Prediction of Scoliosis Progression in Time Series Using a Hybrid Learning Technique

Scoliosis is a common and poorly understood spinal disorder that is clinically monitored with a series of full spinal X-rays. The purpose of this study was to predict scoliosis future progression at 6- and 12-month intervals with successive spinal indices and a hybrid learning technique (i.e., the combination of fuzzy c-means clustering and artificial neural network (ANN)). Ultimately this could decrease scoliotic patients´ radiation exposure and the associated cancer risk in growing adolescents. Seventy-two data sets were derived from a database of 56 acquisitions from 11 subjects (29.8 plusmn 9.6deg Cobb angle, 11.4 plusmn 2.4 yr), each consisting of 4 sequential values of Cobb angle and lateral deviations at apices in 6- and 12-month intervals in the coronal plane. Progression patterns in Cobb angles (n = 10) and lateral deviations (n = 8) were successfully identified using a fuzzy c-means clustering algorithm. The accuracies of the trained ANN, having a structure of three input variables, four nonlinear hidden nodes, and one linear output variable, for training and test data sets were within 3.64deg (plusmn 2.58deg) and 4.40deg (plusmn 1.86deg) of Cobb angles, and within 3.59 (plusmn 3.96) mm and 3.98 (plusmn 3.41) mm of lateral deviations, respectively. Those results were twice the accuracy of typical clinical measurement (~10deg) and in close agreement with those using cubic spline extrapolation and adaptive neuro-fuzzy inference system (ANFIS) techniques. The adapted technique for predicting the scoliosis deformity progression holds significant promise for clinical applications