Design and Validation of a Responsible Artificial Intelligence-based System for the Referral of Diabetic Retinopathy Patients

• URL: http://arxiv.org/abs/2508.12506v1
• Date: Sun, 17 Aug 2025 21:54:11 GMT
• Title: Design and Validation of a Responsible Artificial Intelligence-based System for the Referral of Diabetic Retinopathy Patients
• Authors: E. Ulises Moya-Sánchez, Abraham Sánchez-Perez, Raúl Nanclares Da Veiga, Alejandro Zarate-Macías, Edgar Villareal, Alejandro Sánchez-Montes, Edtna Jauregui-Ulloa, Héctor Moreno, Ulises Cortés,
• Abstract summary: Early detection of Diabetic Retinopathy can reduce the risk of vision loss by up to 95%.<n>We developed RAIS-DR, a Responsible AI System for DR screening that incorporates ethical principles across the AI lifecycle.<n>We evaluated RAIS-DR against the FDA-approved EyeArt system on a local dataset of 1,046 patients, unseen by both systems.
• Score: 65.57160385098935
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diabetic Retinopathy (DR) is a leading cause of vision loss in working-age individuals. Early detection of DR can reduce the risk of vision loss by up to 95%, but a shortage of retinologists and challenges in timely examination complicate detection. Artificial Intelligence (AI) models using retinal fundus photographs (RFPs) offer a promising solution. However, adoption in clinical settings is hindered by low-quality data and biases that may lead AI systems to learn unintended features. To address these challenges, we developed RAIS-DR, a Responsible AI System for DR screening that incorporates ethical principles across the AI lifecycle. RAIS-DR integrates efficient convolutional models for preprocessing, quality assessment, and three specialized DR classification models. We evaluated RAIS-DR against the FDA-approved EyeArt system on a local dataset of 1,046 patients, unseen by both systems. RAIS-DR demonstrated significant improvements, with F1 scores increasing by 5-12%, accuracy by 6-19%, and specificity by 10-20%. Additionally, fairness metrics such as Disparate Impact and Equal Opportunity Difference indicated equitable performance across demographic subgroups, underscoring RAIS-DR's potential to reduce healthcare disparities. These results highlight RAIS-DR as a robust and ethically aligned solution for DR screening in clinical settings. The code, weights of RAIS-DR are available at https://gitlab.com/inteligencia-gubernamental-jalisco/jalisco-retinopathy with RAIL.

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