Usability and Clinician Acceptance of a Deep Learning-Based Clinical Decision Support Tool for Predicting Glaucomatous Visual Field Progression.

Abstract

Authors

• Jimmy S Chen
  Department of Ophthalmology, Oregon Health and Science University, Portland, Oregon.
• Sally L Baxter
  Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla.
• Astrid van den Brandt
  Eindhoven University of Technology, The Netherlands.
• Alexander Lieu
  Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute.
• Andrew S Camp
  Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute.
• Jiun L Do
  Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute.
• Derek S Welsbie
  Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, California. Electronic address: dwelsbie@health.ucsd.edu.
• Sasan Moghimi
  Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran.
• Mark Christopher
  Department of Ophthalmology, Hamilton Glaucoma Center, Shiley Eye Institute, University of California San Diego, La Jolla, California, United States.
• 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.

Keywords

External Resources