Application of deep learning algorithms for diabetic retinopathy screening

Abstract

Diabetic retinopathy (DR) is one of the most common causes of blindness in adults (1-3). The global prevalence of DR is estimated to be 34.6 % in all diabetics (4). Early stages of DR can be asymptomatic for many years. The detection of DR is important to reduce the progression of DR and preserve vision in the long term (2). In recent years, a number of procedures have evolved that use fundus photographs to make a diagnostic determination of DR (5,6). In this issue of Annals of Translational Medicine, Yang and colleagues present findings of a deeplearning algorithm (artificial intelligence DR screening, AIDRScreening system) to detect DR from a prospective, multicenter clinical evaluation study (7). First, Yang's results are compared with results from existing algorithms. Also, international experiences from deep learning-based screening of DR are addressed. Trends in the field are highlighted and the unique situation in China is discussed in the article.The first deep learning method to be approved by the U.S. Food and Drug Administration (FDA) was the IDx-DR algorithm in 2018 (IDx Technologies Inc., Coralville, Iowa, USA) (8). Also, Google has developed an algorithm for DR detection: automated retinal disease assessment (ARDA) (9).All three algorithms (IDx-DR, ARDA and the novel AIDRScreening algorithm published in this issue of Annals of Translational Medicine) use convolutional-neural-networks as the main method for image classification. In all three