{"id":3168,"date":"2026-09-15T11:06:40","date_gmt":"2026-09-15T14:06:40","guid":{"rendered":"https:\/\/icc.fcen.uba.ar\/?p=3168"},"modified":"2026-09-15T11:06:40","modified_gmt":"2026-09-15T14:06:40","slug":"predicting-cardiovascular-disease-risk-using-retinal-optical-coherence-tomography-imaging","status":"publish","type":"post","link":"https:\/\/icc.fcen.uba.ar\/en\/predicting-cardiovascular-disease-risk-using-retinal-optical-coherence-tomography-imaging\/","title":{"rendered":"Predicting cardiovascular disease risk using retinal optical coherence tomography imaging"},"content":{"rendered":"<div class=\"fusion-fullwidth fullwidth-box fusion-builder-row-1 fusion-flex-container has-pattern-background has-mask-background nonhundred-percent-fullwidth non-hundred-percent-height-scrolling\" style=\"--awb-border-radius-top-left:0px;--awb-border-radius-top-right:0px;--awb-border-radius-bottom-right:0px;--awb-border-radius-bottom-left:0px;--awb-flex-wrap:wrap;\" ><div class=\"fusion-builder-row fusion-row fusion-flex-align-items-flex-start fusion-flex-content-wrap\" style=\"max-width:1144px;margin-left: calc(-4% \/ 2 );margin-right: calc(-4% \/ 2 );\"><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-0 fusion_builder_column_1_1 1_1 fusion-flex-column\" style=\"--awb-bg-size:cover;--awb-width-large:100%;--awb-margin-top-large:0px;--awb-spacing-right-large:1.92%;--awb-margin-bottom-large:20px;--awb-spacing-left-large:1.92%;--awb-width-medium:100%;--awb-order-medium:0;--awb-spacing-right-medium:1.92%;--awb-spacing-left-medium:1.92%;--awb-width-small:100%;--awb-order-small:0;--awb-spacing-right-small:1.92%;--awb-spacing-left-small:1.92%;\"><div class=\"fusion-column-wrapper fusion-column-has-shadow fusion-flex-justify-content-flex-start fusion-content-layout-column\"><div class=\"fusion-text fusion-text-1\"><p>Authors: Cynthia Maldonado-Garcia, Rodrigo Bonazzola, Enzo Ferrante, Thomas H Julian, Panagiotis I Sergouniotis, Nishant Ravikumar, Alejandro F Frangi.<\/p>\n<p>Abstract: Cardiovascular Diseases (CVD) are the leading cause of death globally. Non-invasive, cost-effective imaging techniques play a crucial role in early detection and prevention of CVD. Optical Coherence Tomography (OCT) has gained recognition as a noninvasive method of detecting microvascular alterations that might enable earlier identification and targeting of at-risk patients. In this study, we investigated the potential of OCT as an additional imaging technique to predict future CVD events.<br \/>\nMethods: We analyzed retinal OCT data from the UK Biobank. The dataset included 612 patients who suffered a Myocardial Infarction (MI) or stroke within five years of imaging and 2,234 controls without CVD (total: 2,846 participants). A self-supervised deep learning approach based on Variational Autoencoders (VAE) was used to extract low-dimensional latent representations from high-dimensional 3D OCT images, capturing structural and morphological features of retinal and choroidal layers. These latent features, along with clinical data, were used to train a Random Forest (RF) classifier to differentiate between patients at risk of future CVD events (MI or stroke) and healthy controls.<br \/>\nResults: Our model achieved an AUC of 0.75, sensitivity of 0.70, specificity of 0.70, and accuracy of 0.70. The choroidal layer in OCT images was identified as a key predictor of future CVD events, revealed through a novel model explainability approach.<br \/>\nDiscussion: Our findings demonstrate the potential of retinal OCT imaging, when combined with advanced deep learning methods, as a predictive tool for identifying individuals at increased risk of CVD events.<\/p>\n<p>More information:\u00a0<a href=\"https:\/\/www.frontiersin.org\/journals\/artificial-intelligence\/articles\/10.3389\/frai.2025.1624550\/full\" target=\"_blank\" rel=\"noopener\">https:\/\/www.frontiersin.org\/journals\/artificial-intelligence\/articles\/10.3389\/frai.2025.1624550\/full<\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":9,"featured_media":3169,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[98],"tags":[],"class_list":["post-3168","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-papers"],"_links":{"self":[{"href":"https:\/\/icc.fcen.uba.ar\/en\/wp-json\/wp\/v2\/posts\/3168","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/icc.fcen.uba.ar\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/icc.fcen.uba.ar\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/icc.fcen.uba.ar\/en\/wp-json\/wp\/v2\/users\/9"}],"replies":[{"embeddable":true,"href":"https:\/\/icc.fcen.uba.ar\/en\/wp-json\/wp\/v2\/comments?post=3168"}],"version-history":[{"count":1,"href":"https:\/\/icc.fcen.uba.ar\/en\/wp-json\/wp\/v2\/posts\/3168\/revisions"}],"predecessor-version":[{"id":3170,"href":"https:\/\/icc.fcen.uba.ar\/en\/wp-json\/wp\/v2\/posts\/3168\/revisions\/3170"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/icc.fcen.uba.ar\/en\/wp-json\/wp\/v2\/media\/3169"}],"wp:attachment":[{"href":"https:\/\/icc.fcen.uba.ar\/en\/wp-json\/wp\/v2\/media?parent=3168"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/icc.fcen.uba.ar\/en\/wp-json\/wp\/v2\/categories?post=3168"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/icc.fcen.uba.ar\/en\/wp-json\/wp\/v2\/tags?post=3168"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}