Deep Learning in the Detection of Rare Fractures - Development of a "Deep Learning Convolutional Network" Model for Detecting Acetabular Fractures.

Journal: Zeitschrift fur Orthopadie und Unfallchirurgie
Published Date: Feb 1, 2023

Abstract

BACKGROUND: Fracture detection by artificial intelligence and especially Deep Convolutional Neural Networks (DCNN) is a topic of growing interest in current orthopaedic and radiological research. As learning a DCNN usually needs a large amount of training data, mostly frequent fractures as well as conventional X-ray are used. Therefore, less common fractures like acetabular fractures (AF) are underrepresented in the literature. The aim of this pilot study was to establish a DCNN for detection of AF using computer tomography (CT) scans.

Authors

  • Felix Erne
    Department of Trauma and Reconstructive Surgery, Occupational Accident Clinic Tübingen, Tübingen, Germany.
  • Daniel Dehncke
    Department of Informatics, Methods in Medical Informatics, Eberhard Karls University of Tübingen Faculty of Mathematics and Natural Sciences, Tubingen, Germany.
  • Steven C Herath
    Department of Trauma and Reconstructive Surgery, Occupational Accident Clinic Tübingen, Tübingen, Germany.
  • Fabian Springer
    Department of Diagnostic & Interventional Radiology, University Hospital Tübingen, Tübingen, Germany.
  • Nico Pfeifer
    Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Campus E1.4, 66123 Saarbrücken and Saarbrücken Graduate School of Computer Science, Saarland University, 66123 Saarbrücken.
  • Ralf Eggeling
    Department of Informatics, Methods in Medical Informatics, Eberhard Karls University of Tübingen Faculty of Mathematics and Natural Sciences, Tubingen, Germany.
  • Markus Alexander Küper
    Department of Trauma and Reconstructive Surgery, Occupational Accident Clinic Tübingen, Tübingen, Germany.

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