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Published in: Medical & Biological Engineering & Computing 11/2010

01-11-2010 | Original Article

Prediction of scoliosis progression with serial three-dimensional spinal curves and the artificial progression surface technique

Authors: Hongfa Wu, Janet L. Ronsky, Farida Cheriet, Jessica Küpper, James Harder, Deyi Xue, Ronald F. Zernicke

Published in: Medical & Biological Engineering & Computing | Issue 11/2010

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Abstract

Adolescent idiopathic scoliosis (AIS) progression is clinically monitored by a series of full spinal X-rays. To decrease radiation exposure, an artificial progression surface (APS) is proposed to predict progression. Fifty-six acquisitions (posteroanterior radiographs, 0° and 20°) were obtained from 11 AIS patients (29.8 ± 9.6° Cobb angle). Three-dimensional curves were constructed through vertebral pedicle centers. Three previous serial spinal curves (6-month intervals) were used to construct an APS with a Non-uniform Rational B-Spline surfacing technique. Future progression was achieved by aligning the curves on the APS using the generalized cross-validation extrapolation technique. With three and four previous serial spinal curves, the prediction accuracies of future progression at the next 6-month interval were 4.1 ± 3.3° for Cobb angles and 3.6 ± 3.5 mm for apex lateral deviations. Apex locations and Cobb regions varied within one vertebral level. The proposed technique shows potential as an accurate three-dimensional prediction method for AIS progression and could help pediatricians make decisions about treatment. However, it could only be applied once before more radiographic data would be needed.

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Metadata
Title
Prediction of scoliosis progression with serial three-dimensional spinal curves and the artificial progression surface technique
Authors
Hongfa Wu
Janet L. Ronsky
Farida Cheriet
Jessica Küpper
James Harder
Deyi Xue
Ronald F. Zernicke
Publication date
01-11-2010
Publisher
Springer-Verlag
Published in
Medical & Biological Engineering & Computing / Issue 11/2010
Print ISSN: 0140-0118
Electronic ISSN: 1741-0444
DOI
https://doi.org/10.1007/s11517-010-0654-6

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