Revolutionizing Cardiovascular Risk Prediction: IoT-Enhanced Retinal Imaging With Deep Learning

Abstract

Cardiovascular diseases (CVDs) remain the leading cause of global mortality, emphasizing the need for accurate and accessible predictive tools for early risk assessment. This study introduces the Hierarchical Residual Fusion Network (HRFN), an advanced deep learning framework designed to estimate 10-year CVD risk using paired retinal images. The proposed architecture leverages residual learning and dual-pooling mechanisms to extract complex vascular features that reflect systemic cardiovascular health. Trained and validated on the ODIR-5 K dataset, HRFN achieves a coefficient of determination of 0.7169, surpassing traditional models in predictive accuracy. Designed for clinical applicability, the framework ensures precise and interpretable risk estimations to support early medical interventions. Additionally, this study envisions future integration with Internet of Things (IoT)-enabled medical systems, enabling real-time data acquisition and analysis for scalable deployment. By combining AI-driven predictive modeling with emerging healthcare technologies, this approach contributes to advancing personalized medicine and improving global cardiovascular health outcomes.