Deep learning-based image analysis for automated measurement of eyelid morphology before and after blepharoptosis surgery.

Journal: Annals of medicine
Published Date: Dec 1, 2021

Abstract

BACKGROUND AND AIM: Eyelid position and contour abnormality could lead to various diseases, such as blepharoptosis, which is a common eyelid disease. Accurate assessment of eyelid morphology is important in the management of blepharoptosis. We aimed to proposed a novel deep learning-based image analysis to automatically measure eyelid morphological properties before and after blepharoptosis surgery.

Authors

  • Lixia Lou
    Department of Ophthalmology, College of Medicine, The Second Affiliated Hospital of Zhejiang University, Hangzhou, China.
  • Jing Cao
    Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, Zhejiang, People's Republic of China.
  • Yaqi Wang
    Key Laboratory of RF Circuits and Systems, Ministry of Education, Hangzhou Dianzi University, Hangzhou 310018, China.
  • Zhiyuan Gao
    Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University, College of Medicine, Hangzhou, Zhejiang, China.
  • Kai Jin
    Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
  • Zhaoyang Xu
    Google Health, Google LLC, Palo Alto, California, United States of America.
  • Qianni Zhang
    Queen Mary University of London, London, UK.
  • Xingru Huang
    School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK.
  • Juan Ye
    Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.

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