Artificial intelligence using deep learning to predict the anatomical outcome of rhegmatogenous retinal detachment surgery: a pilot study.

Journal: Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PMID: 36303063

Abstract

PURPOSE: To develop and evaluate an automated deep learning model to predict the anatomical outcome of rhegmatogenous retinal detachment (RRD) surgery.

Authors

  • Timothy H M Fung
    Guy's and St Thomas' NHS Foundation Trust, London, UK. timothyfung@doctors.org.uk.
  • Neville C R A John
    Centre for Vision, Speech and Signal Processing, Department of Electrical and Electronic Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, UK.
  • Jean-Yves Guillemaut
    Centre for Vision, Speech and Signal Processing, Department of Electrical and Electronic Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, UK.
  • David Yorston
    Gartnavel Hospital, Glasgow, UK.
  • David Frohlich
    Centre for Vision, Speech and Signal Processing, Department of Electrical and Electronic Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, UK.
  • David H W Steel
    Sunderland Eye Hospital, Sunderland, UK.
  • Tom H Williamson
    Guy's and St Thomas' NHS Foundation Trust, London, UK.

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