Geometric Deep Learning to Identify the Critical 3D Structural Features of the Optic Nerve Head for Glaucoma Diagnosis.

Journal: American journal of ophthalmology
Published Date:

Abstract

PURPOSE: To compare the performance of 2 relatively recent geometric deep learning techniques in diagnosing glaucoma from a single optical coherence tomographic (OCT) scan of the optic nerve head (ONH); and to identify the 3-dimensional (3D) structural features of the ONH that are critical for the diagnosis of glaucoma.

Authors

  • Fabian A Braeu
    From the Ophthalmic Engineering & Innovation Laboratory, Singapore Eye Research Institute, Singapore National Eye Centre (F.A.B., M.J.A.G.), Singapore; Singapore-MIT Alliance for Research and Technology (F.A.B., G.B.), Singapore; Yong Loo Lin School of Medicine, National University of Singapore (F.A.B., T.A.), Singapore.
  • Alexandre H Thiéry
    Department of Statistics and Applied Probability, National University of Singapore, Singapore.
  • Tin A Tun
    Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.
  • Aiste Kadziauskiene
    Clinic of Ears, Nose, Throat and Eye Diseases, Institute of Clinical Medicine, Faculty of Medicine, Vilnius University (A.K.), Vilnius, Lithuania; Center of Eye diseases, Vilnius University Hospital Santaros Klinikos (A.K.), Vilnius, Lithuania.
  • George Barbastathis
    Singapore-MIT Alliance for Research and Technology (F.A.B., G.B.), Singapore; Department of Mechanical Engineering, Massachusetts Institute of Technology (G.B.), Cambridge, Massachusetts, USA.
  • Tin Aung
    Singapore Eye Research Institute, Singapore National Eye Center, Singapore.
  • Michaël J A Girard
    Ophthalmic Engineering and Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore.