Retinal Image Analysis for Heart Disease Risk Prediction: A Deep Learning Approach

Abstract

Heart disease is considered as one of the leading causes of death worldwide. Predicting heart diseases from retinal fundus images is a promising approach in the early detection and monitoring of cardiovascular health conditions. The change in the retinal microvasculature is an indication towards systemic diseases such as cardiovascular diseases and hypertension. This study aims to explore the potential use of deep learning for early detection and prediction of cardiovascular health. through retinal images. The connection between the heart and the small blood vessels are called microvasculature. Imaging the retinal vessels provides a noninvasive way to study the cardiovascular system. By leveraging the potential of Convolutional Neural Network, retinal images are analyzed to identify patterns and anomalies which strongly correlates with cardiovascular conditions. Experimental results show that our method improves the accuracy in prediction of heart diseases, hence opens a novel and improved non-invasive approach to predict Cardiovascular diseases.