Deep Learning–Based Detection of Papilledema on Retinal Photographs From Handheld Cameras: A Prospective Study
A DLS trained on a large dataset of mydriatic photographs achieved excellent diagnostic performance for detection of papilledema and other optic disc abnormalities when applied to nonmydriatic retinal photographs acquired with a handheld camera in real life conditions.
Authors
- Dan Miléa (ORCID: https://orcid.org/0000-0002-6388-8639)
- Valérie Biousse (ORCID: https://orcid.org/0000-0002-3594-850X)
- Raymond P. Najjar (ORCID: https://orcid.org/0000-0002-3770-2300)
- Nancy J. Newman (ORCID: https://orcid.org/0000-0003-4898-7839)
- Shweta Singhal (ORCID: https://orcid.org/0000-0003-4990-3115)
- Jing Liang Loo
- Zhiqun Tang
- Ayse Gungor
- Ilias Sarbout (ORCID: https://orcid.org/0009-0003-4437-7897)
- Sharon T. L. Choon
- Reuben F. C. Ming
- Louis Tadayoni
Institutions
- Centre National de la Recherche Scientifique (FR)
- Inserm (FR)
- Sorbonne Université (FR)
- Assistance Publique – Hôpitaux de Paris (FR)
- Fondation de Rothschild (FR)
- Institut du Cerveau (FR)
Publication Details
- Journal
- Journal of Neuro-Ophthalmology
- Published
- 2025-08-28
- DOI
- https://doi.org/10.1097/wno.0000000000002394
- Citations
- 1
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 1.36