Deep learning in fracture detection: a narrative review
DOI:
https://doi.org/10.1080/17453674.2019.1711323Abstract
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.
Downloads
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2020 Pishtiwan H S Kalmet, Sebastian Sanduleanu, Sergey Primakov, Guangyao Wu, Arthur Jochems, Turkey Refaee, Abdalla Ibrahim, Luca v. Hulst, Philippe Lambin, Martijn Poeze

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
