Noninvasive clinical assessment of trunk deformities associated with scoliosis

From National Research Council Canada

DOI	Resolve DOI: https://doi.org/10.1109/TITB.2012.2222425
Author	Search for: Seoud, L.; Search for: Dansereau, J.; Search for: Labelle, H.; Search for: Cheriet, F.
Format	Text, Article
Subject	shape analysis; Clustering; functional data analysis; scoliosis
Abstract	Besides the spinal deformity, scoliosis modifies notably the general appearance of the trunk resulting in trunk rotation, imbalance, and asymmetries that constitutes patients' major concern. Existing classifications of scoliosis, based on the type of spinal curve as depicted on radiographs, are currently used to guide treatment strategies. Unfortunately, even though a perfect correction of the spinal curve is achieved, some trunk deformities remain, making patients dissatisfied with the treatment received. The purpose of this study is to identify possible shape patterns of trunk surface deformity associated with scoliosis. First, trunk surface is represented by a multivariate functional trunk shape descriptor based on 3-D clinical measurements computed on cross sections of the trunk. Then, the classical formulation of hierarchical clustering is adapted to the case of multivariate functional data and applied to a set of 236 trunk surface 3-D reconstructions. The highest internal validity is obtained when considering 11 clusters that explain up to 65% of the variance in our dataset. Our clustering result shows a concordance with the radiographic classification of spinal curves in 68% of the cases. As opposed to radiographic evaluation, the trunk descriptor is 3-D and its functional nature offers a compact and elegant description of not only the type, but also the severity and extent of the trunk surface deformity along the trunk length. In future work, new management strategies based on the resulting trunk shape patterns could be thought of in order to improve the esthetic outcome after treatment, and thus patients satisfaction.
Publication date	2013-03-07
Publisher	Institute of Electrical and Electronics Engineers
In	IEEE Journal of Biomedical and Health Informatics 17, no. 2: 392–401.
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	23000993
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Record identifier	21ac41c8-ed61-40c9-b2a2-ef926d9d5e75
Record created	2016-11-25
Record modified	2020-04-22

Date modified:: 2024-12-01