DiabEye-Q: AI-Driven Longitudinal Analysis of Ophthalmoscopic Images for Early Diabetes Prediction in Qatari Adults

Sulaiman Khan, Md. Rafiul Biswas, Zubair Shah

Abstract: Despite significant advances in diabetes classification, early prediction of diabetes onset using longitudinal multimodal data remains underexplored. In this study, we integrate clinical data with ophthalmoscopic images to predict diabetes onset among Qatari adults. his longitudinal study performs case—control analysis of 2,041 participants, including 1,076 males (246 cases, 830 controls) and 965 females (184 cases, 781 controls). We investigated the relationship between retinal features and diabetes status by extracting fractal geometry features and employing ANOVA for statistical validation. Furthermore, we develop a pipelined architecture that fuses XGBoost with a vision transformer (ViT) to identify risk factors associated with diabetes development. Age-stratified analysis reveals that while the model tends to overpredict diabetes in younger individuals (22–40 years), prediction accuracy improves markedly in older age groups (particularly 56–68 years). Additionally, gender analysis indicates a higher predisposition for diabetes among males compared to females. Our integrated model outperforms both standalone ViT and XGBoost, achieving 88.08% accuracy, 93% AUROC, and 88% recall. These findings underscore the potential of ophthalmoscopic imaging as a rapid, non-invasive screening tool for early diabetes detection.

External IDs:dblp:conf/aiih/KhanBS25