1. Artificial intelligence-based radiographic extent analysis to predict tuberculosis treatment outcomes: a multicenter cohort study.
- Author
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Kim, Hyung-Jun, Kwak, Nakwon, Yoon, Soon Ho, Park, Nanhee, Kim, Young Ran, Lee, Jae Ho, Lee, Ji Yeon, Park, Youngmok, Kang, Young Ae, Kim, Saerom, Mok, Jeongha, Kim, Joong-Yub, Jeon, Doosoo, Lee, Jung-Kyu, and Yim, Jae-Joon
- Subjects
ARTIFICIAL intelligence ,TREATMENT effectiveness ,TUBERCULOSIS ,COHORT analysis ,ODDS ratio ,RADIOSCOPIC diagnosis - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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