Deep-learning prediction of cardiovascular outcomes from routine retinal images in individuals with type 2 diabetes.

Journal: Cardiovascular diabetology
PMID: 39748380

Abstract

BACKGROUND: Prior studies have demonstrated an association between retinal vascular features and cardiovascular disease (CVD), however most studies have only evaluated a few simple parameters at a time. Our aim was to determine whether a deep-learning artificial intelligence (AI) model could be used to predict CVD outcomes from routinely obtained diabetic retinal screening photographs and to compare its performance to a traditional clinical CVD risk score.

Authors

  • Mohammad Ghouse Syed
    VAMPIRE Project, Computer Vision and Image Processing Group, School of Science and Engineering (Computing), University of Dundee, Dundee, DD1 9SY, UK.
  • Emanuele Trucco
  • Muthu R K Mookiah
    VAMPIRE project, Computing, School of Science and Engineering, University of Dundee, Dundee, USA.
  • Chim C Lang
    Division of Molecular and Clinical Medicine, School of Medicine, University of Dundee, Dundee, UK.
  • Rory J McCrimmon
    Division of Systems Medicine, School of Medicine, University of Dundee, Dundee, UK.
  • Colin N A Palmer
    Division of Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, UK.
  • Ewan R Pearson
    Division of Population Health and Genomics, School of Medicine, University of Dundee, Ninewells Hospital, Dundee, United Kingdom.
  • Alex S F Doney
    Division of Cardiovascular Research, School of Medicine, University of Dundee, Dundee, DD1 9SY, UK.
  • Ify R Mordi
    Division of Molecular and Clinical Medicine, School of Medicine, University of Dundee, Dundee, UK.

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