Prediction of scoliosis curve type based on the analysis of trunk surface topography

DOIResolve DOI: http://doi.org/10.1109/ISBI.2010.5490322
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TypeArticle
Proceedings title2010 IEEE International Symposium on Biomedical Imaging: from Nano to Macro
Conference2010 IEEE International Symposium on Biomedical Imaging: from Nano to Macro, 2010-04-14 - 2010-04-17, Rotterdam, Netherlands
ISBN978-1-4244-4125-9
Pages408411
Subjectpattern classification Scoliosis Surface topography
AbstractScoliosis treatment strategy is generally chosen according to the severity and type of the spinal curve. Currently, the curve type is determined from X-rays whose acquisition can be harmful for the patient. We propose in this paper a system that can predict the scoliosis curve type based on the analysis of the surface of the trunk. The latter is acquired and reconstructed in 3D using a non invasive multi-head digitizing system. The deformity is described by the back surface rotation, measured on several cross-sections of the trunk. A classifier composed of three support vector machines was trained and tested using the data of 97 patients with scoliosis. A prediction rate of 72.2% was obtained, showing that the use of the trunk surface for a high-level scoliosis classification is feasible and promising.
Publication date
PublisherIEEE
LanguageEnglish
AffiliationNational Research Council Canada; Information and Communication Technologies
Peer reviewedYes
NRC publication
This is a non-NRC publication

"Non-NRC publications" are publications authored by NRC employees prior to their employment by NRC.

NPARC number23001009
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Record identifier21f4e532-da9a-4d82-94a3-8c15eeb344b8
Record created2016-11-28
Record modified2016-11-28
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