Diagnostic accuracy of diabetic retinopathy grading by an artificial intelligence-enabled algorithm compared with a human standard for wide-field true-colour confocal scanning and standard digital retinal images.

Journal: The British journal of ophthalmology
Published Date: May 6, 2020

Abstract

BACKGROUND: Photographic diabetic retinopathy screening requires labour-intensive grading of retinal images by humans. Automated retinal image analysis software (ARIAS) could provide an alternative to human grading. We compare the performance of an ARIAS using true-colour, wide-field confocal scanning images and standard fundus images in the English National Diabetic Eye Screening Programme (NDESP) against human grading.

Authors

  • Abraham Olvera-Barrios
    Medical Retina, Moorfields Eye Hospital NHS Foundation Trust, London, UK a.olvera@nhs.net.
  • Tjebo Fc Heeren
    Medical Retina, Moorfields Eye Hospital NHS Foundation Trust, London, UK.
  • Konstantinos Balaskas
    School of Pharmacy and Optometry, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom; Manchester Royal Eye Hospital, NHS Central Manchester University Hospitals, Manchester, United Kingdom.
  • Ryan Chambers
    Diabetes, Homerton University Hospital NHS Foundation Trust, London, UK.
  • Louis Bolter
    Diabetes, Homerton University Hospital NHS Foundation Trust, London, UK.
  • Catherine Egan
    NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK.
  • Adnan Tufail
    London, United Kingdom. Electronic address: Adnan.Tufail@moorfields.nhs.uk.
  • John Anderson
    Kimel Family Translational Imaging Genetics Research Laboratory, The Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada.

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