Application of an artificial neural network and morphing techniques in the redesign of dysplastic trochlea.

[trochlear dysplasia]

Segmentation and computer assisted design tools have the potential to test the validity of simulated surgical procedures , e .g ., trochleoplasty . A repeatable measurement method for three dimensional femur models that enables quantification of knee parameters of the distal femur is presented . Fifteen healthy knees are analysed using the method to provide a training set for an artificial neural network . The aim is to use this artificial neural network for the prediction of parameter values that describe the shape of a normal trochlear groove geometry . This is achieved by feeding the artificial neural network with the unaffected parameters of a dysplastic knee . Four dysplastic knees (Type A through D ) are virtually redesigned by way of morphing the groove geometries based on the suggested shape from the artificial neural network . Each of the four resulting shapes is analysed and compared to its initial dysplastic shape in terms of three anteroposterior dimensions : lateral , central and medial . For the four knees the trochlear depth is increased , the ventral trochlear prominence reduced and the sulcus angle corrected to within published normal ranges . The results show a lateral facet elevation inadequate , with a sulcus deepening or a depression trochleoplasty more beneficial to correct trochlear dysplasia .