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European Spine Journal

, Volume 25, Issue 10, pp 3104–3113 | Cite as

Three-dimensional morphology study of surgical adolescent idiopathic scoliosis patient from encoded geometric models

  • William Thong
  • Stefan Parent
  • James Wu
  • Carl-Eric Aubin
  • Hubert Labelle
  • Samuel KadouryEmail author
Original Article

Abstract

Purpose

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.

Methods

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.

Results

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.

Conclusion

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.

Keywords

Adolescent idiopathic scoliosis Spine Machine learning Morphology Cluster analysis 

Notes

Acknowledgments

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • William Thong
    • 1
    • 2
  • Stefan Parent
    • 2
  • James Wu
    • 2
  • Carl-Eric Aubin
    • 1
    • 2
  • Hubert Labelle
    • 2
  • Samuel Kadoury
    • 1
    • 2
    Email author
  1. 1.Polytechnique MontréalMontrealCanada
  2. 2.Sainte-Justine University Hospital CentreMontrealCanada

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