Development and Validation of an Artificial Intelligence-assisted Diagnostic System for Ophthalmic Pathologies

Background and Rationale Ophthalmic diseases, including glaucoma, diabetic retinopathy, and cataract, represent a major public health burden both globally and in Costa Rica. Early detection is critical for all of these conditions, yet it faces persistent challenges: glaucoma is asymptomatic in its early stages, diabetic retinopathy requires annual screening that overwhelms available ophthalmology capacity, image interpretation is time-consuming and subject to inter-observer variability, and in resource-limited settings the supply of expert ophthalmologists is insufficient to screen all at-risk populations.

Advances in deep learning have demonstrated strong capability in ophthalmic image analysis, with published studies showing AI systems achieving diagnostic accuracy comparable to expert ophthalmologists across multiple disease categories. However, most AI systems have been developed and validated in Asian, European, or North American populations. These systems may not generalize well to Latin American populations due to differences in disease prevalence patterns, demographic characteristics, imaging equipment and protocols, and healthcare system structures. Applying AI systems developed elsewhere without local validation is scientifically questionable and potentially unsafe. This study addresses that gap by developing and validating an AI system specifically for the Costa Rican population.

Study Design and Setting This is a retrospective diagnostic technology validation study conducted at two ophthalmology centers in Costa Rica: Asociados de Mácula y Vítreo de Costa Rica in San José (contributing approximately 10,000 images) and Centro Ocular in Heredia (contributing approximately 5,000 images). The study will use approximately 15,000 existing ophthalmic images from adult patients who received care at these centers during routine clinical practice. No temporal restrictions are applied; all available historical images meeting quality and modality criteria will be included to maximize data volume and representativeness. There is no active participant recruitment and no new images will be captured. Image modalities include fundus photography (color and autofluorescence), optical coherence tomography (OCT) of the posterior segment, anterior segment photography, automated perimetry (visual fields), and video-OCT.

Study Procedures Image Extraction (Months 1-6). All available ophthalmic images meeting inclusion criteria will be automatically extracted from the Picture Archiving and Communication Systems (PACS) at each site.

Anonymization (Months 1-6). A rigorous anonymization protocol will be applied. All direct identifiers (name, national ID number, address, phone, email, medical record number) will be removed and each image will be assigned a random anonymous code. Potentially identifying data will be transformed: exact birth dates will be converted to age groups (18-40, 41-60, 61-75, >75) and exact dates will be reduced to year only. An encrypted linkage table will be stored locally at each site solely for ethical emergencies, such as incidental findings requiring patient notification. Non-identifying clinical data will be preserved, including age group, sex, diagnosis, intraocular pressure, best-corrected visual acuity, diabetes mellitus history, and recent ocular surgery history.