Feasibility of support vector machine learning in age-related macular degeneration using small sample yielding sparse optical coherence tomography data.

Journal: Acta ophthalmologica
Published Date: Aug 1, 2019

Abstract

PURPOSE: A retrospective pilot study is conducted to demonstrate the utility of a novel support vector machine learning (SVML) algorithm in a small three-dimensional (3D) sample yielding sparse optical coherence tomography (spOCT) data for the automatic monitoring of neovascular (wet) age-related macular degeneration (wAMD).

Authors

  • Gwenolé Quellec
    Inserm, UMR 1101, 22 avenue Camille-Desmoulins, Brest F-29200, France. Electronic address: gwenole.quellec@inserm.fr.
  • Jens Kowal
    ARTORG Centre for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
  • Pascal W Hasler
    OCTlab, Department of Ophthalmology, University of Basel, Basel, Switzerland.
  • Hendrik P N Scholl
    Department of Ophthalmology, University of Basel, Basel, Switzerland.
  • Sandrine Zweifel
    Department of Ophthalmology, University Hospital Zurich, Zurich, Switzerland.
  • Balaskas Konstantinos
    Moorfields Ophthalmic Reading Centre, London, UK.
  • João Emanuel Ramos de Carvalho
    Moorfields Eye Hospital NHS Trust, Institute of Ophthalmology UCL, London, UK.
  • Tjebo Heeren
    Moorfields Ophthalmic Reading Centre, 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.
  • Peter M Maloca
    OCTlab, Department of Ophthalmology, University of Basel, Basel, Switzerland.

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