Autonomous Screening for Diabetic Macular Edema Using Deep Learning Processing of Retinal Images

Abstract

AbstractBackgroundDiabetic Macular Edema (DME) is a complication of diabetes which, when untreated, leads to vision loss. Screening for signs of diabetic eye disease, including DME, is recommended for all patients with diabetes at least every one to two years, however, compliance with this standard is low.MethodsA deep learning model was trained for DME detection using the EyePACS dataset. Data was randomly assigned, by participant, into development (n= 14,246) and validation (n= 1,583) sets. Analysis was conducted on the single image, eye, and patient levels. Model performance was evaluated using sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). Independent validation was further performed on the IDRiD dataset, as well as new data.FindingsAt the image level, sensitivity of 0.889 (CI 95% 0.878, 0.900), specificity of 0.889 (CI 95% 0.877, 0.900), and AUC of 0.954 (CI 95% 0.949, 0.959) were achieved. At the eye level, sensitivity of 0.905 (CI 95% 0.890, 0.920), specificity of 0.902 (CI 95% 0.890, 0.913), and AUC of 0.964 (CI 95% 0.958, 0.969) were achieved. At the patient level, sensitivity of 0.901 (CI 95% 0.879, 0.917), specificity of 0.900 (CI 95% 0.883, 0.911), and AUC of 0.962 (CI 95% 0.955, 0.968) were achieved.InterpretationDME can be detected from color fundus imaging with high performance on all analysis metrics. Automatic DME detection may simplify screening, leading to more encompassing screening for diabetic patients. Further prospective studies are necessary.FundingFunding was provided by AEYE Health Inc.