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

From National Research Council Canada

DOI	Resolve DOI: https://doi.org/10.1109/ISBI.2010.5490322
Author	Search for: Seoud, L.¹; Search for: Adankon, M. M.; Search for: Labelle, H.; Search for: Dansereau, J.; Search for: Cheriet, F.
Affiliation	National Research Council Canada. Information and Communication Technologies
Format	Text, Article
Conference	2010 IEEE International Symposium on Biomedical Imaging: from Nano to Macro, 2010-04-14 - 2010-04-17, Rotterdam, Netherlands
Subject	pattern classification Scoliosis Surface topography
Abstract	Scoliosis 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	2010-04
Publisher	IEEE
In	2010 IEEE International Symposium on Biomedical Imaging: from Nano to Macro: 408–411.
Language	English
Peer reviewed	Yes
NRC publication	This is a non-NRC publication "Non-NRC publications" are publications authored by NRC employees prior to their employment by NRC.
NPARC number	23001009
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Record identifier	21f4e532-da9a-4d82-94a3-8c15eeb344b8
Record created	2016-11-28
Record modified	2020-04-17

Date modified:: 2025-12-14