Development and Validation of an Artificial Intelligence-Based Tool to Detect Subclinical Atheroesclerosis Using Non-Mydriatic Retinal Funduscopic Images: The Aitheroscope Project
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ID: 324578
2026
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Abstract
Abstract Aims Current cardiovascular risk scores may underestimate the risk of future cardiovascular events, particularly in younger individuals with prolonged exposure to risk factors. In contrast, imaging-based detection of subclinical atherosclerosis provides a more accurate assessment of cardiovascular risk by identifying established vascular disease. We aimed to develop and prospectively validate an artificial intelligence–based tool using retinal fundus images to detect ultrasound-confirmed subclinical atherosclerosis. Methods and results In this prospective observational study, 931 participants (mean age 52.6 years; 70.2% women) without prior cardiovascular disease underwent standardized clinical evaluation, non-mydriatic retinal imaging, and carotid and femoral ultrasound to detect subclinical atherosclerosis. A multimodal AI model integrating deep learning from retinal images with radiomic and clinical data was developed in a derivation cohort (n=781) and evaluated in a held-out prospective test set (n=150). Subclinical atherosclerosis was present in 50.8% of participants. In the prospective test set, the AI model demonstrated good discrimination. In image-only mode, the model achieved an area under the curve (AUC) of 0.80 (95% CI 0.73–0.87), with sensitivity of 88.2%. In the enhanced mode incorporating clinical variables, performance improved to an AUC of 0.86 (95% CI 0.80–0.92), with sensitivity of 93.4% and a negative predictive value of 90.6%. Discrimination was higher in younger individuals and those at low-to-intermediate cardiovascular risk. Conclusion AI-based retinal image analysis enables non-invasive detection of systemic subclinical atherosclerosis. This scalable approach may enhance early identification of high cardiovascular-risk patients, particularly in populations in whom risk is underestimated.
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| Authors | Juan Torres‐Macho, María Ángeles Sánchez-Uriz, Vyara Hrystova, Jesús Prada-Alonso, José Manuel López-Aragonés, Nuria Muñoz-Rivas, Anabel Franco-Moreno, E. Moya-Mateo, María Pilar Herrera-Ahijado, Eva María Duro-Perales, María Antón-Vallejo, Montserrat Saiz-González, Alberto Díez-García, Celeste Marina-Verde, Andrés Rosa‐López, Inmaculada Fernández-Sotillo, Carolina Espejo‐Paeres, Miguel Bravo-Prieto, Javier Corrochano del Pino, Carmen Pantoja-Zarza, M.J. Crespo Carballés |
| Journal | European Heart Journal - Digital Health |
| Year | 2026 |
| DOI |
10.1093/ehjdh/ztag129
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| URL | |
| Keywords | Keywords not found |
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