Integration of ultra-widefield optical coherence tomography angiography images and clinical indicators for identifying early-stage diabetic retinopathy in type 2 diabetes
TL;DR
Integrating UW-OCTA with clinical factors improved early-stage DR identification in patients with type 2 diabetes mellitus and may support non-invasive risk assessment.
Abstract
Background Diabetic retinopathy (DR) is a common microvascular complication of diabetes and a leading cause of irreversible vision loss among working-age adults, making early detection essential for preventing permanent visual impairment. This study aimed to identify early-stage DR in patients with type 2 diabetes mellitus (T2DM) using a multimodal deep learning (DL) approach integrating ultra-widefield optical coherence tomography angiography (UW-OCTA) images with clinical factors. Methods This cross-sectional study enrolled patients with T2DM from an urban tertiary hospital and a community primary care setting between July 2024 and April 2026. Baseline demographic and laboratory data and 26 × 21 mm UW-OCTA images were obtained. Automatically segmented UW-OCTA slabs were compared to identify the most discriminative layer. Clinical variables were selected using between-group comparisons, XGBoost-based Shapley additive explanations (SHAP), and multivariable logistic regression. Selected clinical variables and UW-OCTA image features were integrated into a multimodal model. Performance was evaluated in internal test and external validation sets using receiver operating characteristic analysis. Gradient-weighted class activation mapping (Grad-CAM) was used for visualization. Results Overall, 1,676 patients were included: 1,047 and 453 tertiary-care patients formed the training and internal test sets, and 176 community-based patients formed the external validation set. Diabetes duration, glycated hemoglobin, low-density lipoprotein cholesterol and systolic blood pressure were independently associated with early-stage DR. The retinal slab showed the best image-only performance with AUCs of 0.906 (95%CI: 0.870-0.942) and 0.828 (95%CI: 0.725-0.931) in the internal and external sets. The multimodal model achieved AUCs of 0.921 (95%CI: 0.887-0.954) and 0.849 (95%CI: 0.760-0.953), outperforming image-only and clinical-only models. Grad-CAM highlighted macular and peripheral retinal regions. Conclusion Integrating UW-OCTA with clinical factors improved early-stage DR identification in T2DM and may support non-invasive risk assessment.