A multi-label transformer-based deep learning approach to predict focal visual field progression.

Journal: Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Published Date: Jul 1, 2024

Abstract

PURPOSE: Tracking functional changes in visual fields (VFs) through standard automated perimetry remains a clinical standard for glaucoma diagnosis. This study aims to develop and evaluate a deep learning (DL) model to predict regional VF progression, which has not been explored in prior studies.

Authors

  • Ling Chen
    Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, United States.
  • Vincent S Tseng
    Computer Science and Information Engineering, National Chiao Tung University, Hsinchu, Taiwan.
  • Ta-Hsin Tsung
    Department of Ophthalmology, Tri-Service General Hospital, National Defense Medical Center, No.325, Sec.2, Chenggong Rd., Neihu District, Taipei, Taiwan.
  • Da-Wen Lu
    Department of Ophthalmology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.

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