Feasibility of a Support Vector Machine Classifier for Myofascial Pain Syndrome: Diagnostic Case-Control Study.

Journal: Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine

Published Date: Aug 1, 2019

Abstract

OBJECTIVES: Myofascial pain syndrome (MPS) is the most common cause of chronic pain worldwide. The diagnosis of MPS is subjective, which has created a need for a robust quantitative method of diagnosing MPS. We propose that using a support vector machine (SVM) along with ultrasound (US) texture features can differentiate between healthy and MPS-affected skeletal muscle.

Authors

Michael Behr

Department of Medicine, Division of Physical Medicine and Rehabilitation, University of Toronto, Toronto, Ontario, Canada.
Michael Noseworthy

McMaster School of Biomedical Engineering, McMaster University, Hamilton, Ontario, Canada.
Dinesh Kumbhare

Department of Medicine, Division of Physical Medicine and Rehabilitation, University of Toronto, Toronto, Ontario, Canada.

Keywords

Adult Algorithms Case-Control Studies Feasibility Studies Female Humans Male Myofascial Pain Syndromes Sensitivity and Specificity Superficial Back Muscles Support Vector Machine Syndrome Ultrasonography Young Adult

External Resources

View on PubMed Access via DOI PubMed (30614553)

Feasibility of a Support Vector Machine Classifier for Myofascial Pain Syndrome: Diagnostic Case-Control Study.

Abstract

Authors

Keywords

External Resources

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