A Deep Learning Algorithm for Classifying Diabetic Retinopathy Using Optical Coherence Tomography Angiography.

Journal: Translational vision science & technology
Published Date: Feb 1, 2022

Abstract

PURPOSE: To develop an automated diabetic retinopathy (DR) staging system using optical coherence tomography angiography (OCTA) images with a convolutional neural network (CNN) and to verify the feasibility of the system.

Authors

  • Gahyung Ryu
    Department of Ophthalmology, Yeungnam University College of Medicine, #170 Hyunchungro, Nam-gu, Daegu, 42415, South Korea.
  • Kyungmin Lee
    Department of Radiology, Ajou University School of Medicine, Wonchon-Dong, Yeongtong-Gu, Suwon, 443-380, South Korea.
  • Donggeun Park
    Department of Ophthalmology, Yeungnam University College of Medicine, #170 Hyunchungro, Nam-gu, Daegu, 42415, South Korea.
  • Inhye Kim
    Department of Ophthalmology, Yeungnam University College of Medicine, Daegu, South Korea.
  • Sang Hyun Park
    Department of Electrical Engineering, ASRI, INMC, Seoul National University, Seoul, Republic of Korea. Electronic address: shpark13135@gmail.com.
  • Min Sagong
    Department of Ophthalmology, Yeungnam University College of Medicine, #170 Hyunchungro, Nam-gu, Daegu, 42415, South Korea. msagong@yu.ac.kr.

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