Deep learning in fracture detection: a narrative review.

Journal: Acta orthopaedica
Published Date:

Abstract

Artificial intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI, particularly deep learning, has recently made substantial strides in perception tasks allowing machines to better represent and interpret complex data. Deep learning is a subset of AI represented by the combination of artificial neuron layers. In the last years, deep learning has gained great momentum. In the field of orthopaedics and traumatology, some studies have been done using deep learning to detect fractures in radiographs. Deep learning studies to detect and classify fractures on computed tomography (CT) scans are even more limited. In this narrative review, we provide a brief overview of deep learning technology: we (1) describe the ways in which deep learning until now has been applied to fracture detection on radiographs and CT examinations; (2) discuss what value deep learning offers to this field; and finally (3) comment on future directions of this technology.

Authors

  • Pishtiwan H S Kalmet
    Maastricht University Medical Center+, Department of Trauma Surgery, Maastricht.
  • Sebastian Sanduleanu
    The D-Lab: Decision Support for Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht.
  • Sergey Primakov
    The D-Lab: Decision Support for Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht.
  • Guangyao Wu
    Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Arthur Jochems
    a Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology , Maastricht University Medical Centre , Maastricht , The Netherlands.
  • Turkey Refaee
    The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands, t.refaee@maastrichtuniversity.nl.
  • Abdalla Ibrahim
    The D-Lab: Decision Support for Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht.
  • Luca V Hulst
    Maastricht University Medical Center+, Department of Trauma Surgery, Maastricht.
  • Philippe Lambin
    Department of Radiation Oncology (MAASTRO Clinic), Dr. Tanslaan 12, Maastricht, The Netherlands.
  • Martijn Poeze
    Maastricht University Medical Center+, Department of Trauma Surgery, Maastricht.