Deep learning model to identify homonymous defects on automated perimetry.

Journal: The British journal of ophthalmology
Published Date: Aug 3, 2022

Abstract

BACKGROUND: Homonymous visual field (VF) defects are usually an indicator of serious intracranial pathology but may be subtle and difficult to detect. Artificial intelligence (AI) models could play a key role in simplifying the detection of these defects. This study aimed to develop an automated deep learning AI model to accurately identify homonymous VF defects from automated perimetry.

Authors

  • Aaron Hao Tan
    Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada.
  • Laura Donaldson
    Ophthalmology and Vision Science, University of Toronto, Toronto, Ontario, Canada.
  • Luqmaan Moolla
    College of Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.
  • Austin Pereira
    Department of Ophthalmology & Visual Sciences, University of Toronto, Toronto, ON Canada.
  • Edward Margolin
    Ophthalmology, University of Toronto, Toronto, Ontario, Canada edmargolin@gmail.com.

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