Retinal Image Analysis for Cardiovascular Risk Prediction: A Deep Learning Perspective

Cardio vascular diseases (CVDs) continue to be a leading cause of death globally, underscoring the urgent need for effective methods to predict risks and intervene early. Utilizing retinal imaging, a non-invasive and readily accessible technique, shows promise for assessing cardiovascular risk. This project investigates the use of deep learning techniques to analyze retinal images for predictive biomarkers linked to cardiovascular health. By employing convolutional neural networks (CNNs) and other advanced deep learning models, our research aims to create robust predictive models capable of detecting subtle vascular changes and abnormalities associated with cardiovascular risk factors. This proposed framework not only streamlines risk assessment but also provides insights into the underlying pathological mechanisms contributing to CVD progression. Through rigorous validation and performance assessments, we aim to showcase the potential of deep learning-based retinal image analysis as a valuable tool for cardiovascular risk assessment and personalized healthcare.