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Review
. 2020 Apr;91(2):215-220.
doi: 10.1080/17453674.2019.1711323. Epub 2020 Jan 13.

Deep learning in fracture detection: a narrative review

Pishtiwan H S Kalmet  1 , Sebastian Sanduleanu  2 , Sergey Primakov  2 , Guangyao Wu  2 , Arthur Jochems  2 , Turkey Refaee  2 , Abdalla Ibrahim  2 , Luca V Hulst  1 , Philippe Lambin  2 , Martijn Poeze  1   3
Affiliations
Review

Deep learning in fracture detection: a narrative review

Pishtiwan H S Kalmet et al. Acta Orthop. 2020 Apr.

Update in

  • Deep learning in fracture detection: a narrative review.
    Kalmet PHS, Sanduleanu S, Primakov S, Wu G, Jochems A, Refaee T, Ibrahim A, Hulst LV, Lambin P, Poeze M. Kalmet PHS, et al. Acta Orthop. 2020 Jun;91(3):362. doi: 10.1080/17453674.2020.1723292. Epub 2020 Feb 5. Acta Orthop. 2020. PMID: 32019386 Free PMC article. No abstract available.

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.

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Figures

Figure 1.
Figure 1.
Visualization of Artificial Intelligence sub-family.
Figure 2.
Figure 2.
Visualization of artificial neuron model. Where A1–AN are the inputs, W1–WN are the weights for the input connections to neuron, b is the bias value, z is the output from the neuron.
Figure 3.
Figure 3.
Deep learning aided workflow in fracture detection
See this image and copyright information in PMC

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