Deep Learning Estimation of 24-2 Visual Field Map From Optic Nerve Head Optical Coherence Tomography Angiography
DL models from OCTA images demonstrated high accuracy in estimating 24-2 VF maps by leveraging information from ONH layers. By extending the application of DL to OCTA images using RPC or superficial layers, it may be possible to reduce the frequency of VF testing to individual patients.
Authors
- Golnoush Mahmoudinezhad (ORCID: https://orcid.org/0000-0002-9233-9882)
- Sasan Moghimi (ORCID: https://orcid.org/0000-0002-9375-4711)
- Kareem Latif (ORCID: https://orcid.org/0000-0002-2417-7981)
- Jiacheng Cheng
- Nuno Vasconcelos (ORCID: https://orcid.org/0000-0002-9024-4302)
- Robert Neal Weinreb (ORCID: https://orcid.org/0000-0001-9553-3202)
- Evan Walker (ORCID: https://orcid.org/0000-0001-8566-0371)
- Linda M. Zangwill (ORCID: https://orcid.org/0000-0002-1143-5224)
- Kelvin Du
- Siavash Beheshtaein (ORCID: https://orcid.org/0000-0002-1686-9865)
- Gopikasree Gunasegaran (ORCID: https://orcid.org/0009-0008-6717-9363)
- Dongchen Yang
- Takashi Nishida (ORCID: https://orcid.org/0000-0001-8220-2738)
- Mark Christopher
- Yu Xuan Yong
- Liyang Ru
Institutions
- University of California San Diego (US)
- The California Eye Institute (US)
- Veris Technologies (United States) (US)
Publication Details
- Journal
- Journal of Glaucoma
- Published
- 2025-09-09
- DOI
- https://doi.org/10.1097/ijg.0000000000002626
- Citations
- 1
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 1.36