{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T15:37:02Z","timestamp":1788017822578,"version":"build-2784847793"},"reference-count":110,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T00:00:00Z","timestamp":1763078400000},"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. Cardiovasc. Med."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Cardiovascular disease (CVD) is the leading cause of death worldwide, and early prediction and prevention are essential to reduce its incidence. In recent years, Artificial Intelligence (AI) techniques have made significant progress in medical imaging analysis, especially in predicting CVD risk from retinal imaging.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>As of August 2025, we searched using several electronic databases including PubMed, Web Of Science Core Collection. Screening was performed based on inclusion and exclusion criteria, and 43 papers were finally selected.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>AI shows great potential in predicting CVD risk from retinal imaging [optical coherence tomography (OCT), optical coherence tomography angiography (OCTA), and color fundus photography (CFP)]. Non-invasive eye examinations combined with AI analysis offer the potential for mass screening and early warning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>AI has made significant progress in the field of CVD assisted diagnosis and risk assessment using retinal imaging. Single-modality models have achieved high accuracy, while multimodal models have further enhanced performance. However, challenges remain, including reliance on single-center data and insufficient generalization capabilities. Future steps include building multi-center datasets, developing dynamic risk models, and promoting portable devices for underserved regions. While promising for early CVD prevention, interdisciplinary collaboration is needed to improve generalizability, standardization, and interpretability for higher clinical value.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcvm.2025.1615857","type":"journal-article","created":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T14:49:31Z","timestamp":1763131771000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Research advances on artificial intelligence assisted diagnosis and risk assessment in cardiovascular disease using retinal 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