Innovative decision support for scoliosis brace therapy based on statistical modelling of markerless 3D trunk surface data
| Autor: | Stephan Rothstock, Daniel Krueger, Victoria Kleban, Lothar Paul, Hans-Rudolf Weiss |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Male
Adolescent 0206 medical engineering Biomedical Engineering Bioengineering 02 engineering and technology Scoliosis 03 medical and health sciences Automation 0302 clinical medicine Lumbar Imaging Three-Dimensional medicine Humans Child Probability Orthodontics Braces Models Statistical Cobb angle business.industry Torso Statistical model 030229 sport sciences General Medicine medicine.disease Decision Support Systems Clinical 020601 biomedical engineering Trunk Brace Computer Science Applications Human-Computer Interaction Data point Female business Distance transform Algorithms |
| Zdroj: | Computer methods in biomechanics and biomedical engineering. 23(13) |
| ISSN: | 1476-8259 |
| Popis: | Recently markerless 3D scanning methods receive an increased interest for therapy planning and brace treatment of patients with scoliosis. This avoids repeated radiation known from standard X-Ray analysis. Several authors introduced the method of asymmetry distance maps in order to classify curve severity and progression. The current work extends this approach by statistical mean shape 3D models of the human trunk in order to classify patients. 50 patients were included in this study performing frontal X-ray and 3D scanning analysis. All patients were classified by a clinician according to their Cobb angle and spinal curve pattern (Augmented-Lehnert-Schroth ALS). 3D reconstructions of each patient trunk were processed in a way to elastically register a reference surface mesh with fixed number of data points. Mean 3D shape models were generated for each curve pattern. An asymmetry distance map was then calculated for each patient and mean shape model. Single patient 3D reconstructions were classified according to severity and ALS treatment group. Optimal sensitivity and specificity was 97%/39% thoracic and 87%/42% lumbar respectively for detecting mild and moderate-severe patients. Identifying a treatment group was possible for three combined groups allowing to support decisions during diagnosis and therapy planning. |
| Databáze: | OpenAIRE |
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