26-LB: Identifying the Risk of Diabetic Retinopathy Progression Using Machine Learning on Ultrawide Field Retinal Images

Abstract

Estimating the risk of diabetic retinopathy (DR) progression is one of the most important and difficult tasks when caring for individuals with diabetic eye disease. Current DR severity scales have informed clinicians on the progression risk and provided recommendations for follow-up and treatment. The use of artificial intelligence (AI) algorithms may improve in this process. In this study, we developed and validated machine learning (ML) models for DR progression from ultrawide field (UWF) retinal images, which were labeled for baseline DR severity and progression based on clinician review of the images and 3-year longitudinal follow-up using the Early Treatment Diabetic Retinopathy Study (ETDRS) severity scale. This dataset has 8 classes: no DR nonprogression (14.62%), Mild nonproliferative DR (NPDR) progression (10.16%) /nonprogression (10.73%), Moderate NPDR progression (10.1%) /nonprogression (15.85%), Severe NPDR progression (11.27%) /nonprogression (10.68%), and proliferative DR (16.55%). A total of 9970 unique images were split into the train, the validation, and the test datasets based on 60-20-20 proportions. The class imbalance was addressed during model building through data augmentation. The ResNet model fine-tuned on this dataset has a classification test accuracy of 81% and an AUC of 0.967 on the test dataset. The objective of the model is to reduce false negatives, which refers to predicting a class that is less progressive than the true label. The predicted labels for 91% of the images were either correct labels or were the labels with greater progression than the original labels. These findings demonstrate the accuracy and feasibility of using machine learning models for identifying DR progression developed using UWF images. Potentially, the use of machine learning algorithms may further refine the risk of disease progression and personalize screening intervals that may reduce costs and improve vision-related outcomes. Disclosure A. Nigam: None. J. Sun: None. V. Subhash: None. P. S. Silva: Research Support; Optos plc., Optomed, Speaker's Bureau; Novartis, Roche Pharmaceuticals, Bayer Inc.