Assessment of a Segmentation-Free Deep Learning Algorithm for Diagnosing Glaucoma From Optical Coherence Tomography Scans.

Journal: JAMA ophthalmology
Published Date: Apr 1, 2020

Abstract

IMPORTANCE: Conventional segmentation of the retinal nerve fiber layer (RNFL) is prone to errors that may affect the accuracy of spectral-domain optical coherence tomography (SD-OCT) scans in detecting glaucomatous damage.

Authors

  • Atalie C Thompson
    Vision, Imaging and Performance (VIP) Laboratory, Duke Eye Center and Department of Ophthalmology, Duke University, Durham, North Carolina.
  • Alessandro A Jammal
    Vision, Imaging and Performance (VIP) Laboratory, Duke Eye Center and Department of Ophthalmology, Duke University, Durham, North Carolina.
  • Samuel I Berchuck
    Vision, Imaging and Performance Laboratory (VIP), Duke Eye Center and Department of Ophthalmology, Duke University, Durham, North Carolina, USA; Department of Statistical Science and Forge, Duke University, Durham, North Carolina, USA.
  • Eduardo B Mariottoni
    Vision, Imaging and Performance Laboratory (VIP), Duke Eye Center and Department of Ophthalmology, Duke University, Durham, North Carolina, USA.
  • Felipe A Medeiros
    Duke Eye Center, Department of Ophthalmology, Duke University, Durham, North Carolina, United States.

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