Background and Objectives: The present study evaluated the detection of diabetic retinopathy (DR) using an automated fundus camera focusing exclusively on retinal hemorrhage (RH) using a deep convolutional neural network, which is a machine-learning technology. Materials and Methods: This investigation was conducted via a prospective and observational study. The study included 89 fundus ophthalmoscopy images. Seventy images passed an image quality review and were graded as showing no apparent DR (n = 51), mild nonproliferative DR (NPDR; n = 16), moderate NPDR (n = 1), severe NPDR (n = 1), and proliferative DR (n = 1) by three retinal experts according to the International Clinical Diabetic Retinopathy Severity scale. The RH numbers and areas were automatically detected and the results of two tests-the detection of mild-or-worse NPDR and the detection of moderate-or-worse NPDR-were examined. Results: The detection of mild-or-worse DR showed a sensitivity of 0.812 (95% confidence interval: 0.680-0.945), specificity of 0.888, and area under the curve (AUC) of 0.884, whereas the detection of moderate-or-worse DR showed a sensitivity of 1.0, specificity of 1.0, and AUC of 1.0. Conclusions: Automated diagnosis using artificial intelligence focusing exclusively on RH could be used to diagnose DR requiring ophthalmologist intervention.
Keywords: deep convolutional neural network; deep learning; diabetic retinopathy; fundus ophthalmoscopy; retinal hemorrhage.