Artificial Intelligence Diagnosis of Ocular Motility Disorders From Clinical Videos
This proof-of-concept study demonstrates the feasibility of using Gemini 2.0 for automated recognition of EOM abnormalities in clinical videos. Although performance was stronger in overt cases, overall diagnostic accuracy remains limited. Substantial validation on standardized, clinician-annotated datasets is needed before clinical application.
Authors
- Michael Balas (ORCID: https://orcid.org/0000-0002-5948-0331)
- David Mikhail (ORCID: https://orcid.org/0009-0009-0831-1915)
Institutions
- Toronto Western Hospital (CA)
Publication Details
- Journal
- Journal of Neuro-Ophthalmology
- Published
- 2025-08-28
- DOI
- https://doi.org/10.1097/wno.0000000000002393
- Citations
- 1
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 1.36