IDF21-0299 An automatic deep learning-based system for screening and management of DME

Abstract

Background: Diabetic macular oedema (DME) is the most common complication form of sight-threatening retinopathy in people with a diabetic condition. Previous studies demonstrate specifically that poor glycaemic and blood pressure control are associated with the presence and development of DME condition. The aim of this study is to validate the implementation of deep learning systems as a tool into routinely practiced diabetic retinopathy screening to improve diagnosis and management of DME. Method: 62,383 color images of the eye fundus were used. These images were acquired between 2016 and 2020 with a non mydriatic Centervue DRS camera. Images were graded as DME and no DME by retinal specialists according to the classification of American Association of Ophthalmology. The dataset was randomly divided into train-validation and test sets in proportions of 89.5% and 10.5%, respectively. We trained 4 CNNs based on the ResNeXt architecture with standardised convolutions as in Kolesnikov et al (ECCV 2020). Model parameters were initialized from a solution to the ImageNet image classification task and further optimized by minimizing the cross-entropy loss in its Sharpness-Aware extension, which attempts to find a parameter configuration lying in neighborhoods with uniformly low loss values, leading to improved generalization ability. The training was carried out by error back-propagation with weight updates given by the Stochastic Gradient Descent algorithm, and a learning rate of 0.01 that was cyclically annealed towards 0. The optimization ran for 20 cycles but was stopped as soon as the AUC computed on the validation set did not improve for two cycles. Final predictions were computed from the average ensemble of the four models. Results: The resulting model obtained a high performance on the test dataset. The area under the curve (AUC) was 0.9591(0.9536-0.9645). In addition, at an optimal cut-off given by the Youden index, the accuracy, the sensitivity and the specificity achieved were 0.9033(0.8959-0.9104), 0.8942 and 0.9054 respectively. An F1 score of 0.7759 and a Kappa score of 0.7156 indicate a high agreement of the model’s predictions with ophthalmologists’ opinions. Conclusion: The proposed system achieved high sensitivity and specificity in DME screening from retinal images of the eye fundus. The performance of the proposed approach is comparable to that of human experts and could be a valuable tool to help primary care triage.