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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2508.12506 |
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| _version_ | 1866913995930206208 |
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| author | Moya-Sánchez, E. Ulises Sánchez-Perez, Abraham Da Veiga, Raúl Nanclares Zarate-Macías, Alejandro Villareal, Edgar Sánchez-Montes, Alejandro Jauregui-Ulloa, Edtna Moreno, Héctor Cortés, Ulises |
| author_facet | Moya-Sánchez, E. Ulises Sánchez-Perez, Abraham Da Veiga, Raúl Nanclares Zarate-Macías, Alejandro Villareal, Edgar Sánchez-Montes, Alejandro Jauregui-Ulloa, Edtna Moreno, Héctor Cortés, Ulises |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_12506 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Design and Validation of a Responsible Artificial Intelligence-based System for the Referral of Diabetic Retinopathy Patients Moya-Sánchez, E. Ulises Sánchez-Perez, Abraham Da Veiga, Raúl Nanclares Zarate-Macías, Alejandro Villareal, Edgar Sánchez-Montes, Alejandro Jauregui-Ulloa, Edtna Moreno, Héctor Cortés, Ulises Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Design and Validation of a Responsible Artificial Intelligence-based System for the Referral of Diabetic Retinopathy Patients |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2508.12506 |