Deep learning-assisted (automatic) diagnosis of glaucoma using a smartphone.

Journal: The British journal of ophthalmology
Published Date: Jul 14, 2021

Abstract

BACKGROUND/AIMS: To validate a deep learning algorithm to diagnose glaucoma from fundus photography obtained with a smartphone.

Authors

  • Kenichi Nakahara
    Queue Inc., Tokyo, Japan.
  • Ryo Asaoka
    Department of Ophthalmology, The University of Tokyo, Tokyo, Japan.
  • Masaki Tanito
    Division of Ophthalmology, Matsue Red Cross Hospital, Shimane, Japan.
  • Naoto Shibata
    Queue inc, Tokyo, Japan.
  • Keita Mitsuhashi
    Queue inc, Tokyo, 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.
  • Tatsuya Inoue
    Department of Ophthalmology, University of Tokyo, Tokyo, Japan.
  • Keiko Azuma
    Department of Ophthalmology, University of Tokyo, Tokyo, Japan.
  • Ryo Obata
    Department of Ophthalmology, University of Tokyo, Tokyo, Japan.
  • Hiroshi Murata
    Department of Ophthalmology, The University of Tokyo, Tokyo, Japan.

Keywords

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