Artificial Intelligence-assisted Diagnosis in Ophthalmology

Sponsor
Marisse Masis-Solano
Study ID
NCT07497815
Status
Not Yet Recruiting

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Conditions

Eligibility Criteria

Sex
ALL
Age
18 Years - N/A
Healthy Volunteers
Accepted

Interventions

  • No interventions — OTHER
    This retrospective observational study involves no therapeutic interventions, no treatment modifications, no patient contact, and no comparison groups. It is purely diagnostic technology development and validation using existing historical data.

Study Details

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.

Key Dates

First listed
Mar 27, 2026
Start date
May 1, 2026
Status verified
Mar 2026
Primary completion
May 1, 2028
Completion
May 1, 2029

Study Design

Enrollment
15,000 participants (estimated)

Arms

  • Arm: Images of patients over 18 years old
    This is a diagnostic validation study without intervention. All images are analyzed using the same methodology. There are no comparison groups, treatment arms, or cohorts. The study evaluates AI system performance against expert ophthalmologist diagnoses (ground truth).

Primary Outcome Measure

Area Under ROC Curve (AUC) [ Time Frame: At study completion (Month 24) ]

Central Contacts

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