Building an AI tool to detect eye diseases using existing eye images
Tests an AI system for spotting eye diseases in existing images from Costa Rica.
Plain-language title generated from the registry record
Registry title: Artificial Intelligence-assisted Diagnosis in Ophthalmology
Key facts
- Where
- Heredia and San José, Costa RicaSee the 2 sites
Where in Costa Rica
- Centro OcularHeredia, Costa RicaNo longer taking part
- Asociados de Mácula y Vítreo de Costa RicaSan José, Costa RicaNo longer taking part
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- Marisse Masis-Solano
- Trial code
- NCT07497815
- Record updated
- 1 April 2026
- Original description
This is a retrospective, multicenter, observational study designed to develop and validate an artificial intelligence (AI) system capable of detecting and classifying major ophthalmic diseases (glaucoma, cataract, diabetic retinopathy, and other retinal pathologies) in the Costa Rican population. The study will use approximately 15,000 existing medical images from digital archives of two ophthalmic centers in Costa Rica, without active participant recruitment or capture of new images.Read full original descriptionShow less
Development and Validation of an Artificial Intelligence-assisted Diagnostic System for Ophthalmic Pathologies
This is a retrospective, multicenter, observational study designed to develop and validate an artificial intelligence (AI) system capable of detecting and classifying major ophthalmic diseases (glaucoma, cataract, diabetic retinopathy, and other retinal pathologies) in the Costa Rican population. The study will use approximately 15,000 existing medical images from digital archives of two ophthalmic centers in Costa Rica, without active participant recruitment or capture of new images.
The primary motivation is that AI systems developed in other countries (primarily Asian, European, or North American populations) do not necessarily perform with the same accuracy when applied to Latin American populations. This study seeks to establish a precedent for the importance of locally validating any medical AI technology before clinical implementation.
Inclusion: Image corresponds to patient ≥18 years of age at time of capture; Image modality is one of: fundus photography, posterior segment OCT, anterior segment photography, automated perimetry, or video-OCT; Image quality sufficient for diagnostic interpretation (adequate resolution, focus, illumination, complete visualization of anatomical area of interest, no major artifacts); Minimum clinical data available (age or age group, sex, and diagnosis or clinical indication); Image captured during routine clinical care (not specifically for research); No patient objection to use of medical data for research (when applicable per center policy).
Exclusion: Images from eyes with recent intraocular surgery (<3 months); Images in non-standard or corrupted formats that cannot be processed; Sex/Gender: All Minimum Age: 18 Years Maximum Age: No limit Accepts Healthy Volunteers: Yes (images of healthy eyes without pathology are included as controls); CLINICAL:; Images from eyes with severe ocular trauma distorting anatomy; Images from patients with rare or unique ocular pathologies not allowing generalization; Images post-recent laser treatment where acute changes may confuse analysis; TECHNICAL:; Severely degraded image quality (extreme blur, severe under/overexposure, major artifacts preventing interpretation); Duplicate images of same eye on same date; Images with missing or clearly erroneous metadata.