A support vector machines classifier to assess the severity of idiopathic scoliosis from surface topography.
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.
Ramirez
Lino
L
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada. l.ramirez@ieee.org
Durdle
Nelson G
NG
Raso
V James
VJ
Hill
Doug L
DL
D000293:Adolescent; D000465:Algorithms; D001185:Artificial Intelligence; D002648:Child; D016000:Cluster Analysis; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods; D007834:Lasers; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D010808:Physical Examination / Q000379:methods*; D015203:Reproducibility of Results; D012600:Scoliosis / Q000473:pathology*; D012680:Sensitivity and Specificity