Why the study?
Can an artificial neural network using a genetic algorithm accurately estimate the Cobb angle from torso surface asymmetry in scoliosis patients?
Can an artificial neural network using a genetic algorithm accurately estimate the Cobb angle from torso surface asymmetry in scoliosis patients?
A genetic algorithm-neural network can estimate scoliosis severity (Cobb angle) from torso asymmetry without X-rays with reasonable accuracy.
May support radiation-free scoliosis monitoring; hypothesis-generating and requires prospective validation before clinical use.
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.
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Jaremko et al. (2002) studied this question.
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