{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T07:16:27Z","timestamp":1788419787985,"version":"build-2803163510"},"reference-count":24,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>AI-enabled retinal imaging provides a non-invasive method for assessing cardiovascular risk by identifying microvascular changes linked to atherosclerotic cardiovascular disease. This study assessed the feasibility and clinical associations of the AI-derived retinal risk score (Reti-CVD) in a hybrid care model and compared its categorical alignment with the American Heart Association PREVENT equation as a comparator risk model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this retrospective cross-sectional study, 1,461 adults aged \u226540 at Metabolic underwent non-mydriatic retinal imaging. Fundus photographs were analyzed using the Dr. Noon AI platform to classify Reti-CVD risk. Associations with cardiometabolic characteristics were examined, and categorical agreement with PREVENT was assessed using Cohen\u2019s kappa. ROC analyses evaluated the alignment of the continuous Reti-CVD score with PREVENT thresholds.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Higher Reti-CVD risk was significantly linked to older age, male sex, diabetes, hypertension, elevated HbA1c, higher blood pressure, lower HDL cholesterol, higher triglycerides, and older AI-predicted vascular age (all\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.001). After adjustment, age, female sex, and current smoking were independent predictors. The Reti-CVD score showed moderate discrimination for PREVENT-defined risk thresholds (AUC 0.689\u20130.757) and slight to fair agreement with PREVENT (\n                      <jats:italic>\u03ba<\/jats:italic>\n                      \u202f=\u202f0.144; weighted \u03ba\u202f=\u202f0.241). Among patients classified as low risk by PREVENT, 49.1% were categorized as moderate or high by Reti-CVD.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>AI-enabled retinal imaging was feasible in a hybrid setting and was associated with clinically plausible cardiometabolic risk gradients. Reti-CVD showed limited categorical agreement with PREVENT. However, the findings are hypothesis-generating and require prospective validation against ASCVD events before claims, improved prediction, or beneficial reclassification.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/frai.2026.1920911","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T06:44:59Z","timestamp":1788417899000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-powered retinal imaging for early ASCVD risk stratification in a hybrid care model: preliminary findings from SAFER"],"prefix":"10.3389","volume":"9","author":[{"given":"Hala","family":"Zakaria","sequence":"first","affiliation":[{"name":"Metabolic","place":["Dubai, United Arab Emirates"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juman","family":"Ali","sequence":"additional","affiliation":[{"name":"Metabolic","place":["Dubai, United Arab Emirates"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed Gouda","family":"Ibrahim","sequence":"additional","affiliation":[{"name":"Metabolic","place":["Dubai, United Arab Emirates"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Idalys","family":"Roman","sequence":"additional","affiliation":[{"name":"Metabolic","place":["Dubai, United Arab Emirates"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Hashemi","sequence":"additional","affiliation":[{"name":"Metabolic","place":["Dubai, United Arab Emirates"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ihsan","family":"Almarzooqi","sequence":"additional","affiliation":[{"name":"Metabolic","place":["Dubai, United Arab Emirates"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,3]]},"reference":[{"key":"ref1","year":"2025"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"e177","DOI":"10.1016\/j.jacc.2019.03.010","article-title":"2019 ACC\/AHA guideline on the primary prevention of cardiovascular disease: a report of the American College of Cardiology\/American Heart Association task force on clinical practice guidelines","volume":"74","author":"Arnett","year":"2019","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref3","doi-asserted-by":"publisher","first-page":"e230860","DOI":"10.1148\/radiol.230860","article-title":"Using AI to improve radiologist performance in detection of abnormalities on chest radiographs","volume":"309","author":"Bennani","year":"2023","journal-title":"Radiology"},{"key":"ref4","year":"2025"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"352","DOI":"10.4103\/ijd.IJD_418_20","article-title":"Use of artificial intelligence in dermatology","volume":"65","author":"De","year":"2020","journal-title":"Indian J. Dermatol."},{"key":"ref6","doi-asserted-by":"publisher","first-page":"598","DOI":"10.1093\/eurheartj\/ehw301","article-title":"Risk score overestimation: the impact of individual cardiovascular risk factors and preventive therapies on the performance of the American Heart Association-American College of Cardiology-Atherosclerotic Cardiovascular Disease risk score in a modern multi-ethnic cohort","volume":"38","author":"DeFilippis","year":"2017","journal-title":"Eur. Heart J."