Detalle Publicación

A novel soft tissue prediction methodology for orthognathic surgery based on probabilistic finite element modelling.

Autores: Knoops, P.G.M. (Autor de correspondencia); Borghi, A.; Ruggiero, F.; Badiali, G.; Bianchi, A.; Marchetti, C.; Rodríguez Florez, Naiara; Breakey, R.W.F.; Jeelani, O.; Dunaway, D.
Título de la revista: PLOS ONE
ISSN: 1932-6203
Volumen: 13
Número: 5
Fecha de publicación: 2018
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.