Genetic algorithm-neural network estimation of cobb angle from torso asymmetry in scoliosis.

@article{Jaremko2002GeneticAN,
  title={Genetic algorithm-neural network estimation of cobb angle from torso asymmetry in scoliosis.},
  author={Jacob L. Jaremko and Philippe Poncet and J. Ronsky and James Harder and Jean Dansereau and Hubert Labelle and Ronald F. Zernicke},
  journal={Journal of biomechanical engineering},
  year={2002},
  volume={124 5},
  pages={496-503}
}
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.