title: Bayesian belief network analysis applied to determine the progression of temporomandibular disorders using MRI.
abstract: OBJECTIVE
  This study investigated the applicability of a Bayesian belief network (BBN) to MR images to diagnose temporomandibular disorders (TMDs). Our aim was to determine the progression of TMDs, focusing on how each finding affects the other.
  
  
  METHODS
  We selected 1.5-T MRI findings (33 variables) and diagnoses (bone changes and disc displacement) of patients with TMD from 2007 to 2008. There were a total of 295 cases with 590 sides of temporomandibular joints (TMJs). The data were modified according to the research diagnostic criteria of TMD. We compared the accuracy of the BBN using 11 algorithms (necessary path condition, path condition, greedy search-and-score with Bayesian information criterion, Chow-Liu tree, Rebane-Pearl poly tree, tree augmented naïve Bayes model, maximum log likelihood, Akaike information criterion, minimum description length, K2 and C4.5), a multiple regression analysis and an artificial neural network using resubstitution validation and 10-fold cross-validation.
  
  
  RESULTS
  There were 191 TMJs (32.4%) with bone changes and 340 (57.6%) with articular disc displacement. The BBN path condition algorithm using resubstitution validation and 10-fold cross-validation was >99% accurate. However, the main advantage of a BBN is that it can represent the causal relationships between different findings and assign conditional probabilities, which can then be used to interpret the progression of TMD.
  
  
  CONCLUSIONS
  Osteoarthritic bone changes progressed from condyle to articular fossa and finally to mandibular bone contours. Disc displacement was directly related to severe bone changes. Early bone changes were not directly related to disc displacement. TMJ functional factors (condylar translation, bony space and disc form) and age mediated between bone changes and disc displacement.
authors.affiliation: Support Office of Frontier Oral Science in Faculty of Dentistry, Institute of Health Bioscience, Graduate School, The University of Tokushima, Tokushima, Japan.
authors.forename: H
authors.initials: H
authors.lastname: Iwasaki
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001499:Bayes Theorem; D002648:Child; D018450:Disease Progression; D005260:Female; D006801:Humans; D004204:Joint Dislocations / Q000175:diagnosis / Q000503:physiopathology; D016013:Likelihood Functions; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D008297:Male; D008334:Mandible / Q000473:pathology; D008335:Mandibular Condyle / Q000473:pathology; D008875:Middle Aged; D016571:Neural Networks, Computer; D010003:Osteoarthritis / Q000175:diagnosis / Q000503:physiopathology; D054850:Osteophyte / Q000175:diagnosis / Q000503:physiopathology; D010026:Osteosclerosis / Q000175:diagnosis / Q000503:physiopathology; D012044:Regression Analysis; D015203:Reproducibility of Results; D013701:Temporal Bone / Q000473:pathology; D019224:Temporomandibular Joint Disc / Q000473:pathology; D013705:Temporomandibular Joint Disorders / Q000175:diagnosis / Q000503:physiopathology*; D055815:Young Adult
keywords: Bayesian method; algorithms; computer simulation; magnetic resonance imaging; temporomandibular joint disorders