Deep learning in fracture detection: a narrative review

Pishtiwan H. S. Kalmet*, Sebastian Sanduleanu, Sergey Primakov, Guangyao Wu, Arthur Jochems, Turkey Refaee, Abdalla Ibrahim, Luca Hulst, Philippe Lambin, Martijn Poeze

*Corresponding author for this work

Research output: Contribution to journal(Systematic) Review article peer-review

38 Citations (Web of Science)

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.

Original languageEnglish
Pages (from-to)215-220
Number of pages6
JournalActa Orthopaedica
Volume91
Issue number2
DOIs
Publication statusPublished - Mar 2020

Keywords

  • ARTIFICIAL-INTELLIGENCE
  • AUTOMATED CLASSIFICATION
  • DIABETIC-RETINOPATHY
  • NEURAL-NETWORK
  • RADIOLOGY
  • SEGMENTATION
  • VALIDATION
  • FUTURE
  • IMAGES

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