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Non invasive classification system of scoliosis curve types using least-squares support vector machines.
MedLine Citation:
PMID:  23017984     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
OBJECTIVE: To determine scoliosis curve types using non invasive surface acquisition, without prior knowledge from X-ray data. METHODS: Classification of scoliosis deformities according to curve type is used in the clinical management of scoliotic patients. In this work, we propose a robust system that can determine the scoliosis curve type from non invasive acquisition of the 3D back surface of the patients. The 3D image of the surface of the trunk is divided into patches and local geometric descriptors characterizing the back surface are computed from each patch and constitute the features. We reduce the dimensionality by using principal component analysis and retain 53 components using an overlap criterion combined with the total variance in the observed variables. In this work, a multi-class classifier is built with least-squares support vector machines (LS-SVM). The original LS-SVM formulation was modified by weighting the positive and negative samples differently and a new kernel was designed in order to achieve a robust classifier. The proposed system is validated using data from 165 patients with different scoliosis curve types. The results of our non invasive classification were compared with those obtained by an expert using X-ray images. RESULTS: The average rate of successful classification was computed using a leave-one-out cross-validation procedure. The overall accuracy of the system was 95%. As for the correct classification rates per class, we obtained 96%, 84% and 97% for the thoracic, double major and lumbar/thoracolumbar curve types, respectively. CONCLUSION: This study shows that it is possible to find a relationship between the internal deformity and the back surface deformity in scoliosis with machine learning methods. The proposed system uses non invasive surface acquisition, which is safe for the patient as it involves no radiation. Also, the design of a specific kernel improved classification performance.
Authors:
Mathias M Adankon; Jean Dansereau; Hubert Labelle; Farida Cheriet
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-9-24
Journal Detail:
Title:  Artificial intelligence in medicine     Volume:  -     ISSN:  1873-2860     ISO Abbreviation:  Artif Intell Med     Publication Date:  2012 Sep 
Date Detail:
Created Date:  2012-9-28     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8915031     Medline TA:  Artif Intell Med     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
Copyright © 2012 Elsevier B.V. All rights reserved.
Affiliation:
Ecole Polytechnique de Montreal, University of Montreal, 2900, boul. Edouard-Montpetit, Campus de l'Universite de Montreal, 2500, chemin de Polytechnique, Montreal (Quebec) H3T 1J4, Canada; Sainte-Justine Hospital Research Center, 3175, Chemin de la Cote-Sainte-Catherine, Montreal (Quebec) H3T 1C5, Canada. Electronic address: mathias-mahouzonsou.adankon@polymtl.ca.
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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