Predicting the future development of diabetic retinopathy using a deep learning algorithm for the analysis of non-invasive retinal imaging

Abstract

AimsDiabetic retinopathy (DR) is the most common cause of vision loss in the working age. This research aimed to develop an artificial intelligence (AI) machine learning model which can predict the development of referable DR from fundus imagery of otherwise healthy eyes.MethodsOur researchers trained a machine learning algorithm on the EyePACS data set, consisting of 156 363 fundus images. Referrable DR was defined as any level above mild on the International Clinical Diabetic Retinopathy scale.ResultsThe algorithm achieved 0.81 area under receiver operating curve (AUC) when averaging scores from multiple images on the task of predicting development of referrable DR, and 0.76 AUC when using a single image.ConclusionOur results suggest that risk of DR may be predicted from fundus photography alone. Prediction of personalised risk of DR may become key in treatment and contribute to patient compliance across the board, particularly when supported by further prospective research.