Using Deep Learning and Transfer Learning to Accurately Diagnose Early-Onset Glaucoma From Macular Optical Coherence Tomography Images.

Journal: American journal of ophthalmology
Published Date:

Abstract

PURPOSE: We sought to construct and evaluate a deep learning (DL) model to diagnose early glaucoma from spectral-domain optical coherence tomography (OCT) images.

Authors

  • Ryo Asaoka
    Department of Ophthalmology, The University of Tokyo, Tokyo, Japan.
  • Hiroshi Murata
    Department of Ophthalmology, The University of Tokyo, Tokyo, Japan.
  • Kazunori Hirasawa
    Moorfields Eye Hospital National Health Service Foundation Trust and University College London, Institute of Ophthalmology, London, United Kingdom; Department of Ophthalmology, School of Medicine, Kitasato University, Kanagawa, Japan.
  • Yuri Fujino
    Department of Ophthalmology, The University of Tokyo, Tokyo, Japan.
  • Masato Matsuura
    Department of Ophthalmology, The University of Tokyo, Tokyo, Japan; Moorfields Eye Hospital National Health Service Foundation Trust and University College London, Institute of Ophthalmology, London, United Kingdom.
  • Atsuya Miki
    Department of Ophthalmology, Osaka University Graduate School of Medicine, Osaka, Japan.
  • Takashi Kanamoto
    Department of Ophthalmology, Hiroshima Memorial Hospital, Hiroshima, Japan.
  • Yoko Ikeda
    Department of Ophthalmology, Kyoto Prefectural University of Medicine, Kyoto, Japan; Oike Ikeda Eye Clinic, Kyoto, Japan.
  • Kazuhiko Mori
    Medicinal Safety Research Laboratories, Daiichi Sankyo Co., Ltd., 1-16-13 Kitakasai, Edogawa-ku, Tokyo, 134-8630, Japan.
  • Aiko Iwase
    Tajimi Iwase Eye Clinic, Tajimi, Japan.
  • Nobuyuki Shoji
    Department of Ophthalmology, School of Medicine, Kitasato University, Kanagawa, Japan.
  • Kenji Inoue
    Inouye Eye Hospital, Tokyo, Japan.
  • Junkichi Yamagami
    JR Tokyo General Hospital, Tokyo, Japan.
  • Makoto Araie
    Kanto Central Hospital of the Mutual Aid Association of Public School Teachers, Tokyo, Japan.