Shaping the future of myopia: artificial intelligence for vitreoretinal complications of high and pathologic myopia
Researchers have demonstrated excellent results for developing DL models that accurately classify and segment retinal pathologies associated with myopia. However, despite strong performance, additional work is needed to translate these models into clinical use, including robust external validation, calibration for clinical decision-making, and prospective evaluation, particularly for longitudinal prognostication of incident complications in pathologic myopia.
Authors
- Vinit B. Mahajan (ORCID: https://orcid.org/0000-0003-1886-1741)
- Chase A. Ludwig (ORCID: https://orcid.org/0000-0002-9586-3735)
- Curtis P. Langlotz (ORCID: https://orcid.org/0000-0002-8972-8051)
- Yeabsira Mesfin
- Leo Arnal
- Anish Salvi
Institutions
- Palo Alto University (US)
- University of California, San Francisco (US)
- Stanford Medicine (US)
- Smith-Kettlewell Eye Research Institute (US)
- University of San Francisco (US)
- Stanford University (US)
Publication Details
- Journal
- Graefe s Archive for Clinical and Experimental Ophthalmology
- Published
- 2026-02-04
- DOI
- https://doi.org/10.1007/s00417-025-07098-9
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Research to Prevent Blindness
- National Eye Institute