Feasibility of simple machine learning approaches to support detection of non-glaucomatous visual fields in future automated glaucoma clinics.

Journal: Eye (London, England)
Published Date: Mar 4, 2019

Abstract

OBJECTIVES: To assess the performance of feed-forward back-propagation artificial neural networks (ANNs) in detecting field defects caused by pituitary disease from among a glaucomatous population.

Authors

  • Peter B M Thomas
    NIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, EC1V 9EL, UK. pbmthomas@gmail.com.
  • Thomas Chan
    Discipline of Ophthalmology, University of Sydney, Sydney, Australia.
  • Thomas Nixon
    Department of Ophthalmology, Faculty of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK.
  • Brinda Muthusamy
    Department of Ophthalmology, Addenbrooke's Hospital, Cambridge, UK.
  • Andrew White
    Discipline of Ophthalmology, University of Sydney, Sydney, Australia.

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