Diagnostic accuracy of code-free deep learning for detection and evaluation of posterior capsule opacification.

Journal: BMJ open ophthalmology
Published Date: May 23, 2022

Abstract

OBJECTIVE: To train and validate a code-free deep learning system (CFDLS) on classifying high-resolution digital retroillumination images of posterior capsule opacification (PCO) and to discriminate between clinically significant and non-significant PCOs.

Authors

  • Josef Huemer
    Moorfields Eye Hospital, London, United Kingdom.
  • Martin Kronschläger
    VIROS-Vienna Institute for Research in Ocular Surgery, a Karl Landsteiner Institute, Hanusch Hospital, Vienna, Austria.
  • Manuel Ruiss
    VIROS-Vienna Institute for Research in Ocular Surgery, a Karl Landsteiner Institute, Hanusch Hospital, Vienna, Austria.
  • Dawn Sim
    National Institute of Health Research Biomedical Research Center, Moorfields Eye Hospital National Health Service Foundation Trust, and University College London Institute of Ophthalmology, London, UK; Medical Retina Department, Moorfields Eye Hospital National Health Service Foundation Trust, London, UK.
  • Pearse A Keane
    National Institute for Health Research Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK.
  • Oliver Findl
    VIROS-Vienna Institute for Research in Ocular Surgery, a Karl Landsteiner Institute, Hanusch Hospital, Vienna, Austria.
  • Siegfried K Wagner
    National Institute for Health Research Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK.

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