Application of Artificial Neural Networks in Prediction of Human Mandible Geometry

Accurate predictive models of human mandible are the most important component in maxillofacial reconstruction surgery when medical images of the whole bone are not available (due to the bone illness, fracture or an extreme traumatic bone damage, osteoporosis). Existing predictive models are based on predicting the intermediate form of the model including all of its variations based on the input data set, but the shortcomings are inaccurate prediction of shape variations and geometry of the bone outside of the input set. In this paper, the method for building predictive 3D model of the human mandible, which geometry can be changed according to the specific patient’s bone, is presented. The method is based on the Method of Anatomical Features, with the implementation of the important improvement, with the application of artificial neural networks in the prediction of human mandible geometry. This approach enables easy personalization of the model’s shape and geometry and can serve as a template for individual treatment planning.