Analysis of three-dimensional (3D) images of human torsos for torso deformities such as scoliosis requires classifying torso distortion. Assessing torso distortion from 3D images is not trivial as actual torsos are non-symmetric and show an outstanding range of variations leading to high classification errors. As the degree of spinal deformity (and classification of torso shape) influences scoliosis treatment options, the development of more accurate classification procedures is desirable. This paper presents a technique for assessing torso shape and classifying scoliosis into mild, moderate and severe categories using two indices, 'twist' and 'bend', obtained from orthogonally transformed images of the complete torso surface called orthogonal maps. Four transforms (axial line, unfolded cylinder, enclosing cylinder and subtracting cylinder) were used. Blind tests on 361 computer models with known deformation parameter values show 100% classification accuracy. Tests on eight volunteers without scoliosis validated the system and tests on 22 torso images of volunteers with scoliosis showed up to 95.5% classification accuracy. In addition to classifying scoliosis, orthogonal maps present the entire torso in one view and are viable for use in scoliosis clinics for monitoring the progression of scoliosis.

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A support vector classifier (SVC) approach was employed in predicting the risk of progression of adolescent idiopathic scoliosis (AIS), a condition that causes visible trunk asymmetries. As the aetiology of AIS is unknown, its risk of progression can only be predicted from measured indicators. Previous studies suggest that individual indicators of AIS do not reliably predict its risk of progression. Complex indicators with better predictive values have been developed but are unsuitable for clinical use as obtaining their values is often onerous, involving much skill and repeated measurements taken over time. Based on the hypothesis that combining common indicators of AIS using an SVC approach would produce better prediction results more quickly, we conducted a study using three datasets comprising a total of 44 moderate AIS patients (30 observed, 14 treated with brace). Of the 44 patients, 13 progressed less than 5 degrees and 31 progressed more than 5 degrees. One dataset comprised all the patients. A second dataset comprised all the observed patients and a third comprised all the brace-treated patients. Twenty-one radiographic and clinical indicators were obtained for each patient. The result of testing on the three datasets showed that the system achieved 100% accuracy in training and 65%-80% accuracy in testing. It outperformed a "statistically equivalent" logistic regression model and a stepwise linear regression model on the said datasets. It took less than 20 min per patient to measure the indicators, input their values into the system, and produce the needed results, making the system viable for use in a clinical environment.

This paper describes a method of characterizing the torso shape deformity associated with scoliosis by both its type and severity. This problem is challenging because regular human torsos show an astounding range of variations that is only compounded by scoliosis, and it is difficult to isolate natural shape variations from those caused by scoliosis. Torso shape characterization is important in the clinical management of scoliosis because torso aesthetics is a key concern that influences a patient's quality of life. Our method involves modeling 3-D torso range images into structured sequences of 3-D spline curves stacked along the spine. We obtain local shape measures from points of maximal curvature (dominant points) along each torso cross section by evaluating the relative symmetry of the spline curve at that cross section. This results in a scalable characterization scheme for torso deformity type and a measure of torso deformity severity. We assess the accuracy and precision of this shape characterization scheme, and its relationship to the actual deformities present in the underlying spine.

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