Predicting Visual Fields From Optical Coherence Tomography via an Ensemble of Deep Representation Learners.

Journal: American journal of ophthalmology
Published Date: Jan 5, 2022

Abstract

PURPOSE: To develop and validate a deep learning method of predicting visual function from spectral domain optical coherence tomography (SD-OCT)-derived retinal nerve fiber layer thickness (RNFLT) measurements and corresponding SD-OCT images.

Authors

  • Georgios Lazaridis
    NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom; Centre for Medical Image Computing, University College London, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom. Electronic address: g.lazaridis@ucl.ac.uk.
  • Giovanni Montesano
    From the NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology (G.L., G.M., J.M.-N., D.F.G.-H.), London, United Kingdom; Optometry and Visual Sciences, City, University of London, London, United Kingdom.
  • Saman Sadeghi Afgeh
    Data Science Institute, City University (S.S.A.), London, United Kingdom.
  • Jibran Mohamed-Noriega
    NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom; Departamento de Oftalmología, Hospital Universitario, UANL, Monterrey, México.
  • Sébastien Ourselin
    Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK.
  • Marco Lorenzi
    Epione Research Project, Centre Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, Valbonne, Antibes, France.
  • David F Garway-Heath
    NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom.

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