Artificial intelligence to automate assessment of ocular and periocular measurements.

Journal: European journal of ophthalmology
PMID: 38710195

Abstract

PURPOSE: To develop and validate a deep learning facial landmark detection network to automate the assessment of periocular anthropometric measurements.

Authors

  • Khizar Rana
    Department of Ophthalmology & Visual Sciences, South Australian Institute of Ophthalmology, University of Adelaide, North Terrace, SA 5000, Australia.
  • Mark Beecher
    Department of Ophthalmology & Visual Sciences, South Australian Institute of Ophthalmology, University of Adelaide, North Terrace, SA 5000, Australia.
  • Carmelo Caltabiano
    Department of Ophthalmology & Visual Sciences, South Australian Institute of Ophthalmology, University of Adelaide, North Terrace, SA 5000, Australia.
  • Carmelo Macri
    Machine Learning Division, Ophthalmic Research Laboratory, University of Adelaide, Adelaide, South Australia, Australia.
  • Yang Zhao
    The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
  • Johan Verjans
  • Dinesh Selva
    South Australian Institute of Ophthalmology, Royal Adelaide Hospital, Adelaide, South Australia, Australia.

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