Towards a new 3D classification for adolescent idiopathic scoliosis

Spine Deform. 2020 Jun;8(3):387-396. doi: 10.1007/s43390-020-00051-2. Epub 2020 Feb 5.


Study design: Retrospective analysis of consecutive cases.

Objectives: To identify clinically relevant three-dimensional (3D) sub-groups for adolescent idiopathic scoliosis (AIS). Classifications for AIS are developed to assist surgeons in surgical planning and therapeutic management. However, current systems are based on two-dimensional (2D) parameters that do not completely describe the 3D deformity. Hence, variations in surgical results based on pre-operative 2D classifications may be attributed to the lack of 3D description.

Methods: Subjects from a multicenter database of AIS patients were included in this study. All patients had bi-planar radiographs and 3D reconstruction of the entire spine. A clustering algorithm based on fuzzy c-means was utilized to identify sub-groups based on the following ten parameters measured on 3D reconstructions of the spine: Cobb angle, orientation of the plane of maximum curvature of the proximal thoracic, mid-thoracic (MT) and thoracolumbar (TLL) levels, axial rotation of the apical vertebra of the MT and TLL segments, T4-T12 thoracic kyphosis, and L1-S1 lumbar lordosis. Da Vinci views were also generated and analyzed for each patient in the study. A panel of four experienced spine surgeons from the SRS 3D Scoliosis Committee reviewed and evaluated each group to determine if cluster groups were clinically distinct from each other.

Results: The clustering algorithm was able to detect 11 sub-groups. The population size for each cluster varied from 11 to 290. Statistically significant differences were seen between the parameters for each group. Four spine surgeons reviewed the three most representative cases of each group and unanimously agreed that each cluster group represents a sub-group that was not defined in current classifications.

Conclusions: This study presents a new method of classifying AIS based on a fuzzy clustering algorithm using parameters describing the 3D characteristics of the deformity. Further clinical validation is needed to confirm the usefulness of this classification system.

Level of evidence: IV.

Keywords: 3D; Adolescent idiopathic scoliosis; Cluster analysis; Machine learning; Morphology; Spine.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Algorithms*
  • Cluster Analysis
  • Female
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Imaging, Three-Dimensional / methods*
  • Lumbar Vertebrae / diagnostic imaging
  • Lumbar Vertebrae / pathology
  • Male
  • Radiography*
  • Retrospective Studies
  • Scoliosis / classification*
  • Scoliosis / diagnostic imaging*
  • Scoliosis / pathology
  • Scoliosis / surgery
  • Thoracic Vertebrae / diagnostic imaging
  • Thoracic Vertebrae / pathology