Validating the Generalizability of Ophthalmic Artificial Intelligence Models on Real-World Clinical Data.

Journal: Translational vision science & technology
Published Date: Nov 1, 2023

Abstract

PURPOSE: This study aims to investigate generalizability of deep learning (DL) models trained on commonly used public fundus images to an instance of real-world data (RWD) for glaucoma diagnosis.

Authors

  • Homa Rashidisabet
    Department of Biomedical Engineering, University of Illinois Chicago, Chicago, IL, USA.
  • Abhishek Sethi
    Artificial Intelligence in Ophthalmology (Ai-O) Center, University of Illinois Chicago, Chicago, IL, USA.
  • Ponpawee Jindarak
    Illinois Eye and Ear Infirmary, Department of Ophthalmology and Visual Sciences, University of Illinois Chicago, Chicago, IL, USA.
  • James Edmonds
    Artificial Intelligence in Ophthalmology (Ai-O) Center, University of Illinois Chicago, Chicago, IL, USA.
  • R V Paul Chan
    Ophthalmology, Illinois Eye and Ear Infirmary, Chicago, IL, United States.
  • Yannek I Leiderman
    Department of Ophthalmology and Visual Sciences - Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois; Richard and Loan Hill Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, Illinois. Electronic address: yannek@uic.edu.
  • Thasarat Sutabutr Vajaranant
    Artificial Intelligence in Ophthalmology (Ai-O) Center, University of Illinois Chicago, Chicago, IL, USA.
  • Darvin Yi
    Stanford University, Department of Radiology, Stanford, CA.

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