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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....
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Taylor & Francis
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7144272/ https://www.ncbi.nlm.nih.gov/pubmed/31928116 http://dx.doi.org/10.1080/17453674.2019.1711323 |
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author | Kalmet, Pishtiwan H S Sanduleanu, Sebastian Primakov, Sergey Wu, Guangyao Jochems, Arthur Refaee, Turkey Ibrahim, Abdalla Hulst, Luca v. Lambin, Philippe Poeze, Martijn |
author_facet | Kalmet, Pishtiwan H S Sanduleanu, Sebastian Primakov, Sergey Wu, Guangyao Jochems, Arthur Refaee, Turkey Ibrahim, Abdalla Hulst, Luca v. Lambin, Philippe Poeze, Martijn |
author_sort | Kalmet, Pishtiwan H S |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7144272 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Taylor & Francis |
record_format | MEDLINE/PubMed |
spelling | pubmed-71442722020-04-13 Deep learning in fracture detection: a narrative review Kalmet, Pishtiwan H S Sanduleanu, Sebastian Primakov, Sergey Wu, Guangyao Jochems, Arthur Refaee, Turkey Ibrahim, Abdalla Hulst, Luca v. Lambin, Philippe Poeze, Martijn Acta Orthop Articles 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. Taylor & Francis 2020-01-13 /pmc/articles/PMC7144272/ /pubmed/31928116 http://dx.doi.org/10.1080/17453674.2019.1711323 Text en © 2020 The Author(s). Published by Taylor & Francis on behalf of the Nordic Orthopedic Federation https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Articles Kalmet, Pishtiwan H S Sanduleanu, Sebastian Primakov, Sergey Wu, Guangyao Jochems, Arthur Refaee, Turkey Ibrahim, Abdalla Hulst, Luca v. Lambin, Philippe Poeze, Martijn Deep learning in fracture detection: a narrative review |
title | Deep learning in fracture detection: a narrative review |
title_full | Deep learning in fracture detection: a narrative review |
title_fullStr | Deep learning in fracture detection: a narrative review |
title_full_unstemmed | Deep learning in fracture detection: a narrative review |
title_short | Deep learning in fracture detection: a narrative review |
title_sort | deep learning in fracture detection: a narrative review |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7144272/ https://www.ncbi.nlm.nih.gov/pubmed/31928116 http://dx.doi.org/10.1080/17453674.2019.1711323 |
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