Deep Learning Model to Detect Diabetic Retinopathy in 45° Images Using Ground Truth from Ultra-Widefield Imaging.

PURPOSE: To evaluate the performance of a deep learning (DL) model in classifying diabetic retinopathy (DR) severity using fundus images with varying fields of view and to assess whether central retinal features alone reflect overall disease burden. The study also investigates vascular biomarkers within regions contributing to model decisions to explore biological basis of inferences and enhance clinical interpretability.

DESIGN

An observational, cross-sectional study.

PARTICIPANTS

A total of 2610 participants aged ≥40 years from a population-based study in South India, with ocular and systemic data, including dilated fundus images and glycated hemoglobin levels.

METHODS

Diabetic retinopathy severity was graded on unmasked 200° ultra-widefield (UWF) and centrally masked 45° images, with peripheral lesions annotated in the 155° field (200°-45°). Convolutional neural network was trained on labeled images and evaluated on both datasets. Performance metrics and receiver operating characteristic (ROC) curves were calculated. Gradient-weighted class activation mapping (Grad-CAM) identified model focus. Vascular biomarkers (tortuosity, fractal dimension, and vessel density) were quantified using LWNet and Fiji software and compared between eyes with and without peripheral lesions.

MAIN OUTCOME MEASURES: Classification accuracy of DR severity, precision and recall, ROC curves, Grad-CAM visualization of regions of model focus, and vascular biomarkers associated with peripheral lesions.

RESULTS

The DL model achieved high classification accuracy: 97.12% for UWF images, 97.24% for 45° masked images with re-evaluated labels, and 96.86% for masked images with original labels. Peripheral pathology, including microaneurysms (19.8%) and hemorrhages/exudates (9.1%), did not affect accuracy. Deep learning-based assessment of 45° fundus images reduced DR underestimation from 16.5% to 6.2%. Gradient-weighted class activation mapping highlighted focus on central regions, including areas without visible lesions. Vascular analysis revealed differences in vessel density and tortuosity between eyes with and without peripheral microvascular abnormalities and neovascularization, suggesting detection of subclinical vascular changes linked to peripheral disease.

CONCLUSIONS

Deep learning applied to 45° fundus images can accurately classify DR and detect subtle vascular biomarkers predictive of peripheral disease. This proof-of-concept highlights the potential of artificial intelligence (AI)-enhanced 45° imaging as a scalable tool for DR screening. Such AI-powered approaches, using accessible and affordable fundus cameras, may enable cost-effective detection and triage of high-risk cases in primary care and resource-limited settings.

FINANCIAL DISCLOSURES: The authors have no proprietary or commercial interest in any materials discussed in this article.