Comparing code-free and bespoke deep learning approaches in ophthalmology.

Journal: Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Published Date: Mar 6, 2024

Abstract

AIM: Code-free deep learning (CFDL) allows clinicians without coding expertise to build high-quality artificial intelligence (AI) models without writing code. In this review, we comprehensively review the advantages that CFDL offers over bespoke expert-designed deep learning (DL). As exemplars, we use the following tasks: (1) diabetic retinopathy screening, (2) retinal multi-disease classification, (3) surgical video classification, (4) oculomics and (5) resource management.

Authors

  • Carolyn Yu Tung Wong
    Institute of Ophthalmology, University College London, 11-43 Bath St, London, EC1V 9EL, UK.
  • Ciara O'Byrne
    Medical Retina Department, Moorfields Eye Hospital NHS Foundation Trust, London, UK.
  • Priyal Taribagil
    Honorary Research Fellow, University College London, London, UK.
  • Timing Liu
    NIHR Biomedical Research Centre Fellow, University College London, London, UK.
  • Fares Antaki
    Department of Ophthalmology, University of Montreal, Montreal, QC H3T 1J4, Canada.
  • Pearse Andrew Keane
    Institute of Ophthalmology, University College London, 11-43 Bath St, London, EC1V 9EL, UK. p.keane@ucl.ac.uk.

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