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Three and Four-Dimensional Visualization and Analysis Approaches to Study Vertebrate Axial Elongation and Segmentation
Published on: February 28, 2021
Seeing the spine in 3D: how will it change what we do?
Hubert Labelle1, Carl-Eric Aubin, Roger Jackson
1Division of Orthopaedic Surgery, CHU Sainte-Justine, University of Montreal, Montreal. hubert.labelle@umontreal.ca
Journal of Pediatric Orthopedics
|December 22, 2010
Summary
Three-dimensional (3D) analysis of adolescent idiopathic scoliosis (AIS) shows potential for a new classification system. This advanced approach may improve understanding and treatment of scoliosis curve patterns.
Area of Science:
- Orthopedics
- Biomedical Engineering
- Radiology
Background:
- The Scoliosis Research Society (SRS) committee is evaluating 3D analysis for scoliotic deformities.
- Developing a 3D classification for adolescent idiopathic scoliosis (AIS) is a key objective.
- This work aims to demonstrate how 3D analysis can transform scoliosis assessment and treatment.
Purpose of the Study:
- To summarize recent advancements in 3D analysis of AIS by the SRS committee.
- To present the potential of 3D analysis to revolutionize scoliosis diagnosis and management.
Main Methods:
- Established a database of 600 3D spinal reconstructions from AIS patients using calibrated radiographs (EOS system or digital).
- Utilized dedicated software for 3D reconstructions and analyzed them with the 'da Vinci' view.
- Employed fuzzy clustering and ISO Data cluster analysis on 3D data, including End-Apex-End vertebrae planes.
Main Results:
- Fuzzy clustering segmented 409 AIS cases into 5 distinct curve patterns, similar to existing classifications.
- 3D reconstructions revealed consistent loss of kyphosis in the apical vertebrae of thoracic curves.
- Analysis of 172 Lenke 1 curves identified 2 statistically different subtypes based on End-Apex-End vertebrae planes.
Conclusions:
- A valid and clinically useful 3D classification system for AIS appears achievable.
- 3D analysis offers improved comprehension of AIS curve types.
- Automated 3D classification can potentially reduce the variability inherent in current 2D methods.
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