rifampicin-susceptible pulmonary tuberculosis confirmed by Xpert assay from sputum samples. We analyzed patient characteristics, AI-based tuberculosis extent scores from chest X-rays, and Xpert Ct values. Of 230 patients, 206 (89.6%) achieved treatment success. The median age was 61 years, predominantly male (76.1%). AI-based radiographic tuberculosis extent scores (median 7.5) significantly correlated with treatment success (odds ratio [OR] 0.938, 95% confidence interval [CI] 0.895-0.983) and culture conversion at 8 weeks (liquid medium: OR 0.911, 95% CI 0.853-0.973; solid medium: OR 0.910, 95% CI 0.850-0.973). Sputum smear positivity was 49.6%, with a median Ct of 26.2. However, Ct values did not significantly correlate with major treatment outcomes. AI-based radiographic scoring at diagnosis is a significant predictor of treatment success and culture conversion in pulmonary tuberculosis, underscoring its potential in personalized patient management.
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