Abstract
Shoulder strength data are important for post-operative assessment of shoulder function and have been used in diagnosis of rotator cuff pathology. Support vector machines (SVM) employ complex analysis techniques to solve classification and regression problems. A SVM, a machine learning technique, can be used for analysis and classification of shoulder strength data. The goals of this study were to determine the diagnostic competency of SVM based on shoulder strength data and to apply SVM analysis in efforts to derive a single representative shoulder strength score. Data were taken from fourteen isometric shoulder strength measurements of each shoulder (involved and uninvolved) in 45 rotator cuff tear patients. SVM diagnostic proficiency was found to be comparable to reported ultrasound values. Improvement of shoulder function was accurately represented by a single score in pairwise comparison of the pre-operative and the 12 month post-operative group (P<0.004). Thus, the SVM-based score may be a promising metric for summarizing rotator cuff strength data.
Original language | English (US) |
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Pages (from-to) | 973-979 |
Number of pages | 7 |
Journal | Journal of Biomechanics |
Volume | 39 |
Issue number | 5 |
DOIs | |
State | Published - 2006 |
Funding
The authors would like to recognize the financial support from the University of Michigan Student Biomedical Research Programs, Steven A. Goldstein, and NIH grants AR41171, AR048540, and HD07447. The authors would also like to thank Michael Rock, M.D., for contributing patients.
Keywords
- Rotator cuff tear
- Shoulder strength
- Support vector machine
ASJC Scopus subject areas
- Biophysics
- Rehabilitation
- Biomedical Engineering
- Orthopedics and Sports Medicine