AI-assisted differentiation of nontuberculous mycobacterial pulmonary disease from colonization: a multi-center study
Accurately distinguishing nontuberculous mycobacteria (NTM) disease status is clinically important but challenging. The NTMNet model effectively differentiated the NTM disease status and matched the performance of the pulmonologists. The NTMNet model could be a potential diagnostic tool for patients with respiratory NTM isolates.
Authors
- Lu-Cheng Kuo (ORCID: https://orcid.org/0000-0003-4247-5197)
- Jann‐Yuan Wang (ORCID: https://orcid.org/0000-0003-3406-366X)
- Chong‐Jen Yu (ORCID: https://orcid.org/0000-0001-5664-9392)
- Yi-Jie Liu
- Yu‐Sen Huang (ORCID: https://orcid.org/0000-0003-4262-8712)
- Chia-Jung Liu
- Meng‐Rui Lee (ORCID: https://orcid.org/0000-0002-7220-4833)
- Yu‐Hsuan Chen (ORCID: https://orcid.org/0000-0002-6634-2746)
- Po‐Chih Kuo (ORCID: https://orcid.org/0000-0003-4020-3147)
- Jin-Yuan Shih
Institutions
- National Taiwan University (TW)
- National Tsing Hua University (TW)
- Min Sheng General Hospital (TW)
- National Taiwan University Hospital (TW)
Publication Details
- Journal
- Insights into Imaging
- Published
- 2025-11-09
- DOI
- https://doi.org/10.1186/s13244-025-02131-1
- Primary Topic
- Mycobacterium research and diagnosis
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Aromatic Plant Research Center
- National Tsing Hua University
- National Taiwan University Hospital
- International Union Against Tuberculosis and Lung Disease
- National Taiwan University
- National Science and Technology Council