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Artificial intelligence-based radiographic extent analysis to predict tuberculosis treatment outcomes: a multicenter cohort study
DC Field | Value | Language |
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dc.contributor.author | 강영애 | - |
dc.contributor.author | 박영목 | - |
dc.date.accessioned | 2024-07-18T05:05:21Z | - |
dc.date.available | 2024-07-18T05:05:21Z | - |
dc.date.issued | 2024-06 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/199994 | - |
dc.description.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. | - |
dc.description.statementOfResponsibility | open | - |
dc.language | English | - |
dc.publisher | Nature Publishing Group | - |
dc.relation.isPartOf | SCIENTIFIC REPORTS | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Adult | - |
dc.subject.MESH | Aged | - |
dc.subject.MESH | Antitubercular Agents / therapeutic use | - |
dc.subject.MESH | Artificial Intelligence* | - |
dc.subject.MESH | Female | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Male | - |
dc.subject.MESH | Middle Aged | - |
dc.subject.MESH | Mycobacterium tuberculosis / drug effects | - |
dc.subject.MESH | Mycobacterium tuberculosis / isolation & purification | - |
dc.subject.MESH | Radiography, Thoracic / methods | - |
dc.subject.MESH | Republic of Korea | - |
dc.subject.MESH | Retrospective Studies | - |
dc.subject.MESH | Rifampin / therapeutic use | - |
dc.subject.MESH | Sputum* / microbiology | - |
dc.subject.MESH | Tomography, X-Ray Computed / methods | - |
dc.subject.MESH | Treatment Outcome | - |
dc.subject.MESH | Tuberculosis, Pulmonary* / diagnostic imaging | - |
dc.subject.MESH | Tuberculosis, Pulmonary* / drug therapy | - |
dc.title | Artificial intelligence-based radiographic extent analysis to predict tuberculosis treatment outcomes: a multicenter cohort study | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Internal Medicine (내과학교실) | - |
dc.contributor.googleauthor | Hyung-Jun Kim | - |
dc.contributor.googleauthor | Nakwon Kwak | - |
dc.contributor.googleauthor | Soon Ho Yoon | - |
dc.contributor.googleauthor | Nanhee Park | - |
dc.contributor.googleauthor | Young Ran Kim | - |
dc.contributor.googleauthor | Jae Ho Lee | - |
dc.contributor.googleauthor | Ji Yeon Lee | - |
dc.contributor.googleauthor | Youngmok Park | - |
dc.contributor.googleauthor | Young Ae Kang | - |
dc.contributor.googleauthor | Saerom Kim | - |
dc.contributor.googleauthor | Jeongha Mok | - |
dc.contributor.googleauthor | Joong-Yub Kim | - |
dc.contributor.googleauthor | Doosoo Jeon | - |
dc.contributor.googleauthor | Jung-Kyu Lee | - |
dc.contributor.googleauthor | Jae-Joon Yim | - |
dc.identifier.doi | 10.1038/s41598-024-63885-0 | - |
dc.contributor.localId | A00057 | - |
dc.contributor.localId | A05828 | - |
dc.relation.journalcode | J02646 | - |
dc.identifier.eissn | 2045-2322 | - |
dc.identifier.pmid | 38849439 | - |
dc.subject.keyword | Artificial intelligence | - |
dc.subject.keyword | Pulmonary | - |
dc.subject.keyword | Radiography | - |
dc.subject.keyword | Thoracic | - |
dc.subject.keyword | Treatment outcome | - |
dc.subject.keyword | Tuberculosis | - |
dc.contributor.alternativeName | Kang, Young Ae | - |
dc.contributor.affiliatedAuthor | 강영애 | - |
dc.contributor.affiliatedAuthor | 박영목 | - |
dc.citation.volume | 14 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 13162 | - |
dc.identifier.bibliographicCitation | SCIENTIFIC REPORTS, Vol.14(1) : 13162, 2024-06 | - |
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