Deep learning-based automatic differentiation of acute angle closure with or without zonulopathy using ultrasound biomicroscopy: a comparison of diagnostic performance with ophthalmologists
AI models and ophthalmologists effectively differentiated AAC with or without zonulopathy. However, when evaluated in terms of both accuracy and efficiency, the AI model showed superior comprehensive diagnostic performance, demonstrating high clinical applicability for preoperative diagnosis.
Authors
- Jiaming Hong (ORCID: https://orcid.org/0000-0002-7007-8482)
- Lu Cheng (ORCID: https://orcid.org/0000-0001-6051-8782)
- Hailiu Chen
- Shaopeng Liu (ORCID: https://orcid.org/0000-0002-9624-846X)
- Yangyunhui Li
- E.Q. Wu (ORCID: https://orcid.org/0000-0002-7951-6485)
- Jingjing Huang (ORCID: https://orcid.org/0000-0002-3009-8681)
- Haotian Lin
- Shufen Lin
- Yunlan Ling
Institutions
- Guangzhou University of Chinese Medicine (CN)
- Sun Yat-sen University (CN)
- Chinese University of Hong Kong (HK)
- Shantou University (CN)
- Guangdong Polytechnic Normal University (CN)
- Foshan Second People's Hospital (CN)
- Eye Care Associates (US)
Publication Details
- Journal
- BMJ Open Ophthalmology
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1136/bmjophth-2024-002114
- Primary Topic
- Glaucoma and retinal disorders
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Guangdong Province