Title |
Machine Learning in Orthopedics: A Literature Review
|
---|---|
Published in |
Frontiers in Bioengineering and Biotechnology, June 2018
|
DOI | 10.3389/fbioe.2018.00075 |
Pubmed ID | |
Authors |
Federico Cabitza, Angela Locoro, Giuseppe Banfi |
Abstract |
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. |
X Demographics
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Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | 20% |
Brazil | 1 | 20% |
United States | 1 | 20% |
Unknown | 2 | 40% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 5 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 296 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 35 | 12% |
Student > Ph. D. Student | 31 | 10% |
Researcher | 27 | 9% |
Student > Doctoral Student | 24 | 8% |
Student > Bachelor | 18 | 6% |
Other | 53 | 18% |
Unknown | 108 | 36% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 47 | 16% |
Medicine and Dentistry | 44 | 15% |
Computer Science | 39 | 13% |
Business, Management and Accounting | 7 | 2% |
Agricultural and Biological Sciences | 4 | 1% |
Other | 29 | 10% |
Unknown | 126 | 43% |