Detecting dry eye from ocular surface videos based on deep learning.

Journal: The ocular surface
Published Date: Jan 26, 2023

Abstract

OBJECTIVE: To assess the performance of convolutional neural networks (CNNs) for automated diagnosis of dry eye (DE) in patients undergoing video keratoscopy based on single ocular surface video frames.

Authors

  • Hazem Abdelmotaal
    Department of Ophthalmology, Faculty of Medicine, Assiut University, Assiut, Egypt.
  • Rossen Hazarbasanov
    Hospital de Olhos-CRO, Guarulhos, SP, Brazil; Department of Ophthalmology and Visual Sciences, Paulista Medical School, Federal University of São Paulo, São Paulo, Brazil. Electronic address: hazarbassanov@gmail.com.
  • Suphi Taneri
    Ruhr University, Bochum, Germany; Zentrum für Refraktive Chirurgie, Muenster, Germany.
  • Ali Al-Timemy
    Biomedical Engineering Department, Al-Khwarizmi College of Engineering, University of Baghdad, Iraq; Centre for Robotics and Neural Systems (CRNS), Cognitive Institute, School of Engineering, Computing and Mathematics, Plymouth University, UK.
  • Alexandru Lavric
    Computers, Electronics and Automation Department, Stefan cel Mare University of Suceava, Suceava 720229, Romania.
  • Hidenori Takahashi
  • Siamak Yousefi
    Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America.

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