International Scientific Journal of Engineering and Management · Jul 16, 2026
Reti-Cardio: An AI-Driven Multi model Oculo-mics System for Retinal Disease Classification and Cardiovascular Risk Profiling
By Nagakeerthi M, SRUTHI B
ABSTRACT Cardiovascular disease (CVD) remains the leading cause of global mortality, with conventional diagnostic methods being invasive, costly, and fundamentally unsuitable for population-scale early screening. The retina offers a unique non-invasive window into systemic vascular health, with morphological changes in retinal microvasculature — including arteriole-to-venule ratio (AVR), vessel tortuosity, bifurcation angle, and fractal dimension — serving as clinically validated biomarkers for hypertension, coronary artery disease, diabetic retinopathy, glaucoma, and age-related macular degeneration. Despite substantial evidence establishing these oculo-mics associations, no unified research framework currently exists that simultaneously performs quantitative retinal vascular morphometry and multi-class ocular disease classification within a single, explainable, and clinically interpretable system. This paper proposes Reti Cardio, a multimodal oculo-mics framework integrating classical computer-vision-based vascular morphometry with transfer-learning-based deep classification for simultaneous cardiovascular risk profiling and ocular disease detection from a single retinal fundus photograph. A dual-pathway architecture is implemented: a classical image-processing pipeline extracts and quantifies four vascular morphometric parameters mapped to established clinical thresholds, while an EfficientNet-B0 model with two-phase transfer learning performs five-class ocular disease classification (AMD, Diabetic Retinopathy, Glaucoma, Hypertensive Retinopathy, and Normal) on a consolidated dataset of approximately 3,400 retinal fundus images drawn from RFM id, MAPLES-DR, STARE, and ORIGA. The system achieves a final validation accuracy of 93.1% and a system AUROC of 0.941, with Gradient-weighted Class Activation Mapping (Grad-CAM) confirming anatomically appropriate model attention. A Framingham-inspired cardiovascular risk scoring module, a live AUROC performance tracking mechanism, and a rule-based Oculo-mics AI Diagnostic Chatbot are integrated within a Flask-based web interface, together establishing Reti-Cardio as a novel, deployable contribution to population-scale cardiovascular and ophthalmic screening. Keywords: Oculo-mics, Retinal Fundus Analysis, EfficientNet-B0, Transfer Learning, Arteriole-to-Venule Ratio, Grad-CAM, Explainable AI, Cardiovascular Risk Profiling, Framingham Risk Score, Diabetic Retinopathy, Glaucoma, AMD, Hypertensive Retinopathy.