High accuracy in lower limb alignment analysis using convolutional neural networks, with improvements needed for joint-level metrics.

Journal: Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
Published Date: Sep 22, 2024

Abstract

PURPOSE: Evaluation of long-leg standing radiographs (LSR) is a standardised procedure for analysis of primary or secondary deformities of the lower limbs. Deep-learning convolutional neural networks (CNN) offer the potential to enhance radiological measurement by increasing reproducibility and accuracy. This study aims to evaluate the measurement accuracy of an automated CNN-based planning tool (mediCAD® 7.0; mediCAD Hectec GmbH) of lower limb deformities.

Authors

  • Christof Hoffmann
    Department of Trauma Surgery, BG Trauma Center Murnau, Murnau, Germany.
  • Fatih Göksu
    Department of Orthopedics and Reconstructive Surgery, Diakonie Klinikum, GmbH Jung-Stilling-Krankenhaus, Siegen, Germany.
  • Isabella Klöpfer-Krämer
    Department of Trauma Surgery, BG Trauma Center Murnau, Murnau, Germany.
  • Julius Watrinet
    Department of Orthopaedic Sports Medicine, Technical University, Munich, Germany.
  • Philipp Blum
    Department of Trauma Surgery, BG Trauma Center Murnau, Murnau, Germany.
  • Sven Hungerer
    Department of Trauma Surgery, BG Trauma Center Murnau, Murnau, Germany.
  • Steffen Schröter
    Department of Orthopedics and Reconstructive Surgery, Diakonie Klinikum, GmbH Jung-Stilling-Krankenhaus, Siegen, Germany.
  • Fabian Stuby
    Department of Trauma Surgery, BG Trauma Center Murnau, Murnau, Germany.
  • Peter Augat
    Institute of Biomechanics, BG Trauma Centre Murnau, Professor-Küntscher-Strasse 8, 82418, Murnau am Staffelsee, Germany.
  • Julian Fürmetz
    Department of Trauma Surgery, BG Trauma Center Murnau, Murnau, Germany.

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