title: Machine learning classification of OARSI-scored human articular cartilage using magnetic resonance imaging.
abstract: OBJECTIVE
  The purpose of this study is to evaluate the ability of machine learning to discriminate between magnetic resonance images (MRI) of normal and pathological human articular cartilage obtained under standard clinical conditions.
  
  
  METHODS
  An approach to MRI classification of cartilage degradation is proposed using pattern recognition and multivariable regression in which image features from MRIs of histologically scored human articular cartilage plugs were computed using weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHRM). The WND-CHRM method was first applied to several clinically available MRI scan types to perform binary classification of normal and osteoarthritic osteochondral plugs based on the Osteoarthritis Research Society International (OARSI) histological system. In addition, the image features computed from WND-CHRM were used to develop a multiple linear least-squares regression model for classification and prediction of an OARSI score for each cartilage plug.
  
  
  RESULTS
  The binary classification of normal and osteoarthritic plugs yielded results of limited quality with accuracies between 36% and 70%. However, multiple linear least-squares regression successfully predicted OARSI scores and classified plugs with accuracies as high as 86%. The present results improve upon the previously-reported accuracy of classification using average MRI signal intensities and parameter values.
  
  
  CONCLUSIONS
  MRI features detected by WND-CHRM reflect cartilage degradation status as assessed by OARSI histologic grading. WND-CHRM is therefore of potential use in the clinical detection and grading of osteoarthritis.
authors.affiliation: Magnetic Resonance Imaging and Spectroscopy Section, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States. Electronic address: beth.ashinsky@gmail.com.
authors.forename: B G
authors.initials: BG
authors.lastname: Ashinsky
authors.affiliation: Image Informatics and Computational Biology Unit, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States. Electronic address: christopher.coletta@nih.gov.
authors.forename: C E
authors.initials: CE
authors.lastname: Coletta
authors.affiliation: Magnetic Resonance Imaging and Spectroscopy Section, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States. Electronic address: mustapha.bouhrara2@nih.gov.
authors.forename: M
authors.initials: M
authors.lastname: Bouhrara
authors.affiliation: Magnetic Resonance Imaging and Spectroscopy Section, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States. Electronic address: vannylukas@gmail.com.
authors.forename: V A
authors.initials: VA
authors.lastname: Lukas
authors.affiliation: Magnetic Resonance Imaging and Spectroscopy Section, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States. Electronic address: jmboyle@oakland.edu.
authors.forename: J M
authors.initials: JM
authors.lastname: Boyle
authors.affiliation: Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States. Electronic address: reiterda@nia.nih.gov.
authors.forename: D A
authors.initials: DA
authors.lastname: Reiter
authors.affiliation: Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States. Electronic address: cpneu@purdue.edu.
authors.forename: C P
authors.initials: CP
authors.lastname: Neu
authors.affiliation: Image Informatics and Computational Biology Unit, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States. Electronic address: goldbergil@helix.nih.gov.
authors.forename: I G
authors.initials: IG
authors.lastname: Goldberg
authors.affiliation: Magnetic Resonance Imaging and Spectroscopy Section, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States. Electronic address: spencer@helix.nih.gov.
authors.forename: R G
authors.initials: RG
authors.lastname: Spencer
mesh_terms: D000465:Algorithms; D002358:Cartilage, Articular / Q000473:pathology*; D038524:Diffusion Magnetic Resonance Imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016018:Least-Squares Analysis; D016014:Linear Models; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging; D015999:Multivariate Analysis; D020370:Osteoarthritis, Knee / Q000175:diagnosis / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*
keywords: Classification; Human articular cartilage; MRI; Osteoarthritis; Pattern recognition