Innovative decision support for scoliosis brace therapy based on statistical modelling of markerless 3D trunk surface data

Abstract

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.