A Computer-aided Method for Improving the Reliability of Lenke Classification for Scoliosis

Zhang, Junhua; Li, Hongjian; Lv, Liang; Shi, Xinling; Guo, Fei; Zhang, Yufeng

doi:https://doi.org/10.1260/2040-2295.6.2.145

Journal of Healthcare Engineering

On this page

Abstract References Copyright Related Articles

Research Article | Open Access

Volume 6 | Article ID 369621 | https://doi.org/10.1260/2040-2295.6.2.145

A Computer-aided Method for Improving the Reliability of Lenke Classification for Scoliosis

Junhua Zhang,¹Hongjian Li,²Liang Lv,³Xinling Shi,¹Fei Guo,³and Yufeng Zhang¹

Received01 Sept 2014

Accepted01 Mar 2015

Abstract

Classification of the spinal curve pattern is crucial for assessment and treatment of scoliosis. We developed a computer-aided system to improve the reliability of three components of the Lenke classification. The system semi-automatically measured the Cobb angles and identified the apical lumbar vertebra and its pedicles on digitized radiographs. The system then classified the curve type, lumbar modifier, and thoracic sagittal modifier of the Lenke classification based on the computerized measurements and identifications. The system was tested by five operators for 62 scoliotic cases. The kappa statistic was used to assess the reliability. With the aid of computer, the average intra- and interobserver kappa values were improved to 0.89 and 0.81 for the curve type, to 0.83 and 0.81 for the lumbar modifier, and to 0.94 and 0.92 for the sagittal modifier of the Lenke classification, respectively, relative to the classification by two of the operators without the aid of computer. Results indicate that the computerized system can improve reliability for all three components of the Lenke classification.

