Detection of ankle fractures using deep learning algorithms.

Journal: Foot and ankle surgery : official journal of the European Society of Foot and Ankle Surgeons
Published Date: Dec 1, 2022

Abstract

BACKGROUND: Early and accurate detection of ankle fractures are crucial for optimizing treatment and thus reducing future complications. Radiographs are the most abundant imaging techniques for assessing fractures. Deep learning (DL) methods, through adequately trained deep convolutional neural networks (DCNNs), have been previously shown to faster and accurately analyze radiographic images without human intervention. Herein, we aimed to assess the performance of two different DCNNs in detecting ankle fractures using radiographs compared to the ground truth.

Authors

  • Soheil Ashkani-Esfahani
    Foot & Ankle Research and Innovation Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston 02114, MA, USA; Department of Orthopaedic Surgery, Amsterdam University Medical Center, University of Amsterdam, Amsterdam Movement Sciences, Amsterdam, the Netherlands; Foot & Ankle Service, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston 02114, MA, USA. Electronic address: sashkaniesfahani@mgh.harvard.edu.
  • Reza Mojahed Yazdi
    Foot & Ankle Research and Innovation Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston 02114, MA, USA. Electronic address: rmojahedy@gmail.com.
  • Rohan Bhimani
    Foot & Ankle Research and Innovation Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston 02114, MA, USA. Electronic address: dr.rohanbhimani@gmail.com.
  • Gino M Kerkhoffs
    Department of Orthopaedic Surgery, Amsterdam University Medical Center, University of Amsterdam, Amsterdam Movement Sciences, Amsterdam, the Netherlands. Electronic address: g.m.kerkhoffs@amsterdamumc.nl.
  • Mario Maas
    Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, The Netherlands.
  • Christopher W DiGiovanni
    Foot & Ankle Research and Innovation Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston 02114, MA, USA; Foot & Ankle Service, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston 02114, MA, USA. Electronic address: cwdigiovanni@partners.org.
  • Bart Lubberts
    Foot & Ankle Research and Innovation Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston 02114, MA, USA. Electronic address: Blubberts@partners.org.
  • Daniel Guss
    Foot & Ankle Research and Innovation Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston 02114, MA, USA; Foot & Ankle Service, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston 02114, MA, USA. Electronic address: daniel.guss@mgh.harvard.edu.

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