Genetic algorithm-neural network estimation of cobb angle from torso asymmetry in scoliosis.
Scoliosis severity, measured by the Cobb angle, was estimated by artificial neural network from indices of torso surface asymmetry using a genetic algorithm to select the optimal set of input torso indices. Estimates of the Cobb angle were accurate within 5 degrees in two-thirds, and within 10 degrees in six-sevenths, of a test set of 115 scans of 48 scoliosis patients, showing promise for future longitudinal studies to detect scoliosis progression without use of X-rays.
Jaremko
Jacob L
JL
Dept. of Surgery, Faculty of Medicine, University of Calgary, AB, Canada. jljaremk@ucalgary.ca
Poncet
Philippe
P
Ronsky
Janet
J
Harder
James
J
Dansereau
Jean
J
Labelle
Hubert
H
Zernicke
Ronald F
RF
D000005:Abdomen / Q000033:anatomy & histology; D000293:Adolescent; D000328:Adult; D000465:Algorithms; D019412:Anatomy, Cross-Sectional / Q000379:methods; D001415:Back / Q000033:anatomy & histology; D002648:Child; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008297:Male; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011859:Radiography; D015203:Reproducibility of Results; D012600:Scoliosis / Q000175:diagnosis* / Q000000981:diagnostic imaging; D013909:Thorax / Q000033:anatomy & histology