{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T16:24:56Z","timestamp":1787243096749,"version":"build-2736575974"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T00:00:00Z","timestamp":1786320000000},"content-version":"vor","delay-in-days":12,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"AITHEROSCOPE"},{"name":"InnovaRCV price"},{"DOI":"10.13039\/100015758","name":"Novo Nordisk Pharma","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100015758","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>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\u2013based tool using retinal fundus images to detect ultrasound-confirmed subclinical atherosclerosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and results<\/jats:title>\n                    <jats:p>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).<\/jats:p>\n                    <jats:p>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\u20130.87), with sensitivity of 88.2%. In the enhanced mode incorporating clinical variables, performance improved to an AUC of 0.86 (95% CI 0.80\u20130.92), 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/ehjdh\/ztag129","type":"journal-article","created":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T11:09:43Z","timestamp":1786100983000},"source":"Crossref","is-referenced-by-count":0,"title":["Development and validation of an artificial intelligence-based tool to detect subclinical atheroesclerosis using non-mydriatic retinal funduscopic images: the Aitheroscope project"],"prefix":"10.1093","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8860-6837","authenticated-orcid":false,"given":"Juan","family":"Torres-Macho","sequence":"first","affiliation":[{"name":"Internal Medicine Department, Infanta Leonor University Hospital , Av Gran v\u00eda del este 80, Madrid 28031 ,","place":["Spain"]},{"name":"Department of Medicine, Complutense University , 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