Deep Learning-Based Quality Control and Diagnosis of Bronchial Images
The findings highlight AI's significant potential to innovate bronchoscopic image analysis. However, current research has limitations, particularly in the generalizability of models. Future work must focus on multicenter clinical validation to optimize model robustness and on developing real-time decision support systems to ultimately standardize bronchoscopic procedures and improve diagnostic efficiency.
Authors
- Yong Zhou (ORCID: https://orcid.org/0000-0001-7812-8280)
- Felix Herth (ORCID: https://orcid.org/0000-0002-7638-2506)
- Fengjuan Li
- Jia Liu (ORCID: https://orcid.org/0000-0002-5684-9594)
- Huafeng Cai (ORCID: https://orcid.org/0000-0002-3919-1807)
- Chao Ruan
- Yuchen Li (ORCID: https://orcid.org/0000-0002-1594-2717)
- Jianying Li (ORCID: https://orcid.org/0000-0002-9825-672X)
Institutions
- Heidelberg University (DE)
- University Hospital Heidelberg (DE)
- Shanghai Electric (China) (CN)
- Xi'an Chest Hospital (CN)
Publication Details
- Journal
- Respiration
- Published
- 2025-11-27
- DOI
- https://doi.org/10.1159/000548342
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00