Predictive model for functional consequences of oral cavity tumour

The prediction of functional consequences after treatment of large oral cavity tumours is mainly based on the size and location of the tumour. However, patient specific factors play an important role in the functional outcome, making the current predictions unreliable and subjective. An objective prediction is necessary for better patient oriented care, where the choice between surgery or chemo- and radiotherapy could be made according to more reliable measures. In this project, work is being performed to create a tool to obtain this objective prediction. The aim is to develop a virtual biomechanical patient-specific model of the oral cavity for virtual surgery. By adjusting the model, so as to mimic the performed surgery, an accurate preoperative assessment of the postoperative functional consequences can be made for each individual patient

Predicting 3D lip shapes using facial surface EMG

Aim The aim of this study is to prove that facial surface electromyography (sEMG) conveys sufficient information to predict 3D lip shapes. High sEMG predictive accuracy implies we could train a neural control model for activation of biomechanical models by simultaneously recording sEMG signals and their associated motions. Materials and methods With a stereo camera set-up, we recorded 3D lip shapes and simultaneously performed sEMG measurements of the facial muscles, applying principal component analysis (PCA) and a modified general regression neural network (GRNN) to link the sEMG measurements to 3D lip shapes. To test reproducibility, we conducted our experiment on five volunteers, evaluating several sEMG features and window lengths in unipolar and bipolar configurations in search of the optimal settings for facial sEMG. Conclusions The errors of the two methods were comparable. We managed to predict 3D lip shapes with a mean accuracy of 2.76 mm when using the PCA method and 2.78 mm when using modified GRNN. Whereas performance improved with shorter window lengths, feature type and configuration had little influence