Prediction of causative genes in inherited retinal disorder from fundus photography and autofluorescence imaging using deep learning techniques.

Journal: The British journal of ophthalmology
PMID:

Abstract

BACKGROUND/AIMS: To investigate the utility of a data-driven deep learning approach in patients with inherited retinal disorder (IRD) and to predict the causative genes based on fundus photography and fundus autofluorescence (FAF) imaging.

Authors

  • Yu Fujinami-Yokokawa
    Laboratory of Visual Physiology, Division of Vision Research, National Institute of Sensory Organs, National Hospital Organization Tokyo Medical Center, Tokyo, Japan.
  • Hideki Ninomiya
    Department of Health Policy and Management, School of Medicine, Keio University, Tokyo, Japan.
  • Xiao Liu
  • Lizhu Yang
    Laboratory of Visual Physiology, Division of Vision Research, National Institute of Sensory Organs, National Hospital Organization Tokyo Medical Center, Tokyo, Japan.
  • Nikolas Pontikos
    University College London Institute of Ophthalmology, London, UK n.pontikos@ucl.ac.uk.
  • Kazutoshi Yoshitake
    Division of Molecular and Cellular Biology, National Institute of Sensory Organs, National Hospital Organization Tokyo Medical Center, Tokyo, Japan.
  • Takeshi Iwata
    Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka, 565-0871, Japan.
  • Yasunori Sato
    Graduate School of Health Management, Keio University, Tokyo, Japan.
  • Takeshi Hashimoto
    Department of Urology, Tokyo Medical University, Tokyo, Japan.
  • Kazushige Tsunoda
    Division of Vision Research, National Institute of Sensory Organs, National Hospital Organization Tokyo Medical Center, Tokyo, Japan.
  • Hiroaki Miyata
    Department of Health Policy Management, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan.
  • Kaoru Fujinami
    Laboratory of Visual Physiology, Division of Vision Research, National Institute of Sensory Organs, National Hospital Organization Tokyo Medical Center, Tokyo, Japan k.fujinami@ucl.ac.uk.