Type of Document Master's Thesis Author Entrekin, Dean Allen Author's Email Address email@example.com URN etd-05262004-144020 Title On the Geometric Characterization of the Lenke Classification Scheme for Idiopathic Scoliosis Degree Master of Science Department Biomedical Engineering and Sciences Advisory Committee
Advisor Name Title Dankowicz, Harry J. Committee Chair Madigan, Michael L. Committee Member Shilt, Jeff Committee Member Keywords
- three dimensional spinal deformity
- Lenke Classification
Date of Defense 2004-05-21 Availability unrestricted AbstractCurrent methods for treating and diagnosing spinal deformities caused by scoliosis are both surgically intensive and rarely allow for complete correction. This is mainly due to the fact that the diagnostic techniques used are rough estimates made by angles defined by observations of 2-D radiographs. By utilizing the latest software, our research is based on designing a tool that creates a 3-D representation of the spine. When creating a three-dimensional spinal model, it becomes possible to determine local curvature and local torsion values at each specific vertebrae. By manipulating these values at discrete locations on the spine, one can generate "virtual" spines in a three-dimensional environment.
The Scoliosis Learning Tool includes algorithmic steps that determine the Lenke Classification of the "virtual" spines. The Lenke Classification is the most commonly accepted method for diagnosing spinal deformities.
This patient building program will produce a group of spines with random values for curvature, torsion and initial spinal orientation. An algorithm within the software determines the Lenke Classification of each, and discards any curves that appear unnatural. By defining a metric that places an emphasis on certain geometric similarities, the software is able to define diameters of classification groups and separations between different classification groups. In turn it is possible to determine minor to major differences between spines within the same classification. In doing so, the opportunity exists to possibly find an undiscovered deformity that had previously fallen under another classification category.
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