Deep learning in fracture detection: a narrative review

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....

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Veröffentlicht in:Acta orthopaedica Jg. 91; H. 2; S. 215 - 220
Hauptverfasser: Kalmet, Pishtiwan H S, Sanduleanu, Sebastian, Primakov, Sergey, Wu, Guangyao, Jochems, Arthur, Refaee, Turkey, Ibrahim, Abdalla, Hulst, Luca v., Lambin, Philippe, Poeze, Martijn
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Sweden Taylor & Francis 03.03.2020
Medical Journals Sweden
Schlagworte:
Deep Learning
Fractures, Bone - diagnostic imaging
Humans
Radiographic Image Interpretation, Computer-Assisted - methods
Radiography
Tomography, X-Ray Computed
ISSN:1745-3674, 1745-3682, 1745-3682
Online-Zugang:Volltext
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Zusammenfassung: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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ISSN:1745-3674
1745-3682
1745-3682
DOI:10.1080/17453674.2019.1711323