title: Classification of sodium MRI data of cartilage using machine learning.
abstract: OBJECTIVE
  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.
  
  
  CONCLUSIONS
  Machine learning is a promising technique for classifying osteoarthritis patients and controls from sodium magnetic resonance imaging data.
authors.affiliation: Department of Radiology, Center for Biomedical Imaging, New York University School of Medicine, New York, New York, USA.
authors.forename: Guillaume
authors.initials: G
authors.lastname: Madelin
authors.affiliation: Departamento de Astrofísica, Instituto de Astrofísica de Canarias, La Laguna, Tenerife, Spain; Universidad de La Laguna, La Laguna, Tenerife, Spain.
authors.forename: Frederick
authors.initials: F
authors.lastname: Poidevin
authors.affiliation: Department of Physics & Astronomy, University College London, Kathleen Lonsdale Building, Gower Place, London, UK.
authors.forename: Antonios
authors.initials: A
authors.lastname: Makrymallis
authors.affiliation: Department of Radiology, Center for Biomedical Imaging, New York University School of Medicine, New York, New York, USA.
authors.forename: Ravinder R
authors.initials: RR
authors.lastname: Regatte
mesh_terms: D000465:Algorithms; D002358:Cartilage, Articular / Q000033:anatomy & histology* / Q000473:pathology*; D006801:Humans; D007717:Knee / Q000033:anatomy & histology; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010003:Osteoarthritis / Q000473:pathology*; D012964:Sodium
chemical_list: D012964:Sodium
keywords: cartilage; classification; machine leaning; osteoarthritis; sodium MRI