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dc.creatorKnoops, P.G.M. (Paul G. M.)-
dc.creatorBorghi, A. (Alessandro)-
dc.creatorRuggiero, F. (Federica)-
dc.creatorBadiali, G. (Giovanni)-
dc.creatorBianchi, A. (Alberto)-
dc.creatorMarchetti, C. (Claudio)-
dc.creatorRodriguez-Florez, N. (Naiara)-
dc.creatorBreakey, R.W.F. (Richard W. F.)-
dc.creatorJeelani, O. (Owase)-
dc.creatorDunaway, D.J. (David J.)-
dc.creatorSchievano, S. (Silvia)-
dc.identifier.citationKnoops, P.G.M. (Paul G. M.); Borghi, A. (Alessandro); Ruggiero, F. (Federica); et al. "A novel soft tissue prediction methodology for orthognathic surgery based on probabilistic finite element modelling". Plos one. 13 (5), 2018, e0197209es
dc.description.abstractRepositioning 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.es_ES
dc.description.sponsorshipThis work was supported by the Great Ormond Street Hospital Charity http://www.gosh. org/: grant FaceValue (no. 508857) to Silvia Schievano, David Dunaway, Owase Jeelani; Engineering and Physical Sciences Research Council https://www.epsrc.ac.uk/: award no EP/ N02124X/1 to Silvia Schievano and this work was undertaken at Great Ormond Street Hospital and University College London Institute of Child Health, who received a proportion of funding from the United Kingdom Department of Health’s National Institute for Health Research Biomedical Research Centre funding scheme. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.es_ES
dc.publisherPublic Library of Sciencees_ES
dc.subjectRepositioning of the maxillaes_ES
dc.subjectOrthognathic surgeryes_ES
dc.subjectFunctional and aesthetic purposeses_ES
dc.titleA novel soft tissue prediction methodology for orthognathic surgery based on probabilistic finite element modellinges_ES
dc.description.noteThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.es_ES
dadun.citation.publicationNamePlos onees_ES

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