Automatic Diagnosis of Diabetic Retinopathy Stage Focusing Exclusively on Retinal Hemorrhage

• Published in: Medicina (Kaunas, Lithuania) Vol. 58; no. 11; p. 1681
• Main Authors: Tokuda, Yoshihiro, Tabuchi, Hitoshi, Nagasawa, Toshihiko, Tanabe, Mao, Deguchi, Hodaka, Yoshizumi, Yuki, Ohara, Zaigen, Takahashi, Hiroshi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 20-11-2022; MDPI
• Subjects: Accuracy; Artificial intelligence; Blood vessels; Cameras; Classification; Confidence intervals; deep convolutional neural network; deep learning; Development and progression; Diabetes; Diabetic retinopathy; Diagnosis; fundus ophthalmoscopy; Health aspects; Hemorrhage; Machine learning; Methods; Retinal hemorrhage; Support vector machines; Japan
• ISSN: 1648-9144; 1010-660X; 1648-9144
• DOI: 10.3390/medicina58111681

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.