Repositioning of the maxilla in orthognathic surgery is carried out for functional and aesthetic
purposes. Pre-surgical planning tools can predict 3D facial appearance by computing the
response of the soft tissue to the changes to the underlying skeleton. The clinical use of
commercial prediction software remains controversial, likely due to the deterministic nature
of these computational predictions. A novel probabilistic finite element model (FEM) for the
prediction of postoperative facial soft tissues is proposed in this paper. A probabilistic FEM
was developed and validated on a cohort of eight patients who underwent maxillary repositioning and had pre- and postoperative cone beam computed tomography (CBCT) scans
taken. Firstly, a variables correlation assessed various modelling parameters. Secondly, a
design of experiments (DOE) provided a range of potential outcomes based on uniformly
distributed input parameters, followed by an optimisation. Lastly, the second DOE iteration
provided optimised predictions with a probability range. A range of 3D predictions was
obtained using the probabilistic FEM and validated using reconstructed soft tissue surfaces
from the postoperative CBCT data. The predictions in the nose and upper lip areas accurately include the true postoperative position, whereas the prediction under-estimates the
position of the cheeks and lower lip. A probabilistic FEM has been developed and validated
for the prediction of the facial appearance following orthognathic surgery. This method
shows how inaccuracies in the modelling and uncertainties in executing surgical planning
influence the soft tissue prediction and it provides a range of predictions including a minimum and maximum, which may be helpful for patients in understanding the impact of surgery on the face