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A support vector machines (SVM) classifier was used to assess the severity of idiopathic scoliosis (IS) based on surface topographic images of human backs. Scoliosis is a condition that involves abnormal lateral curvature and rotation of the spine that usually causes noticeable trunk deformities. Based on the hypothesis that combining surface topography and clinical data using a SVM would produce better assessment results, we conducted a study using a dataset of 111 IS patients. Twelve surface and clinical indicators were obtained for each patient. The result of testing on the dataset showed that the system achieved 69–85% accuracy in testing. It outperformed a linear discriminant function classifier and a decision tree classifier on the dataset. [ABSTRACT FROM AUTHOR]

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° and 31 progressed more than 5°. 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. [ABSTRACT FROM AUTHOR]

Efficiency of brace treatment for adolescent idiopathic scoliosis is correlated to how the brace has been worn. The more often the patients wear their braces to the prescribed tightness as well as to the prescribed length of time each day, the better the treatment outcomes. This paper describes an active intelligent brace system designed to maintain the interface pressure between the brace and body within the prescribed range during daily activity. The intelligent system consists of a microcomputer, a force transducer and a force feedback component. A subject who has no scoliosis volunteered to test the system for a few hours. Preliminary trials indicate that wearing the system increases the amount of time which a subject wears his brace in the prescribed range of tightness. The test subject wore the brace without feed- back within the prescribed range of tightness for 28% of the time, whereas when the air bladder feedback system was activated, the subject wore the brace effectively for 47 % of the time. [ABSTRACT FROM AUTHOR]