Deep learning detection of diabetic retinopathy in Scotland's diabetic eye screening programme.

Journal: The British journal of ophthalmology
PMID: 37704266

Abstract

BACKGROUND/AIMS: Support vector machine-based automated grading (known as iGradingM) has been shown to be safe, cost-effective and robust in the diabetic retinopathy (DR) screening (DES) programme in Scotland. It triages screening episodes as gradable with no DR versus manual grading required. The study aim was to develop a deep learning-based autograder using images and gradings from DES and to compare its performance with that of iGradingM.

Authors

  • Alan D Fleming
    The Institute of Genetics and Cancer, University of Edinburgh Western General Hospital, Edinburgh, UK.
  • Joseph Mellor
    The Usher Institute, University of Edinburgh, Edinburgh, UK. Electronic address: joe.mellor@ed.ac.uk.
  • Stuart J McGurnaghan
    The Usher Institute, University of Edinburgh, Edinburgh, UK; The Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
  • Luke A K Blackbourn
    Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK.
  • Keith A Goatman
    Canon Medical Research Europe Ltd, Edinburgh, UK.
  • Caroline Styles
    Queen Margaret Hospital, Dunfermline, Fife, UK.
  • Amos J Storkey
    Institute for Adaptive and Neural Computation, University of Edinburgh, UK.
  • Paul M McKeigue
    The Usher Institute, University of Edinburgh, Edinburgh, UK.
  • Helen M Colhoun
    The Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK; Department of Public Health, NHS Fife, Kirkcaldy, UK.

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