Predicting Cardiovascular Disease Risk Using Retinal Optical Coherence Tomography Imaging

Cynthia Maldonado-Garcia^1*Rodrigo Bonazzola¹Enzo Ferrante²Thomas H Julian³Panagiotis I Sergouniotis³Nishant Ravikumar¹Alejandro F Frangi³

• ¹University of Leeds, Leeds, United Kingdom
• ²Research Institute for Signals, Systems and Comp. Intelligence, sinc(i), CONICET-UNL, Santa Fe, Argentina
• ³University of Manchester, Manchester, United Kingdom

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. We analysed 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. 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. In this study, we investigated the potential of OCT as a predictive imaging modality for future CVD events.