Revolutionizing cardiovascular risk prediction: A novel image-based approach using fundus analysis and deep learning

Abstract

Worldwide, CVDs continue to stand as a prominent contributor to mortality. Detecting and assessing CVD risk early is paramount for effective prevention and management. Medical imaging has gained prominence as a tool for evaluating CVD risk factors. In this study, a novel Inception v3 with a VGG16 is proposed to forecast cardiovascular risk rates via readily available and non-invasive fundus images. This approach harnesses advanced image analysis techniques encompassing contrast enhancement and noise reduction. The blood vessel segmentation and Optic disc detection of the pertinent features are extracted from the fundus images. In this context, the Inception v3 architecture is initially employed to capture intricate hierarchical patterns within the images. Alternatively, explore the utilization of the VGG16 architecture. By integrating these features with clinical data, the model is then trained to predict cardiovascular risk rates. Empirical findings underscore the method's remarkable accuracy in risk rate prediction. This non-invasive, image-based methodology holds transformative potential for reshaping early diagnosis and risk management approaches for cardiovascular diseases. Ultimately, this innovation stands to enhance patient care and outcomes.