Deep learning in fracture detection: a narrative review

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 The D-Lab: Decision Support for Precision Medicine, GROW— School for Oncology and Developmental Biology, Maastricht University Medical Center+, MaastrichtGuangyao WU
  • Arthur Jochems The D-Lab: Decision Support for Precision Medicine, GROW— School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht
  • Turkey Refaee The D-Lab: Decision Support for Precision Medicine, GROW— School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht
  • 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 The D-Lab: Decision Support for Precision Medicine, GROW— School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht
  • Martijn Poeze Maastricht University Medical Center+, Department of Trauma Surgery, Maastricht; Nutrim School for Nutrition, Toxicology and Metabolism, Maastricht University, Maastricht, The Netherlands

DOI:

https://doi.org/10.1080/17453674.2019.1711323

Abstract

Artificial intelligence (AI) is a general term that implies the use of a computer to model intelligent behav­ ior 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 nar­ rative 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 radio­ graphs and CT examinations; (2) discuss what value deep learning offers to this field; and finally (3) comment on future directions of this technology.

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Author Biographies

Pishtiwan H S Kalmet, Maastricht University Medical Center+, Department of Trauma Surgery, Maastricht

* Shared first authorship

Sebastian Sanduleanu, The D-Lab: Decision Support for Precision Medicine, GROW— School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht

* Shared first authorship

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Published

2020-01-13

How to Cite

Kalmet, P. H. S., Sanduleanu, S., Primakov, . S., Wu, G., Jochems, A., Refaee, T., … Poeze, M. . (2020). Deep learning in fracture detection: a narrative review. Acta Orthopaedica, 91(2), 215–220. https://doi.org/10.1080/17453674.2019.1711323