Computer-aided Lenke Classification of Scoliotic Spines
The identification and classification of the spine deformity play an important role when considering surgical planning for adolescent patients with idiopathic scoliosis. The subject of this article is the Lenke classification of scoliotic spines using Cobb angle measurements. The purpose is two-fold: (1) design a rulebased diagram to assist clinicians in the classification process and (2) investigate a computer classifier which improves the classification time and accuracy. The rule-based diagram efficiency was evaluated in a series of scoliotic classifications by 10 clinicians. The computer classifier was tested on a radiographic measurement database of 603 patients. Classification accuracy was 93% using the rule-based diagram and 99% for the computer classifier. Both the computer classifier and the rule based diagram can efficiently assist clinicians in their Lenke classification of spine scoliosis.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1331271Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1294
 R. J. Cummings, E. A. Loveless, J. Campbell J, S. Samelson S, and J. M. Mazur. Interobserver reliability and intraobserver reproducibility of the system of King et al. for the classification of adolescent idiopathic scoliosis. The Journal of Bone and Joint Surgery, (80):1107-1111, 1998.
 L. Duong, F. Cheriet, and H. Labelle. Three-dimensional classification of spinal deformities using fuzzy clustering. Spine, 31(8):923-930, 2006.
 M.T. Nooritawati A. Hussain, A. S. Salina, I. A. Khairul, and A. H. Rosmawati. Feature selection for classification using decision tree. In 4th Student Conference on Research and Development, pages 99-102, 2006.
 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, 65(9):1302-1313, 1983.
 L. G. Lenke, R. R. Betz, K. H. Bridwell, D. H. Clements, J. Harms, T. G. Lowe, and H. L. Shufflebarger. Intraobserver and interobserver reliability of the classification of thoracic adolescent idiopathic scoliosis. The Journal of Bone and Joint Surgery (American), (80):1097-1106, 1998.
 L. G. Lenke, R. R. Betz, J. Harms, K. H. Bridwell, D. H. Clements, T. G. Lowe, and K. Blanke. Adolescent idiopathic scoliosis: a new classification to determine extent of spinal arthrodesis. The Journal of Bone and Joint Surgery (American), (83):1169-1181, 2001.
 H. Lin. Identification of spinal deformity classification with total curvature analysis and artificial neural network. IEEE Transactions on Biomedical Engineering, 55(1):376-382, 2008.
 P. Poncet, J. Dansereau, and H. Labelle. Geometric torsion in idiopathic scoliosis: three-dimensional analysis and proposal for a new classification. Spine, 26(20):2235-2243, 2001.
 J. W Roach. Adolescent idiopathic scoliosis. Orthopedic Clinics of. North America, 30:353-365, 1999.
 M. K. Shindle, A. J. Khanna, R. Bhatnagar, and P. D. Sponseller. Adolescent idiopathic scoliosis: modern management guidelines. Journal of surgical orthopaedic advances, (15):43-52, 2006.
 A. F. Stokes and D. D. Aronsson. Computer-assisted algorithms improve reliability of King classification and Cobb angle measurement of scoliosis. Spine, 31(6):665-670, 2006.
 A. F. Stokes and D. D. Aronsson. Identifying sources of variability in scoliosis classification using a rule-based automated algorithm. Spine, 27(24):2801-2805, 2006.