Rapid muscle volume prediction using anthropometric measurements and population-derived statistical models

• Published in: Biomechanics and modeling in mechanobiology Vol. 19; no. 4; pp. 1239 - 1249
• Main Authors: Yeung, S., Fernandez, J. W., Handsfield, G. G., Walker, C., Besier, T. F., Zhang, J.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2020
• Subjects: Biological and Medical Physics; Biomedical Engineering and Bioengineering; Biophysics; Engineering; Original Paper; Theoretical and Applied Mechanics; Statistical model; Predictive model; Machine learning; Regression; Muscle; Computer tomography; SVM; Muscle volume; PLSR
• ISSN: 1617-7959; 1617-7940; 1617-7940
• DOI: 10.1007/s10237-019-01243-0

Knowledge of subject-specific muscle volumes may be used as surrogates for evaluating muscle strength and power generated by ‘fat-free’ muscle mass. This study presents population-based statistical learning approaches for predicting ‘fat-free’ muscle volume from known anthropometric measurements. Using computed tomography (CT) imaging data to obtain lower-limb muscle volumes from 50 men and women, this study evaluated six statistical learning methods for predicting muscle volumes from anthropometric measurements: (i) stepwise regression, (ii) linear support vector machine (SVM), (iii) 2nd-order polynomial SVM, (iv) linear partial least squares regression (PLSR), (v) quadratic PLSR, and (vi) 3rd-order spline fit PLSR. These techniques have successfully been demonstrated in bioengineering applications and freely available in open-source toolkits. Analysis revealed that separating a general population into sexes and/or cohorts based on adipose level may improve prediction accuracies. The most important measures that statistically influence muscle volume predictions were shank girth, followed by sex and finally leg length, as identified using stepwise regression. SVM learning predicted muscle volume with an accuracy of 85 ± 4% when using linear interpolation, but performed poorly with an accuracy of 59 ± 6% using polynomial interpolation. The simpler linear PLSR exhibited muscle volume prediction accuracy of 87 ± 2%, while quadratic PLSR was slightly reduced at 82 ± 3%. For the spline fit PLSR, high accuracy was observed on the training data set (~ 99%) but over-fitting (a drawback of high-interpolation methods) resulted in erroneous predictions on testing data, and hence, the model was deemed unsuitable. In conclusion, use of linear PLSR models with variables of sex, leg length, and shank girth is a useful tool for predicting ‘fat-free’ muscle volume.