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Classification of objective aesthetic parameters in idiopathic scoliosis
Scoliosis volume 4, Article number: O17 (2009)
To use non-invasive surface instruments to define Objective Aesthetic Parameters and to obtain an Aesthetic Classification System in idiopathic Scoliosis.
Aesthetic correction is a main aim in scoliosis treatment as defined by SOSORT. Objectively defining aesthetic parameters is difficult; recently TRACE has been proposed as a possible option, being the standardization of the subjective medical expert evaluation. Objective measures are needed for an automatic classification of aesthetics in scoliosis and for other severe back deformities. A 3D reconstruction of the back surface delivers a set of objective parameters used as a basis for this aesthetic classification.
The Formetric measurement system reconstructs human back surfaces in semi-real time. From the acquiesced 3D data a set of objective anatomical and aesthetical parameters, one can automatically calculate:
shoulder rotation, slope angle and height difference
symmetry of flanks and waist triangles
rib humps and asymmetry in waist region
The above parameters form a basis from which classification indexes can be calculated and derived according to a specific set of expert rules.
Especially in severe cases it is possible to transfer the essentials of relative subjective classifications to objective measures and parameters. Vague and hard-to-catch impressions like "it doesn't look good" can to a certain degree be judged by neutral criteria: Shoulder and pelvis symmetry; angles, area- and height-differences of scapula; differences in waist triangles, etc.
The visual impression of aesthetics correlates closely to the objective parameters described above.
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Diers, H., Mooshake, S., Heitmann, K. et al. Classification of objective aesthetic parameters in idiopathic scoliosis. Scoliosis 4, O17 (2009). https://doi.org/10.1186/1748-7161-4-S2-O17
- Idiopathic Scoliosis
- Back Surface
- Objective Parameter
- Automatic Classification
- Classification Index