Can deep learning on retinal images augment known risk factors for cardiovascular disease prediction in diabetes? A prospective cohort study from the national screening programme in Scotland.

Journal: International journal of medical informatics
PMID: 37167840

Abstract

AIMS: This study's objective was to evaluate whether deep learning (DL) on retinal photographs from a diabetic retinopathy screening programme improve prediction of incident cardiovascular disease (CVD).

Authors

  • Joseph Mellor
    The Usher Institute, University of Edinburgh, Edinburgh, UK. Electronic address: joe.mellor@ed.ac.uk.
  • Wenhua Jiang
    The Usher Institute, University of Edinburgh, Edinburgh, UK.
  • Alan Fleming
    Optos Plc, Queensferry House, Carnegie Business Campus, Enterprise Way, Dunfermline, Scotland, KY11 8GR, UK.
  • Stuart J McGurnaghan
    The Usher Institute, University of Edinburgh, Edinburgh, UK; The Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
  • Luke Blackbourn
    The Institute of Genetics and Cancer, University of Edinburgh, 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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