title: Predicting early symptomatic osteoarthritis in the human knee using machine learning classification of magnetic resonance images from the osteoarthritis initiative.
abstract: The purpose of this study is to evaluate the ability of a machine learning algorithm to classify in vivo magnetic resonance images (MRI) of human articular cartilage for development of osteoarthritis (OA). Sixty-eight subjects were selected from the osteoarthritis initiative (OAI) control and incidence cohorts. Progression to clinical OA was defined by the development of symptoms as quantified by the Western Ontario and McMaster Universities Arthritis (WOMAC) questionnaire 3 years after baseline evaluation. Multi-slice T2-weighted knee images, obtained through the OAI, of these subjects were registered using a nonlinear image registration algorithm. T2maps of cartilage from the central weight bearing slices of the medial femoral condyle were derived from the registered images using the multiple available echo times and were classified for "progression to symptomatic OA" using the machine learning tool, weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHRM). WND-CHRM classified the isolated T2maps for the progression to symptomatic OA with 75% accuracy.
  
  
  
  Machine learning algorithms applied to T2maps have the potential to provide important prognostic information for the development of OA. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:2243-2250, 2017.
authors.affiliation: Laboratory of Clinical Investigation, Magnetic Resonance Imaging and Spectroscopy Section, National Institute on Aging, NIH, 251 Bayview Boulevard, Baltimore 21224, Maryland.
authors.forename: Beth G
authors.initials: BG
authors.lastname: Ashinsky
authors.affiliation: Laboratory of Clinical Investigation, Magnetic Resonance Imaging and Spectroscopy Section, National Institute on Aging, NIH, 251 Bayview Boulevard, Baltimore 21224, Maryland.
authors.forename: Mustapha
authors.initials: M
authors.lastname: Bouhrara
authors.affiliation: Image Informatics and Computational Biology Unit, National Institute on Aging, NIH, Baltimore, Maryland.
authors.forename: Christopher E
authors.initials: CE
authors.lastname: Coletta
authors.affiliation: Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California.
authors.forename: Benoit
authors.initials: B
authors.lastname: Lehallier
authors.affiliation: Bone and Joint Center, Magee Women's Hospital, Department of Orthopaedic Surgery, Pittsburgh, Pennsylvania.
authors.forename: Kenneth L
authors.initials: KL
authors.lastname: Urish
authors.affiliation: Department of Radiology, College of Medicine, Howard University, Washington, DC, Washington.
authors.forename: Ping-Chang
authors.initials: PC
authors.lastname: Lin
authors.affiliation: Image Informatics and Computational Biology Unit, National Institute on Aging, NIH, Baltimore, Maryland.
authors.forename: Ilya G
authors.initials: IG
authors.lastname: Goldberg
authors.affiliation: Laboratory of Clinical Investigation, Magnetic Resonance Imaging and Spectroscopy Section, National Institute on Aging, NIH, 251 Bayview Boulevard, Baltimore 21224, Maryland.
authors.forename: Richard G
authors.initials: RG
authors.lastname: Spencer
mesh_terms: D000465:Algorithms; D002358:Cartilage, Articular / Q000000981:diagnostic imaging*; D015331:Cohort Studies; D006801:Humans; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging; D008875:Middle Aged; D020370:Osteoarthritis, Knee / Q000000981:diagnostic imaging*; D012044:Regression Analysis
keywords: MRI; classification; osteoarthritis; pattern recognition; registration; segmentation