},{"key":"ref7","doi-asserted-by":"publisher","first-page":"2935","DOI":"10.1016\/j.jacc.2013.11.005","article-title":"ACC\/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology\/American Heart Association task force on practice guidelines","volume":"63","author":"Goff","year":"2014","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref8","doi-asserted-by":"publisher","first-page":"j2099","DOI":"10.1136\/bmj.j2099","article-title":"Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study","author":"Hippisley-Cox","year":"2017","journal-title":"BMJ"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1161\/CIRCULATIONAHA.123.067626","article-title":"Development and validation of the American Heart Association\u2019s PREVENT equations","volume":"149","author":"Khan","year":"2024","journal-title":"Circulation"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1093\/jamia\/ocad199","article-title":"Pivotal trial of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from CMERC-HI","volume":"31","author":"Lee","year":"2024","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref11","doi-asserted-by":"publisher","first-page":"101095","DOI":"10.1016\/j.xcrm.2023.101095","article-title":"Artificial intelligence in ophthalmology: the path to the real-world clinic","volume":"4","author":"Li","year":"2023","journal-title":"Cell Rep. Med."},{"key":"ref12","doi-asserted-by":"publisher","first-page":"896366","DOI":"10.3389\/fcvm.2022.896366","article-title":"Artificial intelligence in coronary CT angiography: current status and future prospects","volume":"9","author":"Liao","year":"2022","journal-title":"Front. Cardiovasc. Med."},{"key":"ref13","doi-asserted-by":"publisher","first-page":"653789","DOI":"10.1155\/2013\/653789","article-title":"Impact of diabetes on cardiovascular disease: an update","volume":"2013","author":"Matheus","year":"2013","journal-title":"Int. J. Hypertens."},{"key":"ref14","year":"2025"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.clinimag.2017.11.007","article-title":"Artificial intelligence and deep learning\u2013radiology's next frontier?","volume":"49","author":"Mayo","year":"2018","journal-title":"Clin. Imaging"},{"key":"ref16","volume-title":"Acute Myocardial Infarction (Nursing)","author":"Mechanic","year":"2023"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1038\/s41551-018-0195-0","article-title":"Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning","volume":"2","author":"Poplin","year":"2018","journal-title":"Nat. Biomed. Eng."},{"key":"ref18","doi-asserted-by":"publisher","first-page":"e010763","DOI":"10.1161\/circoutcomes.123.010763","article-title":"PREVENT equations: a new era in cardiovascular disease risk assessment","volume":"17","author":"Razavi","year":"2024","journal-title":"Circ. Cardiovasc. Qual. Outcomes"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"101288","DOI":"10.1016\/j.ajpc.2025.101288","article-title":"Cardiovascular disease risk estimates using the new PREVENT equation: the good, bad, and the ugly","volume":"24","author":"Selvam","year":"2025","journal-title":"Am. J. Prev. Cardiol."},{"key":"ref20","volume-title":"Acute Stroke","author":"Tadi","year":"2023"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"4133365","DOI":"10.1155\/2024\/4133365","article-title":"Major limitations of cardiovascular risk scores","volume":"2024","author":"Talha","year":"2024","journal-title":"Cardiovasc. Ther."},{"key":"ref22","first-page":"2361","volume-title":"The Global Burden of Cardiovascular Diseases and risk: a Compass for Future Health","author":"Vaduganathan","year":"2022"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1001\/jama.2012.9624","article-title":"Comparison of novel risk markers for improvement in cardiovascular risk assessment in intermediate-risk individuals","volume":"308","author":"Yeboah","year":"2012","journal-title":"JAMA"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"104622","DOI":"10.1016\/j.numecd.2026.104622","article-title":"Global burden and causal analysis of LDL-C-related cardiovascular diseases: a GBD 2021 and Mendelian randomization study","volume":"36","author":"Zhang","year":"2026","journal-title":"Nutr. Metab. Cardiovasc. Dis."}],"container-title":["Frontiers in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2026.1920911\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T06:45:00Z","timestamp":1788417900000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2026.1920911\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,3]]},"references-count":24,"alternative-id":["10.3389\/frai.2026.1920911"],"URL":"https:\/\/doi.org\/10.3389\/frai.2026.1920911","relation":{},"ISSN":["2624-8212"],"issn-type":[{"value":"2624-8212","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,3]]},"article-number":"1920911"}}