Glaucoma detection and staging from visual field images using machine learning techniques.

Journal: PloS one
PMID: 39823435

Abstract

PURPOSE: In this study, we investigated the performance of deep learning (DL) models to differentiate between normal and glaucomatous visual fields (VFs) and classify glaucoma from early to the advanced stage to observe if the DL model can stage glaucoma as Mills criteria using only the pattern deviation (PD) plots. The DL model results were compared with a machine learning (ML) classifier trained on conventional VF parameters.

Authors

  • Nahida Akter
    School of Optometry and Vision Science, UNSW Sydney, Sydney, NSW, 2052, Australia.
  • Jack Gordon
    School of Optometry and Vision Science, UNSW Sydney, Sydney, New South Wales, Australia.
  • Sherry Li
    School of Optometry and Vision Science, UNSW Sydney, Sydney, New South Wales, Australia.
  • Mikki Poon
    School of Optometry and Vision Science, UNSW Sydney, Sydney, New South Wales, Australia.
  • Stuart Perry
    School of Electrical and Data Engineering, University of Technology Sydney, Sydney, NSW, 2007, Australia.
  • John Fletcher
    School of Electrical Engineering and Telecommunications, UNSW Sydney, Sydney, NSW, 2052, Australia.
  • Thomas Chan
    Discipline of Ophthalmology, University of Sydney, Sydney, Australia.
  • Andrew White
    Discipline of Ophthalmology, University of Sydney, Sydney, Australia.
  • Maitreyee Roy
    School of Optometry and Vision Science, UNSW Sydney, Sydney, NSW, 2052, Australia. maitreyee.roy@unsw.edu.au.

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