Deep Learning Models Used in the Diagnostic Workup of Keratoconus: A Systematic Review and Exploratory Meta-Analysis.

Journal: Cornea
Published Date: Feb 1, 2024

Abstract

PURPOSE: The prevalence of keratoconus in the general population is reported to be up to 1 of 84. Over the past 2 decades, diagnosis and management evolved rapidly, but keratoconus screening in clinical practice is still challenging and asks for improving the accuracy of keratoconus detection. Deep learning (DL) offers considerable promise for improving the accuracy and speed of medical imaging interpretation. We establish an inventory of studies conducted with DL algorithms that have attempted to diagnose keratoconus.

Authors

  • Nicolas S Bodmer
    Medical Faculty, University of Zurich, Zurich, Switzerland.
  • Dylan G Christensen
    Medignition Inc. Research Consultants Zurich, Zurich, Switzerland.
  • Lucas M Bachmann
    Medignition, Research Consultants, Zurich, Switzerland.
  • Livia Faes
    Moorfields Eye Hospital NHS Foundation Trust, London, UK; Eye Clinic, Cantonal Hospital of Lucerne, Lucerne, Switzerland.
  • Frantisek Sanak
    Department of Ophthalmology, Cantonal Hospital of Lucerne, Lucerne, Switzerland ; and.
  • Katja Iselin
    Medical Faculty, University of Zurich, Zurich, Switzerland.
  • Claude Kaufmann
    Medical Faculty, University of Zurich, Zurich, Switzerland.
  • Michael A Thiel
    Medical Faculty, University of Zurich, Zurich, Switzerland.
  • Philipp B Baenninger
    Medical Faculty, University of Zurich, Zurich, Switzerland.

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