AI-based prediction of best-corrected visual acuity in patients with multiple retinal diseases using multimodal medical imaging
Multimodal fusion provided the most accurate BCVA predictions, demonstrating AI's potential to improve clinical evaluation. However, challenges remain regarding disease diversity and applicability in resource-limited settings.
Authors
- Luyuan Niu
- Ruiheng Zhang (ORCID: https://orcid.org/0000-0002-1324-4739)
- Heyan Li (ORCID: https://orcid.org/0009-0002-6558-4724)
- Wen‐Da Zhou
- Zheng Gong (ORCID: https://orcid.org/0000-0003-4327-3423)
- Weimin Gao (ORCID: https://orcid.org/0000-0001-5522-7809)
- Li Dong (ORCID: https://orcid.org/0000-0003-0120-5756)
- Zhuo Deng (ORCID: https://orcid.org/0000-0002-8907-588X)
- Lei Shao (ORCID: https://orcid.org/0000-0001-6776-9787)
- Wenbin Wei (ORCID: https://orcid.org/0000-0003-2386-0989)
- Li-Jian Fang
- Lan Ma
Institutions
- Beijing Tongren Hospital (CN)
- Tsinghua–Berkeley Shenzhen Institute (CN)
- Tang Hospital (CN)
- Tsinghua University (CN)
Publication Details
- Journal
- British Journal of Ophthalmology
- Published
- 2025-08-14
- DOI
- https://doi.org/10.1136/bjo-2025-327189
- Citations
- 1
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 1.36