In Medicina (Kaunas, Lithuania)
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.
Tokuda Yoshihiro, Tabuchi Hitoshi, Nagasawa Toshihiko, Tanabe Mao, Deguchi Hodaka, Yoshizumi Yuki, Ohara Zaigen, Takahashi Hiroshi
2022-Nov-20
deep convolutional neural network, deep learning, diabetic retinopathy, fundus ophthalmoscopy, retinal hemorrhage