References

I. A. F. Stokes, “Three-dimensional terminology of spinal deformity. A report presented to the Scoliosis Research Society by the Scoliosis Research Society Working Group on 3-D terminology of spinal deformity,” Spine, vol. 19, no. 2, pp. 236–248, 1994.
View at: Google Scholar
L. G. Lenke, R. R. Betz, J. Harms et al., “Adolescent idiopathic scoliosis: a new classification to determine extent of spinal arthrodesis,” The Journal of Bone and Joint Surgery - American Volume, vol. 83-A, no. 8, pp. 1169–1181, 2001.
View at: Google Scholar
H. A. King, J. H. Moe, D. S. Bradford, and R. B. Winter, “The selection of fusion levels in thoracic idiopathic scoliosis,” The Journal of Bone and Joint Surgery - American Volume, vol. 65, no. 9, pp. 1302–1313, 1983.
View at: Google Scholar
M. Ogon, K. Giesinger, H. Behensky et al., “Interobserver and intraobserver reliability of Lenke’s new scoliosis classification system,” Spine, vol. 27, no. 8, pp. 858–863, 2002.
View at: Google Scholar
B. S. Richards, D. J. Sucato, D. E. Konigsberg, and J. A. Ouellet, “Comparison of reliability between the Lenke and King classification systems for adolescent idiopathic scoliosis using radiographs that were not premeasured,” Spine, vol. 28, no. 11, pp. 1148–1157, 2003.
View at: Google Scholar
P. Phan, N. Mezghani, C. E. Aubin, J. de Guise, and H. Labelle, “Computer algorithms and applications used to assist the evaluation and treatment of adolescent idiopathic scoliosis: a review of published articles 2000-2009,” Eur Spine J, vol. 20, no. 7, pp. 1058–1068, 2011.
View at: Google Scholar
I. A. F. Stokes and D. D. Aronsson, “Computer-assisted algorithms improve reliability of King classification and Cobb angle measurement of scoliosis,” Spine, vol. 31, no. 6, pp. 665–670, 2006.
View at: Google Scholar
I. A. F. Stokes and D. D. Aronsson, “Rule-based algorithm for automated King-type classification of idiopathic scoliosis,” Stud Health Technol Inform, vol. 88, pp. 149–152, 2002.
View at: Google Scholar
I. A. F. Stokes and D. D. Aronsson, “Identifying sources of variability in scoliosis classification using a rule-based automated algorithm,” Spine, vol. 27, no. 24, pp. 2801–2805, 2002.
View at: Google Scholar
P. Phan, N. Mezghani, E. K. Wai, J. de Guise, and H. Labelle, “Artificial neural networks assessing adolescent idiopathic scoliosis comparison with Lenke classification,” The Spine Journal, vol. 13, no. 11, pp. 1527–1533, 2013.
View at: Google Scholar
N. Mezghani, R. Chav, L. Humbert, S. Parent, W. Skalli, and J. de Guise, “A computer-based classifier of three-dimensional spinal scoliosis severity,” Int J Comput Assist Radiol Surg, vol. 3, no. 1, pp. 55–60, 2008.
View at: Google Scholar
P. Poncet, J. Dansereau, and H. Labelle, “Geometric Torsion in Idiopathic Scoliosis: Three-Dimensional Analysis and Proposal for a New Classification,” Spine, vol. 26, no. 20, pp. 2235–2243, 2001.
View at: Google Scholar
P. Phan, N. Mezghani, M. L. Nault et al., “A decision tree can increase accuracy when assessing curve types according to Lenke classification of adolescent idiopathic scoliosis,” Spine, vol. 35, no. 10, pp. 1054–1059, 2010.
View at: Google Scholar
N. Mezghani, P. Phan, H. Labelle, C. E. Aubin, and J. de Guise, “Computer-aided Lenke classification of scoliotic spines. World Academy of Science,” Engineering and Technology, vol. 3, no. 5, pp. 646–649, 2009.
View at: Google Scholar
J. Zhang, X. Shi, L. Lv, X. Wang, Y. Zhang, and F. Guo, “Computerized Lenke classification of scoliotic spine,” in Conf Proc IEEE Eng Med Biol Soc, pp. 945–948, 2013.
View at: Google Scholar
L. Duong, F. Cheriet, H. Labelle et al., “Interobserver and intraobserver variability in the identification of the Lenke classification lumbar modifier in adolescent idiopathic scoliosis,” J Spinal Disorders & Techniques, vol. 22, no. 6, pp. 448–455.
View at: Google Scholar
L. Duong, F. Cheriet, and H. Labelle, “Three-Dimensional Classification of Spinal Deformities Using Fuzzy Clustering,” Spine, vol. 31, no. 8, pp. 923–930, 2006.
View at: Google Scholar
H. Lin, “Identification of spinal deformity classification with total curvature analysis and artificial neural network,” IEEE Transactions on Biomedical Engineering, vol. 55, no. 1, pp. 376–382, 2008.
View at: Google Scholar
H. Lin and D. Sucato, “Identification of Lenke spine deformity classification by simplified 3D spine model,” in Conf Proc IEEE Eng Med Biol Soc, vol. 5, pp. 3144–3146, 2004.
View at: Google Scholar
A. P. Sangole, C. E. Aubin, H. Labelle et al., “Three-dimensional classification of thoracic scoliotic curves,” Spine, vol. 34, no. 1, pp. 91–99.
View at: Google Scholar
I. A. F. Stokes, A. P. Sangole, and C. E. Aubin, “Classification of scoliosis deformity three-dimensional spinal shape by cluster analysis,” Spine, vol. 34, no. 6, pp. 584–590, 2009.
View at: Google Scholar
J. Zhang, E. Lou, X. Shi et al., “Computer-aided assessment of scoliosis on posteroanterior radiographs,” Medical & Biological Engineering & Computing, vol. 48, no. 2, pp. 185–195, 2010.
View at: Google Scholar
J. Canny, “A computational approach to edge detection,” IEEE Trans Pattern Anal Mach Intell, vol. 8, no. 6, pp. 679–714, 1986.
View at: Google Scholar
J. R. Landis and G. G. Koch, “The measurement of observer agreement for categorical data,” Biometrics, vol. 33, no. 1, pp. 159–174, 1977.
View at: Google Scholar

Copyright

Copyright © 2015 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation Order printed copies

Views

823

Downloads

1034

Citations