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Machine Learning in Orthopedics: A Literature Review
In this paper we present the findings of a systematic literature review covering the articles published in the last two decades in which the authors described the application of a machine learning technique and method to an orthopedic problem or purpose. By searching both in the Scopus and Medline d...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2018
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6030383/ https://www.ncbi.nlm.nih.gov/pubmed/29998104 http://dx.doi.org/10.3389/fbioe.2018.00075 |
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author | Cabitza, Federico Locoro, Angela Banfi, Giuseppe |
author_facet | Cabitza, Federico Locoro, Angela Banfi, Giuseppe |
author_sort | Cabitza, Federico |
collection | PubMed |
description | In this paper we present the findings of a systematic literature review covering the articles published in the last two decades in which the authors described the application of a machine learning technique and method to an orthopedic problem or purpose. By searching both in the Scopus and Medline databases, we retrieved, screened and analyzed the content of 70 journal articles, and coded these resources following an iterative method within a Grounded Theory approach. We report the survey findings by outlining the articles' content in terms of the main machine learning techniques mentioned therein, the orthopedic application domains, the source data and the quality of their predictive performance. |
format | Online Article Text |
id | pubmed-6030383 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-60303832018-07-11 Machine Learning in Orthopedics: A Literature Review Cabitza, Federico Locoro, Angela Banfi, Giuseppe Front Bioeng Biotechnol Bioengineering and Biotechnology In this paper we present the findings of a systematic literature review covering the articles published in the last two decades in which the authors described the application of a machine learning technique and method to an orthopedic problem or purpose. By searching both in the Scopus and Medline databases, we retrieved, screened and analyzed the content of 70 journal articles, and coded these resources following an iterative method within a Grounded Theory approach. We report the survey findings by outlining the articles' content in terms of the main machine learning techniques mentioned therein, the orthopedic application domains, the source data and the quality of their predictive performance. Frontiers Media S.A. 2018-06-27 /pmc/articles/PMC6030383/ /pubmed/29998104 http://dx.doi.org/10.3389/fbioe.2018.00075 Text en Copyright © 2018 Cabitza, Locoro and Banfi. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Bioengineering and Biotechnology Cabitza, Federico Locoro, Angela Banfi, Giuseppe Machine Learning in Orthopedics: A Literature Review |
title | Machine Learning in Orthopedics: A Literature Review |
title_full | Machine Learning in Orthopedics: A Literature Review |
title_fullStr | Machine Learning in Orthopedics: A Literature Review |
title_full_unstemmed | Machine Learning in Orthopedics: A Literature Review |
title_short | Machine Learning in Orthopedics: A Literature Review |
title_sort | machine learning in orthopedics: a literature review |
topic | Bioengineering and Biotechnology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6030383/ https://www.ncbi.nlm.nih.gov/pubmed/29998104 http://dx.doi.org/10.3389/fbioe.2018.00075 |
work_keys_str_mv | AT cabitzafederico machinelearninginorthopedicsaliteraturereview AT locoroangela machinelearninginorthopedicsaliteraturereview AT banfigiuseppe machinelearninginorthopedicsaliteraturereview |