Deep Learning Differentiates Papilledema, NAION, and Healthy Eyes with Unsegmented 3D OCT Volumes.

Journal: American journal of ophthalmology
Published Date: May 28, 2025

Abstract

OBJECTIVE: Deep learning (DL) has been used to differentiate papilledema from healthy eyes and optic disc elevation on fundus photos. As we described optic nerve head (ONH) and peripapillary retina (PPR) optical coherence tomography (OCT) features that distinguish non-arteritic anterior ischemic optic neuropathy (NAION) from papilledema, we hypothesized that a DL approach using the full 3D OCT volume could reliably differentiate NAION, papilledema and healthy eyes.

Authors

  • David Szanto
    Neurology, Icahn School of Medicine at Mount Sinai, New York, USA.
  • Jui-Kai Wang
    Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA.
  • Brian Woods
    Irish Clinical Academic Training Programme, Department of Ophthalmology, Cork University Hospital, Cork, Ireland.
  • Mona K Garvin
    Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, 52242, United States; Iowa City VA Health Care System, Iowa City, IA, 52246, United States. Electronic address: mona-garvin@uiowa.edu.
  • Brett A Johnson
    Department of Ophthalmology and Visual Sciences, The University of Iowa Hospitals & Clinics, Iowa City, IA, USA.
  • Randy H Kardon
    Iowa City VA Health Care System and Iowa City VA Center for the Prevention and Treatment of Visual Loss, Iowa City, IA, USA.
  • Edward F Linton
    Center for the Prevention and Treatment of Visual Loss, Iowa City VA Health Care System, Iowa City, IA, USA.
  • Mark J Kupersmith
    Departments of Neurology, Neurosurgery and Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

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