title: Prediction of myelopathic level in cervical spondylotic myelopathy using diffusion tensor imaging.
abstract: OBJECTIVE
  To investigate the use of a newly designed machine learning-based classifier in the automatic identification of myelopathic levels in cervical spondylotic myelopathy (CSM).
  
  
  METHODS
  In all, 58 normal volunteers and 16 subjects with CSM were recruited for diffusion tensor imaging (DTI) acquisition. The eigenvalues were extracted as the selected features from DTI images. Three classifiers, naive Bayesian, support vector machine, and support tensor machine, and fractional anisotropy (FA) were employed to identify myelopathic levels. The results were compared with clinical level diagnosis results and accuracy, sensitivity, and specificity were calculated to evaluate the performance of the developed classifiers.
  
  
  RESULTS
  The accuracy by support tensor machine was the highest (93.62%) among the three classifiers. The support tensor machine also showed excellent capacity to identify true positives (sensitivity: 84.62%) and true negatives (specificity: 97.06%). The accuracy by FA value was the lowest (76%) in all the methods.
  
  
  CONCLUSIONS
  The classifiers-based method using eigenvalues had a better performance in identifying the levels of CSM than the diagnosis using FA values. The support tensor machine was the best among three classifiers.
authors.affiliation: Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, the University of Hong Kong, Hong Kong, China.|Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
authors.forename: Shu-Qiang
authors.initials: SQ
authors.lastname: Wang
authors.affiliation: Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, the University of Hong Kong, Hong Kong, China.
authors.forename: Xiang
authors.initials: X
authors.lastname: Li
authors.affiliation: Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, the University of Hong Kong, Hong Kong, China.
authors.forename: Jiao-Long
authors.initials: JL
authors.lastname: Cui
authors.affiliation: Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong, China.
authors.forename: Han-Xiong
authors.initials: HX
authors.lastname: Li
authors.affiliation: Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, the University of Hong Kong, Hong Kong, China.
authors.forename: Keith D K
authors.initials: KD
authors.lastname: Luk
authors.affiliation: Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, the University of Hong Kong, Hong Kong, China.
authors.forename: Yong
authors.initials: Y
authors.lastname: Hu
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D016880:Anisotropy; D001499:Bayes Theorem; D016022:Case-Control Studies; D002574:Cervical Vertebrae; D056324:Diffusion Tensor Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D012680:Sensitivity and Specificity; D013118:Spinal Cord Diseases / Q000145:classification*; D055009:Spondylosis / Q000145:classification*; D060388:Support Vector Machine
keywords: cervical spondylotic myelopathy; diffusion tensor imaging; eigenvalue; fractional anisotropy; machine learning; spinal cord