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 in two-thirds, and within 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.
Genetic Algorithm–Neural Network Estimation of Cobb Angle from Torso Asymmetry in Scoliosis
Contributed by the Bioengineering Division for publication in the JOURNAL OF BIOMECHANICAL ENGINEERING. Manuscript received July 2001; revised manuscript received, June 2002. Associate Editor: C. L. Vaughan.
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Jaremko, J. L., Poncet , P., Ronsky , J., Harder, J., Dansereau, J., Labelle, H., and Zernicke, R. F. (September 30, 2002). "Genetic Algorithm–Neural Network Estimation of Cobb Angle from Torso Asymmetry in Scoliosis ." ASME. J Biomech Eng. October 2002; 124(5): 496–503. https://doi.org/10.1115/1.1503375
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