Artificial Intelligence-assisted Diagnosis in Ophthalmology
Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorMarisse Masis-Solano
About this trial
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.
Eligibility criteria
This trial accepts healthy volunteersQualifiers
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)
Disqualifiers
Images from eyes with recent intraocular surgery (<3 months)
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
Trial population
Ophthalmic medical images from adult patients (≥18 years) who received eye care at two ophthalmology centers in Costa Rica (Asociados de Mácula y Vítreo de Costa Rica in San José, and Centro Ocular in Heredia). Images were captured during routine clinical care for various clinical indications, including routine screening, follow-up visits, and diagnostic evaluations. The study population represents the real-world spectrum of patients seeking ophthalmology care in these centers, including healthy individuals, patients with various stages of eye diseases, and patients with multiple ocular pathologies.
Trial design
Design model
Other
Time perspective
Retrospective
Treatments tested in this trial
No interventions
Other interventionThis 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.
Treatment groups
15,000 Participants
are divided into 1 treatment groupGroup A: Images of patients over 18 years old1 intervention
Trial outcomes
Primary outcomes
1
Area Under ROC Curve (AUC)
Area under the receiver operating characteristic curve (AUC-ROC) for each of the pathologies detection by the AI system, evaluated on the independent validation set of 3,000 images. AUC-ROC is a comprehensive measure of diagnostic performance across all possible decision thresholds. Values range from 0.5 (random guessing) to 1.0 (perfect classification). Success criterion: AUC ≥ 0.90.
Time frame
At study completion (Month 24)2
Specificity
Specificity (true negative rate) of the AI system for glaucoma detection, defined as the proportion of non-glaucoma cases correctly identified as negative. Success criterion: Specificity ≥ 85%.
Time frame
At study completion (Month 24)Secondary outcomes
1
Sensitivity
Sensitivity (recall/true positive rate) of the AI system for ophthalmic pathologies, defined as the proportion of true glaucoma cases correctly identified. Success criterion: Sensitivity ≥ 85%.
Time frame
At study completion (Month 24)Sponsors and contacts
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Source ClinicalTrials.gov
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