Opportunities for Improving Glaucoma Clinical Trials via Deep Learning-Based Identification of Patients with Low Visual Field Variability.

Journal: Ophthalmology. Glaucoma
Published Date: Jan 1, 2024

Abstract

PURPOSE: Develop and evaluate the performance of a deep learning model (DLM) that forecasts eyes with low future visual field (VF) variability, and study the impact of using this DLM on sample size requirements for neuroprotective trials.

Authors

  • Ruolin Wang
    Malone Center for Engineering in Healthcare, Johns Hopkins University School of Medicine, Baltimore, Maryland; Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
  • Chris Bradley
    Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Patrick Herbert
    Malone Center for Engineering, Johns Hopkins University, Baltimore, MD, USA.
  • Kaihua Hou
    Malone Center for Engineering, Johns Hopkins University, Baltimore, MD, USA.
  • Gregory D Hager
    Department of Computer Science, The Johns Hopkins University, 3400 N. Charles St., Malone Hall Room 340, Baltimore, MD, 21218, USA.
  • Katharina Breininger
  • Mathias Unberath
    Johns Hopkins University, Baltimore, MD, USA.
  • Pradeep Ramulu
    Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland.
  • Jithin Yohannan
    Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland.

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