Classification of sodium MRI data of cartilage using machine learning

Purpose To assess the possible utility of machine learning for classifying subjects with and subjects without osteoarthritis using sodium magnetic resonance imaging data. Theory: support vector machine, k‐nearest neighbors, naïve Bayes, discriminant analysis, linear regression, logistic regression,...

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Published in:	Magnetic resonance in medicine 2015-11, Vol.74 (5), p.1435-1448
Main Authors:	Madelin, Guillaume, Poidevin, Frederick, Makrymallis, Antonios, Regatte, Ravinder R.
Format:	Article
Language:	eng
Subjects:	Algorithms cartilage Cartilage, Articular - anatomy & histology Cartilage, Articular - pathology classification Humans Knee - anatomy & histology machine leaning Machine Learning Magnetic Resonance Imaging - methods osteoarthritis Osteoarthritis - pathology Sodium sodium MRI
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Summary:	Purpose To assess the possible utility of machine learning for classifying subjects with and subjects without osteoarthritis using sodium magnetic resonance imaging data. Theory: support vector machine, k‐nearest neighbors, naïve Bayes, discriminant analysis, linear regression, logistic regression, neural networks, decision tree, and tree bagging were tested. Methods Sodium magnetic resonance imaging with and without fluid suppression by inversion recovery was acquired on the knee cartilage of 19 controls and 28 osteoarthritis patients. Sodium concentrations were measured in regions of interests in the knee for both acquisitions. Mean (MEAN) and standard deviation (STD) of these concentrations were measured in each regions of interest, and the minimum, maximum, and mean of these two measurements were calculated over all regions of interests for each subject. The resulting 12 variables per subject were used as predictors for classification. Results Either Min [STD] alone, or in combination with Mean [MEAN] or Min [MEAN], all from fluid suppressed data, were the best predictors with an accuracy >74%, mainly with linear logistic regression and linear support vector machine. Other good classifiers include discriminant analysis, linear regression, and naïve Bayes. Conclusion Machine learning is a promising technique for classifying osteoarthritis patients and controls from sodium magnetic resonance imaging data. Magn Reson Med 74:1435–1448, 2015. © 2014 Wiley Periodicals, Inc.
ISSN:	0740-3194 1522-2594