Three-dimensional morphology study of surgical adolescent idiopathic scoliosis patient from encoded geometric models
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The classification of three-dimensional (3D) spinal deformities remains an open question in adolescent idiopathic scoliosis. Recent studies have investigated pattern classification based on explicit clinical parameters. An emerging trend however seeks to simplify complex spine geometries and capture the predominant modes of variability of the deformation. The objective of this study is to perform a 3D characterization and morphology analysis of the thoracic and thoraco/lumbar scoliotic spines (cross-sectional study). The presence of subgroups within all Lenke types will be investigated by analyzing a simplified representation of the geometric 3D reconstruction of a patient’s spine, and to establish the basis for a new classification approach based on a machine learning algorithm.
Three-dimensional reconstructions of coronal and sagittal standing radiographs of 663 patients, for a total of 915 visits, covering all types of deformities in adolescent idiopathic scoliosis (single, double and triple curves) and reviewed by the 3D Classification Committee of the Scoliosis Research Society, were analyzed using a machine learning algorithm based on stacked auto-encoders. The codes produced for each 3D reconstruction would be then grouped together using an unsupervised clustering method. For each identified cluster, Cobb angle and orientation of the plane of maximum curvature in the thoracic and lumbar curves, axial rotation of the apical vertebrae, kyphosis (T4–T12), lordosis (L1–S1) and pelvic incidence were obtained. No assumptions were made regarding grouping tendencies in the data nor were the number of clusters predefined.
Eleven groups were revealed from the 915 visits, wherein the location of the main curve, kyphosis and lordosis were the three major discriminating factors with slight overlap between groups. Two main groups emerge among the eleven different clusters of patients: a first with small thoracic deformities and large lumbar deformities, while the other with large thoracic deformities and small lumbar curvature. The main factor that allowed identifying eleven distinct subgroups within the surgical patients (major curves) from Lenke type-1 to type-6 curves, was the location of the apical vertebra as identified by the planes of maximum curvature obtained in both thoracic and thoraco/lumbar segments. Both hypokyphotic and hyperkypothic clusters were primarily composed of Lenke 1–4 curve type patients, while a hyperlordotic cluster was composed of Lenke 5 and 6 curve type patients.
The stacked auto-encoder analysis technique helped to simplify the complex nature of 3D spine models, while preserving the intrinsic properties that are typically measured with explicit parameters derived from the 3D reconstruction.
KeywordsAdolescent idiopathic scoliosis Spine Machine learning Morphology Cluster analysis
This paper was supported in part by the CHU Sainte-Justine Academic Research Chair in Spinal Deformities, the Canada Research Chair in Medical Imaging and Assisted Interventions, the 3D committee of the Scoliosis Research Society, the Natural Sciences and Engineering Research Council of Canada and the MEDITIS training program.
Compliance with ethical standards
Conflict of interest
The authors have no potential conflict of interest.
- 1.Ponseti IV, Friedman B (1950) Prognosis in idiopathic scoliosis. J Bone Joint Surg Am 32(2):381–395Google Scholar
- 12.van der Maaten LJ, Postma EO, van den Herik HJ (2009) Dimensionality reduction: a comparative review. J Mach Learn Res 10(1–41):66–71Google Scholar
- 15.Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol PA (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11:3371–3408Google Scholar
- 16.Vincent P, Larochelle H, Bengio Y, Manzagol PA (2008) Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th international conference on machine learning. ACM, pp 1096–1103Google Scholar
- 17.Arthur D, Vassilvitskii S (2007) k-means++: the advantages of careful seeding. In: Proceedings of the eighteenth annual ACM-SIAM symposium on discrete algorithms. Society for Industrial and Applied Mathematics, pp 1027–1035Google Scholar
- 18.Ray S, Turi RH (1999) Determination of number of clusters in k-means clustering and application in colour image segmentation. In: Proceedings of the 4th international conference on advances in pattern recognition and digital techniques, pp 137–143Google Scholar
- 23.Keenan BE, Izatt MT, Askin GN, Labrom RD, Pearcy MJ, Adam CJ (2014) Supine to standing Cobb angle change in idiopathic scoliosis: the effect of endplate pre-selection. Scoliosis. doi: 10.1186/1748-7161-9-16