Correlation of Vertebral Absolute Axial Rotations in CAD 3D Models of Adolescent Idiopathic Scoliosis NonInvasively Diagnosed This paper presents preliminary results on noninvasive optical diagnosis of patient with adolescent idiopathic scoliosis (AIS) with recently developed tool ScolioSIM. This tool generates intrinsic and extrinsic indicators of the spinal deformity and 3D CAD (Computeraided Design) deformity model based only on a digitalized dorsal surface of the examined patient. Our primary focus in this paper are absolute axial rotations (AAR) of each vertebra in AIS patients and their correlations. Clinical relevance of this research is high, as we intend to avoid traditional methods which rely on highly complicated reading and measuring of AARs on planar xray films, as these methods involve ionizing radiation and require highly experienced observers. Here we demonstrated a good correlation of AARs in 372 patients optically diagnosed, and currently we perform final verification of presented method and generic CAD model of the spine against 200 lowdose EOS scans of AIS patients.

Pediatric Spine Segmentation and Modeling Using Machine Learning Scoliosis embodies the most frequent threedimensional spinal deformity in children. Only timely treatment during the growth of the spine may significantly reduce related health problems inflicted by the deformity on adults. The results obtained via conservative therapy are problematic, and a certain degree of curvature already requires surgical treatment that at the time of writing consists of repeated spinal surgeries posing a high risk of complications. The aim is to use a spine model for computer based simulation of changes in the stress on the spine during idiopathic and syndromic deformity correction via vertebral osteotomy. A machinelearning toolbox for 3D Slicer has been developed. The toolbox has a form of an application extension. Preprocessing of the data, training and usage of the classifier is possible through a simple and modern graphical user interface. The extension is capable of performing a variety of helpful tasks such as an analysis of the impact of the size of the training vector and feature selection on classifier precision. The results suggest that the training vector size can be minimized for all of the tested classifiers. Furthermore, the random forest classifierxe2x80x99s performance seems to be resistant to training parameter changes. Support vector machine is sensitive to training parameter changes with optimal values concentrated in a narrow feature space.