Automatic quantification of scapular and glenoid morphology from CT scans using deep learning.

Journal: European journal of radiology
PMID: 38944907

Abstract

OBJECTIVES: To develop and validate an open-source deep learning model for automatically quantifying scapular and glenoid morphology using CT images of normal subjects and patients with glenohumeral osteoarthritis.

Authors

  • Osman Berk Satir
    ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
  • Pezhman Eghbali
    Laboratory of Biomechanical Orthopedics, Ecole Polytechnique Fédérale de Lausanne, Institute of Bioengineering, Switzerland.
  • Fabio Becce
    Department of Diagnostic and Interventional Radiology, Lausanne University Hospital, University of Lausanne, Lausanne 1011, Switzerland.
  • Patrick Goetti
    Department of Orthopedics and Traumatology, Lausanne University Hospital and University of Lausanne, Switzerland.
  • Arnaud Meylan
    Department of Orthopedics and Traumatology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
  • Kilian Rothenbühler
    Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
  • Robin Diot
    Department of Orthopedics and Traumatology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
  • Alexandre Terrier
    Laboratory of Biomechanical Orthopedics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland.
  • Philippe Büchler
    ARTORG Center for Biomedical Engineering Research, University of Bern, Freiburgstrasse 3, CH-3010, Bern, Switzerland. philippe.buechler@artorg.unibe.ch.

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