Development and Validation of Deep Learning–Based Infectivity Prediction in Pulmonary Tuberculosis Through Chest Radiography: Retrospective Study
This proposed AI tool offers a rapid and accurate alternative for evaluating PTB infectivity through CXR, with significant implications for enhancing screening efficiency by evaluating infectivity before sputum test results in clinical settings, compared with traditional smear and culture tests.
Authors
- Dukyong Yoon (ORCID: https://orcid.org/0000-0003-1635-8376)
- Songsoo Kim (ORCID: https://orcid.org/0000-0002-6908-4324)
- Young Ae Kang (ORCID: https://orcid.org/0000-0002-7783-5271)
- Wou Young Chung (ORCID: https://orcid.org/0000-0002-5435-2787)
- Ji Eun Park (ORCID: https://orcid.org/0000-0002-3035-6353)
- Yujeong Kim (ORCID: https://orcid.org/0000-0003-3674-993X)
- Jinsik Yoon (ORCID: https://orcid.org/0009-0001-8351-3797)
Institutions
- Yonsei University (KR)
- Severance Hospital (KR)
- Yonsei University Health System (KR)
- Ajou University (KR)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2024-09-12
- DOI
- https://doi.org/10.2196/58413
- Citations
- 5
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 2.02