Estimation of postoperative knee flexion at initial contact of Cerebral Palsy children using neural networks

Abstract : Cerebral Palsy (CP) affects walking and often produces excessive knee flexion at initial contact (KFIC). Hamstring lengthening surgery (HL) is applied to decrease KFIC. The objective of this work is to design a simulator of the effect of HL on KFIC that could be used as a decision-making tool. The postoperative KFIC is estimated given the preoperative gait, physical examination and the type of surgery. Nonlinear data fitting is performed by feedforward neural networks. The mean regression error on test is 9.25° and 63.21% of subjects are estimated within an error range of 10°. The simulator is able to give good estimations independently of the preoperative gait parameters and the type of surgery. This system predicts the outcomes of orthopaedic surgery on CP children with real gait parameters, and not with qualitative characteristics.