Diabetic retinopathy screening using deep neural network.

Abstract: Importance: There is a burgeoning interest in the use of deep neural network in diabetic retinal screening. Background: To determine whether a deep neural network could satisfactorily detect diabetic retinopathy that requires referral to an ophthalmologist from a local diabetic retinal screening programme and an international database. Design: Retrospective audit. Participants: Diabetic retinal photos from Otago database photographed during October 2016 (485 photos), and 1200 photos from Messidor international database. Methods: Receiver operating characteristic curve to illustrate the ability of a deep neural network to identify referable diabetic retinopathy (moderate or worse diabetic retinopathy or exudates within one disc diameter of the fovea). Main Outcome Measures: Area under the receiver operating characteristic curve, sensitivity and specificity. Results: For detecting referable diabetic retinopathy, the deep neural network had an area under receiver operating characteristic curve of 0.901 (95% confidence interval 0.807–0.995), with 84.6% sensitivity and 79.7% specificity for Otago and 0.980 (95% confidence interval 0.973–0.986), with 96.0% sensitivity and 90.0% specificity for Messidor. Conclusions and Relevance: This study has shown that a deep neural network can detect referable diabetic retinopathy with sensitivities and specificities close to or better than 80% from both an international and a domestic (New Zealand) database. We believe that deep neural networks can be integrated into community screening once they can successfully detect both diabetic retinopathy and diabetic macular oedema. [ABSTRACT FROM AUTHOR]