Explainable Deep Learning System for Automatic Detection of Thyroid Eye Disease using Facial Images.

Journal: American journal of ophthalmology
Published Date:

Abstract

PURPOSE: To report an explainable deep learning (XDL) system to automatically detect thyroid eye disease (TED) using facial images.

Authors

  • Xiao Dan Sui
    Multimedia Laboratory, The Chinese University of Hong Kong, Hong Kong, China; School of Information Science and Engineering, Shandong Normal University, Jinan, Shandong Province, China.
  • Kenneth Ka Hei Lai
    Department of Ophthalmology and Visual Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China; Department of Ophthalmology and Visual Sciences, Princes of Wales Hospital, Hong Kong, China.
  • Richard Wai Chak Choy
    Department of Ophthalmology and Visual Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China.
  • Han Wang
    Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore.
  • Karen Kar Wun Chan
    Department of Ophthalmology and Visual Sciences, Princes of Wales Hospital, Hong Kong, China.
  • Fatema Mohamed Ali Abdulla Aljufairi
    Department of Ophthalmology and Visual Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China; Department of Ophthalmology and Visual Sciences, Princes of Wales Hospital, Hong Kong, China; Department of Ophthalmology, Salmaniya Medical Complex, Government Hospitals, Bahrain.
  • Yuan Jie Zheng
    School of Information Science and Engineering, Shandong Normal University, Jinan, Shandong Province, China.
  • Wilson Wai Kuen Yip
    Department of Ophthalmology and Visual Sciences, Princes of Wales Hospital, Hong Kong, China.
  • Alvin Lerrmann Young
    Department of Ophthalmology and Visual Sciences, Princes of Wales Hospital, Hong Kong, China.
  • Clement Chee Yung Tham
    Department of Ophthalmology and Visual Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China; Department of Ophthalmology and Visual Sciences, Princes of Wales Hospital, Hong Kong, China; Hong Kong Eye Hospital, Hong Kong; Eye Centre, The Chinese University of Hong Kong Medical Centre, Hong Kong.
  • Chi Pui Pang
    Department of Ophthalmology and Visual Sciences, Chinese University of Hong Kong, Hong Kong.
  • Hong Sheng Li
    Multimedia Laboratory, The Chinese University of Hong Kong, Hong Kong, China. Electronic address: hsli@ee.cuhk.edu.hk.
  • Kelvin Kam Lung Chong
    Department of Ophthalmology and Visual Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China; Department of Ophthalmology and Visual Sciences, Princes of Wales Hospital, Hong Kong, China; Hong Kong Eye Hospital, Hong Kong; Eye Centre, The Chinese University of Hong Kong Medical Centre, Hong Kong. Electronic address: chongkamlung@cuhk.edu.hk.

Keywords

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