International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Cardiovascular diseases (CVDs) are the leading cause of global mortality, necessitating early and accurate detection methods to improve patient outcomes. Traditional diagnostic approaches, such as ECGs and angiograms, are often invasive, costly, or require specialized expertise, making non-invasive alternatives highly desirable. Recent advancements in artificial intelligence (AI) and machine learning (ML) have enabled the analysis of retinal images for heart disease prediction, leveraging the structural and functional similarities between retinal vasculature and coronary arteries. Retinal imaging techniques, such as fundus photography and optical coherence tomography (OCT), allow for non-invasive visualization of microvascular changes linked to cardiovascular conditions. ML models, including convolutional neural networks (CNNs) and hybrid deep learning architectures, can effectively analyze these images to detect abnormalities indicative of heart disease. This review explores various datasets, feature extraction methods, and classification techniques used in retinal image analysis for cardiovascular risk assessment, comparing their effectiveness in predictive modelling. Despite promising advancements, challenges such as data availability, model generalizability, explainability, and clinical integration remain critical. Future research should focus on developing robust, interpretable AI models, enhancing dataset quality, and addressing real-world implementation barriers to establish retinal imaging as a reliable tool for early heart disease detection.

Keywords: Heart Disease, Retinal Imaging, Machine Learning, Deep Learning, Cardiovascular Disease, Medical Image Processing.

Downloads: | DOI: 10.17148/IJARCCE.2025.14936 a