Camera-Agnostic Autonomous Diagnosis of Glaucomatous Optic Neuropathy using Macular Fundus Imaging and Machine Learning
The algorithm achieved high sensitivity and specificity in detecting glaucomatous optic neuropathy using macula-centered fundus images, demonstrating its potential for integration into diverse clinical settings. Its camera-agnostic design and robust performance offer a scalable solution for improving glaucoma screening pathways, making them more accessible and efficient.
Authors
- Rachelle Aviv (ORCID: https://orcid.org/0000-0002-0350-9238)
- Zack Dvey-Aharon (ORCID: https://orcid.org/0000-0002-4211-9937)
- Moshe Livne
- Tsontcho Sean Ianchulev
- Catherine Lalman
- Joel S. Schuman
- Kristen Ann V. Mendoza
- Dan Margalit
Institutions
- Thomas Jefferson University (US)
- New York Eye and Ear Infirmary (US)
- Drexel University (US)
- McKinsey & Company (United States) (US)
- Wills Eye Hospital (US)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- medRxiv
- Published
- 2026-01-22
- DOI
- https://doi.org/10.64898/2026.01.20.26344470
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institutes of Health
- National Eye Institute