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dc.rights.licenseAttribution 4.0 International*
dc.contributor.authorRODRIGUEZ-FLOREZ, NAIARA
dc.contributor.otherKnoops, P.G.M.
dc.contributor.otherSchievano, S.
dc.contributor.otherDunaway, D.
dc.contributor.otherJeelani, O.
dc.contributor.otherMarchetti, Claudio
dc.contributor.otherBreakey, Richard W. F.
dc.contributor.otherBianchi, Alberto
dc.contributor.otherBorghi, Alessandro
dc.contributor.otherRuggiero, Federica
dc.contributor.otherBadiali, Giovanni
dc.date.accessioned2018-07-27T15:18:33Z
dc.date.available2018-07-27T15:18:33Z
dc.date.issued2018
dc.identifier.issn1932-6203eu_ES
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=147714eu_ES
dc.identifier.urihttps://hdl.handle.net/20.500.11984/1101
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.
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.eu_ES
dc.language.isoengeu_ES
dc.publisherPLOSeu_ES
dc.rights© 2018 Knoops et al.eu_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectmaxillofacial surgeryeu_ES
dc.subjectsimulationeu_ES
dc.subjectdeformationseu_ES
dc.subjectdistractioneu_ES
dc.subjectvalidationeu_ES
dc.subjectdeformityeu_ES
dc.subjectcartilageeu_ES
dc.subjectaccuracyeu_ES
dc.subjectsystemeu_ES
dc.subjectskineu_ES
dc.titleA novel soft tissue prediction methodology for orthognathic surgery based on probabilistic finite element modellingeu_ES
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2eu_ES
dcterms.sourcePLoS ONEeu_ES
local.contributor.groupTecnologías de superficieseu_ES
local.description.peerreviewedtrueeu_ES
local.identifier.doihttps://doi.org/10.1371/journal.pone.0197209eu_ES
local.relation.projectIDUnited Kingdom. Great Ormond Street Hospital Charity. 50885. FaceValueeu_ES
local.relation.projectIDUnited Kingdom. Engineering and Physical Sciences Research Council.EP-N02124X-1eu_ES
local.relation.projectIDUnited Kingdom. Department of Health's National Institute for Health Research Biomedical Research Centreeu_ES
local.source.detailsVol. 13. Nº. 5. May 2018eu_ES
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501eu_ES
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85eu_ES


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