Multimodal Deep Learning Classifier for Primary Open Angle Glaucoma Diagnosis Using Wide-Field Optic Nerve Head Cube Scans in Eyes With and Without High Myopia.

Journal: Journal of glaucoma
PMID: 37523623

Abstract

PRCIS: An optical coherence tomography (OCT)-based multimodal deep learning (DL) classification model, including texture information, is introduced that outperforms single-modal models and multimodal models without texture information for glaucoma diagnosis in eyes with and without high myopia.

Authors

  • Christopher Bowd
    Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla, California.
  • Akram Belghith
    Department of Ophthalmology, Hamilton Glaucoma Center, Shiley Eye Institute, University of California San Diego, La Jolla, California, United States.
  • Jasmin Rezapour
    Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, CA, USA.
  • Mark Christopher
    Department of Ophthalmology, Hamilton Glaucoma Center, Shiley Eye Institute, University of California San Diego, La Jolla, California, United States.
  • Jost B Jonas
    Department of Ophthalmology, Ruprecht-Karls University of Heidelberg, Heidelberg, Germany.
  • Leslie Hyman
    Vickie and Jack Farber Vision Research Center, Wills Eye Hospital, Thomas Jefferson University, Philadelphia, PA.
  • Massimo A Fazio
    Department of Ophthalmology, University of Alabama at Birmingham, Birmingham, Alabama.
  • Robert N Weinreb
    Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla, California.
  • Linda M Zangwill
    Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla, California.

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