Deep Learning to Predict the Future Growth of Geographic Atrophy from Fundus Autofluorescence
Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
Authors
- Pearse A. Keane (ORCID: https://orcid.org/0000-0002-9239-745X)
- Frank Gerhard Holz (ORCID: https://orcid.org/0000-0001-8019-9272)
- Julia Cluceru
- Neha Anegondi (ORCID: https://orcid.org/0000-0003-2154-945X)
- Aaron Lee (ORCID: https://orcid.org/0000-0002-7452-1648)
- Christina Rabe
- Daniela Ferrara (ORCID: https://orcid.org/0000-0002-1380-7562)
- Simon S. Gao (ORCID: https://orcid.org/0000-0002-7020-037X)
- Courtney Schiffman (ORCID: https://orcid.org/0000-0003-4126-9033)
- Anish Salvi
- Qi Yang (ORCID: https://orcid.org/0000-0002-4483-2007)
Institutions
- Moorfields Eye Hospital NHS Foundation Trust (GB)
- University of Bonn (DE)
- University of Washington (US)
- National Institute for Health and Care Research (GB)
- Doheny Eye Institute (US)
- University College London (GB)
Publication Details
- Journal
- Ophthalmology Science
- Published
- 2024-10-23
- DOI
- https://doi.org/10.1016/j.xops.2024.100635
- Citations
- 3
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 1.21
Funders
- Genentech