A deep learning approach to identify blepharoptosis by convolutional neural networks.

Journal: International journal of medical informatics
Published Date: Jan 28, 2021

Abstract

PURPOSE: Blepharoptosis is a known cause of reversible vision loss. Accurate assessment can be difficult, especially amongst non-specialists. Existing automated techniques disrupt clinical workflow by requiring user input, or placement of reference markers. Neural networks are known to be effective in image classification tasks. We aim to develop an algorithm that can accurately identify blepharoptosis from a clinical photo.

Authors

  • Ju-Yi Hung
    Ophthalmology, Byers Eye Institute, Stanford University School of Medicine, Palo Alto, California, United States; Ophthalmology, Taipei Medical University Hospital, Taipei, Taiwan; Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
  • Chandrashan Perera
    Department of Ophthalmology, Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Ke-Wei Chen
    PranaQ Pte. Ltd., Singapore.
  • David Myung
    Department of Ophthalmology, Byers Eye Institute at Stanford, Palo Alto, CA, USA.
  • Hsu-Kuang Chiu
    Computer Science, Stanford University, Stanford, California, United States.
  • Chiou-Shann Fuh
    Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
  • Cherng-Ru Hsu
    Ophthalmology, National Taiwan University Hospital, Taipei, Taiwan; Ophthalmology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
  • Shu-Lang Liao
    Ophthalmology, National Taiwan University Hospital, Taipei, Taiwan; College of Medicine, National Taiwan University, Taipei, Taiwan. Electronic address: liaosl89@ntu.edu.tw.
  • Andrea Lora Kossler
    Ophthalmology, Byers Eye Institute, Stanford University School of Medicine, Palo Alto, California, United States. Electronic address: akossler@stanford.edu.

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