DEEP LEARNING METHODS FOR CARDIOVASCULAR RISK ASSESSMENT WITH RETINAL IMAGES

Cardiovascular diseases (CVDs) remain a leading cause of mortality worldwide, necessitating accurate risk assessment for early intervention. Deep learning methodologies offer promising avenues for such assessments, leveraging retinal images as a non-invasive proxy for cardiovascular health. This paper surveys recent advancements in deep learning techniques for cardiovascular risk assessment utilizing retinal images. It explores convolutional neural networks (CNNs), transfer learning, and ensemble methods, highlighting their efficacy in feature extraction and risk prediction. Key challenges such as data scarcity and interpretability are addressed, alongside potential solutions. By integrating machine learning with medical imaging, this research contributes to enhanced early detection and management of CVDs, fostering improved patient outcomes.