Classification of sodium MRI data of cartilage using machine learning.

Journal: Magnetic resonance in medicine
PMID: 25367844

Abstract

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.

Authors

  • Guillaume Madelin
    Department of Radiology, Center for Biomedical Imaging, New York University School of Medicine, New York, New York, USA.
  • Frederick Poidevin
    Departamento de Astrofísica, Instituto de Astrofísica de Canarias, La Laguna, Tenerife, Spain; Universidad de La Laguna, La Laguna, Tenerife, Spain.
  • Antonios Makrymallis
    Department of Physics & Astronomy, University College London, Kathleen Lonsdale Building, Gower Place, London, UK.
  • Ravinder R Regatte
    Department of Radiology, Center for Biomedical Imaging, New York University School of Medicine, New York, New York, USA.

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