Detection of Differences in Longitudinal Cartilage Thickness Loss Using a Deep-Learning Automated Segmentation Algorithm: Data From the Foundation for the National Institutes of Health Biomarkers Study of the Osteoarthritis Initiative.

Journal: Arthritis care & research
Published Date: Jun 1, 2022

Abstract

OBJECTIVE: To study the longitudinal performance of fully automated cartilage segmentation in knees with radiographic osteoarthritis (OA), we evaluated the sensitivity to change in progressor knees from the Foundation for the National Institutes of Health OA Biomarkers Consortium between the automated and previously reported manual expert segmentation, and we determined whether differences in progression rates between predefined cohorts can be detected by the fully automated approach.

Authors

  • Felix Eckstein
    Department of Imaging & Functional Musculoskeletal Research, Institute of Anatomy & Cell Biology, Paracelsus Medical University Salzburg & Nuremberg, Salzburg, Austria; Ludwig Boltzmann Institute for Arthritis and Rehabilitation, Paracelsus Medical University, Salzburg, Austria; Chondrometrics GmbH, Ainring, Germany. Electronic address: felix.eckstein@pmu.ac.at.
  • Akshay S Chaudhari
    Department of Radiology, Stanford University, Stanford, California.
  • David Fuerst
    Paracelsus Medical University, Salzburg and Nuremberg, Salzburg, Austria, and Chondrometrics, Ainring, Germany.
  • Martin Gaisberger
    Paracelsus Medical University, Salzburg and Nuremberg, Salzburg, Austria.
  • Jana Kemnitz
    Paracelsus Medical University, Salzburg and Nuremberg, and Siemens, Vienna, Austria.
  • Christian F Baumgartner
  • Ender Konukoglu
  • David J Hunter
    Royal North Shore Hospital and University of Sydney, Sydney, New South Wales, Australia.
  • Wolfgang Wirth
    Paracelsus Medical University, Salzburg and Nuremberg, Salzburg, Austria, and Chondrometrics, Ainring, Germany.

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