Retinal imaging and AI for non-invasive detection of diabetic macrovascular complications.

BACKGROUND: Diabetes mellitus accelerates vascular degeneration and increases the risk of major macrovascular complications, including Peripheral Arterial Disease (PAD) and aortic pathologies, collectively termed Peripheral Arterial and Aortic Diseases (PAAD). These conditions are strongly associated with adverse cardiovascular outcomes but often remain underdiagnosed in diabetic populations due to asymptomatic progression and limited access to early screening. OBJECTIVE: This study aims to develop and validate a non-invasive, artificial intelligence (AI)-based screening framework using retinal fundus imaging for early detection of PAAD by exploiting retinal microvascular features as systemic biomarkers. METHODS: A hybrid diagnostic pipeline integrated simulated Optical Coherence Tomography (OCT)-like structural features (retinal thickness, texture entropy, vessel density factor, and layer separation index), handcrafted vascular biomarkers, and an attention-enhanced VGG16 backbone with Convolutional Block Attention Modules (VGG16 + CBAM). Multiple Instance Learning (MIL) improved lesion-level discrimination in weakly labeled datasets. Multi-level feature fusion aggregated spatial, physiological, and morphological descriptors. High-resolution fundus datasets from public and clinical cohorts were used for training and validation. Model interpretability was ensured using SHapley Additive exPlanations (SHAP) and Gradient-weighted Class Activation Mapping (Grad-CAM). RESULTS: The framework achieved an accuracy of 94.6%, sensitivity of 90.5%, specificity of 96.2%, and an AUC-ROC of 0.973 on an independent test set. SHAP identified retinal thickness and texture entropy as dominant predictors, while Grad-CAM highlighted vessel bifurcations and arteriolar narrowing, consistent with PAAD pathophysiology. The average inference time was 150 ms per image on GPU, enabling real-time use. CONCLUSION: This interpretable AI-based system demonstrates high diagnostic performance for PAAD detection using retinal imaging. It offers a non-invasive, cost-effective, and scalable alternative to conventional vascular assessments and may support earlier diagnosis and improved prevention of cardiovascular and cerebrovascular complications.