Integrating Retinal Imaging and AI for Early Cardiovascular Disease Prediction

Abstract: Cardiovascular diseases (CVDs) are among the leading causes of death worldwide, creating a strong need for early and reliable prediction techniques. The human retina serves as a non-invasive window to the body’s vascular system, offering valuable insights into cardiovascular health through measurable biomarkers. This study presents an integrated approach that combines retinal imaging and artificial intelligence (AI) to predict cardiovascular diseases at an early stage. High-quality retinal fundus images are collected and preprocessed to enhance vessel clarity and reduce noise. Deep learning models, especially convolutional neural networks (CNNs), are then employed to automatically identify key retinal features such as vessel diameter, tortuosity, and microvascular changes linked to cardiovascular risks. To further enhance predictive performance, the system incorporates both image-based biomarkers and clinical parameters including age, blood pressure, and cholesterol levels. Experimental results show that this AI-assisted retinal analysis provides high diagnostic accuracy, enabling timely medical intervention and reducing the chances of severe cardiovascular events. Overall, the proposed framework demonstrates the potential of combining retinal imaging with AI as a powerful, affordable, and non-invasive tool for early cardiovascular disease prediction.