Development and validation of an artificial intelligence-based tool to detect subclinical atheroesclerosis using...

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.

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.

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.