Development and Validation of an Artificial Intelligence-Based Tool to Detect Subclinical Atheroesclerosis Using Non-Mydriatic Retinal Funduscopic Images: The Aitheroscope Project
Juan Torres-Macho, María Ángeles Sánchez-Uriz, Vyara Hrystova, Jesús Prada-Alonso, Jose Manuel López-Aragonés, Nuria Muñoz-Rivas, Anabel Franco-Moreno, Eva 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, María José Crespo-CarballésAbstract
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.