Accuracy of Diabetic Retinopathy Staging with a Deep Convolutional Neural Network Using Ultra-Wide-Field Fundus Ophthalmoscopy and Optical Coherence Tomography Angiography

• Published in: Journal of ophthalmology Vol. 2021; pp. 1 - 10
• Main Authors: Nagasawa, Toshihiko, Tabuchi, Hitoshi, Masumoto, Hiroki, Morita, Shoji, Niki, Masanori, Ohara, Zaigen, Yoshizumi, Yuki, Mitamura, Yoshinori
• Format: Journal Article
• Language: English
• Published: United States Hindawi 2021; John Wiley & Sons, Inc; Wiley
• Subjects: Accuracy; Angiography; Artificial intelligence; Automation; Clinical medicine; Datasets; Developing countries; Diabetes; Diabetic retinopathy; FDA approval; LDCs; Medical imaging; Medical personnel; Neural networks; Retina; Tomography; Japan

Purpose. The present study aimed to compare the accuracy of diabetic retinopathy (DR) staging with a deep convolutional neural network (DCNN) using two different types of fundus cameras and composite images. Method. The study included 491 ultra-wide-field fundus ophthalmoscopy and optical coherence...