Detection of Longitudinal Visual Field Progression in Glaucoma Using Machine Learning.

Journal: American journal of ophthalmology
PMID: 29920226

Abstract

PURPOSE: Global indices of standard automated perimerty are insensitive to localized losses, while point-wise indices are sensitive but highly variable. Region-wise indices sit in between. This study introduces a machine learning-based index for glaucoma progression detection that outperforms global, region-wise, and point-wise indices.

Authors

  • Siamak Yousefi
    Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America.
  • Taichi Kiwaki
    Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan.
  • Yuhui Zheng
    Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan.
  • Hiroki Sugiura
    Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan.
  • Ryo Asaoka
    Department of Ophthalmology, The University of Tokyo, Tokyo, Japan.
  • Hiroshi Murata
    Department of Ophthalmology, The University of Tokyo, Tokyo, Japan.
  • Hans Lemij
    Rotterdam Eye Hospital, Rotterdam, Netherlands.
  • Kenji Yamanishi
    Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan.

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