Multimodal radiomics integrating deep learning and clinical features for diagnosing multidrug-resistant tuberculosis in HIV/AIDS patients
The multimodal integrated model developed in this study significantly improved the diagnostic efficacy of MDR-TB in HIV/AIDS patients by combining clinical, radiomics, and deep learning features, providing a reliable tool for individualized precision diagnosis and treatment.
Authors
- Shulin Song
- Chong‐Rui Xu (ORCID: https://orcid.org/0000-0002-3474-2809)
- Chunyan Zhao (ORCID: https://orcid.org/0000-0002-4905-0952)
- Lemin Wen
- Yanrong Lin
- Ai-Chun Huang
- Qingdong Zhu (ORCID: https://orcid.org/0009-0003-3368-5296)
- Chang Song
- Hang-Biao Qiang
Institutions
- Guangxi Medical University (CN)
- The Fourth People's Hospital (CN)
- The Second Nanning People's Hospital (CN)
Publication Details
- Journal
- Journal of Global Antimicrobial Resistance
- Published
- 2025-05-03
- DOI
- https://doi.org/10.1016/j.jgar.2025.04.013
- Citations
- 6
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 8.16