Deep learning in fracture detection: a narrative review. (3rd March 2020)

• Record Type: Journal Article
• Authors: Kalmet, Pishtiwan H S; Sanduleanu, Sebastian; Primakov, Sergey; Wu, Guangyao; Jochems, Arthur; Refaee, Turkey; Ibrahim, Abdalla; Hulst, Luca v.; Lambin, Philippe; Poeze, Martijn
• Is Part Of: Acta orthopaedica. Volume 91:Number 2(2020)
• Page Start: 215
• Page End: 220
• Publication Date: 2020-03-03
• Subjects: Orthopedics -- Periodicals; 616.7005
• DOI: 10.1080/17453674.2019.1711323
• Languages: English
• ISSNs: 1745-3674

Abstract

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.