Artificial intelligence fracture recognition on computed tomography: review of literature and recommendations.

Journal: European journal of trauma and emergency surgery : official publication of the European Trauma Society
Published Date: Oct 26, 2022

Abstract

PURPOSE: The use of computed tomography (CT) in fractures is time consuming, challenging and suffers from poor inter-surgeon reliability. Convolutional neural networks (CNNs), a subset of artificial intelligence (AI), may overcome shortcomings and reduce clinical burdens to detect and classify fractures. The aim of this review was to summarize literature on CNNs for the detection and classification of fractures on CT scans, focusing on its accuracy and to evaluate the beneficial role in daily practice.

Authors

  • Lente H M Dankelman
    Trauma Research Unit, Department of Surgery, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands. l.dankelman@erasmusmc.nl.
  • Sanne Schilstra
    Department of Orthopedic Surgery, Groningen University Medical Centre, Groningen, The Netherlands.
  • Frank F A Ijpma
    Department of Trauma Surgery, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
  • Job N Doornberg
  • Joost W Colaris
    Department of Orthopedics, Erasmus University Medical Centre, Rotterdam, The Netherlands.
  • Michael H J Verhofstad
    Trauma Research Unit, Department of Surgery, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands.
  • Mathieu M E Wijffels
    Trauma Research Unit, Department of Surgery, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands.
  • Jasper Prijs
    Flinders University, Adelaide, Australia.

Keywords

External Resources

Popular Topics
Recent Journals