Mostra el registre d'ítem simple

dc.contributor.authorMangado, Nerea
dc.contributor.authorPons Prats, Jordi
dc.contributor.authorComa Company, Martí
dc.contributor.authorMistrik, Pavel
dc.contributor.authorPiella, Gemma
dc.contributor.authorCeresa, Mario
dc.contributor.authorGonzález Ballester, Miguel A.
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Física
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Ciència i Tecnologia Aeroespacials
dc.date.accessioned2020-03-27T10:05:36Z
dc.date.available2020-03-27T10:05:36Z
dc.date.issued2019-04-04
dc.identifier.citationMangado, N. [et al.]. Computational evaluation of Cochlear implant surgery outcomes accounting for uncertainty and parameter variability. A: "Advanced HPC-based computational modeling in biomechanics and systems biology". Lausanne: Frontiers Media, 2019, p. 196-209.
dc.identifier.isbn1664-8714
dc.identifier.urihttp://hdl.handle.net/2117/181971
dc.description.abstractCochlear implantation (CI) is a complex surgical procedure that restores hearing in patients with severe deafness. The successful outcome of the implanted device relies on a group of factors, some of them unpredictable or difficult to control. Uncertainties on the electrode array position and the electrical properties of the bone make it difficult to accurately compute the current propagation delivered by the implant and the resulting neural activation. In this context, we use uncertainty quantification methods to explore how these uncertainties propagate through all the stages of CI computational simulations. To this end, we employ an automatic framework, encompassing from the finite element generation of CI models to the assessment of the neural response induced by the implant stimulation. To estimate the confidence intervals of the simulated neural response, we propose two approaches. First, we encode the variability of the cochlear morphology among the population through a statistical shape model. This allows us to generate a population of virtual patients using Monte Carlo sampling and to assign to each of them a set of parameter values according to a statistical distribution. The framework is implemented and parallelized in a High Throughput Computing environment that enables to maximize the available computing resources. Secondly, we perform a patient-specific study to evaluate the computed neural response to seek the optimal post-implantation stimulus levels. Considering a single cochlear morphology, the uncertainty in tissue electrical resistivity and surgical insertion parameters is propagated using the Probabilistic Collocation method, which reduces the number of samples to evaluate. Results show that bone resistivity has the highest influence on CI outcomes. In conjunction with the variability of the cochlear length, worst outcomes are obtained for small cochleae with high resistivity values. However, the effect of the surgical insertion length on the CI outcomes could not be clearly observed, since its impact may be concealed by the other considered parameters. Whereas the Monte Carlo approach implies a high computational cost, Probabilistic Collocation presents a suitable trade-off between precision and computational time. Results suggest that the proposed framework has a great potential to help in both surgical planning decisions and in the audiological setting process.
dc.format.extent14 p.
dc.language.isoeng
dc.publisherFrontiers Media
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Ciències de la salut
dc.subject.lcshCochlear Implant
dc.subject.lcshUncertainty (Information theory)--Mathematical models
dc.subject.lcshFinite element method
dc.subject.lcshMultiple imputation (Statistics)
dc.subject.lcshMonte Carlo method
dc.subject.otherCochlear implant
dc.subject.otherSurgical outcomes prediction
dc.subject.otherAutomatic framework
dc.subject.otherUncertainty analysis
dc.subject.otherFinite element models
dc.subject.otherComputational modeling
dc.subject.otherMonte carlo
dc.subject.otherProbabilistic collocation method
dc.titleComputational evaluation of Cochlear implant surgery outcomes accounting for uncertainty and parameter variability
dc.typePart of book or chapter of book
dc.subject.lemacMontecarlo, Mètode de
dc.subject.lemacImplants coclears
dc.subject.lemacElements finits, Mètode dels
dc.contributor.groupUniversitat Politècnica de Catalunya. GMNE - Grup de Mètodes Numèrics en Enginyeria
dc.identifier.doi10.3389/fphys.2018.00498
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionwww.frontiersin.org
dc.rights.accessOpen Access
local.identifier.drac24242225
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/SPAIN/MINECO/MDM2015-0502
dc.relation.projectidinfo:eu-repo/grantAgreement/AGAUR/2016-PROD00047
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/FP7/304857/EU/High-resolution image-based computational inner ear modelling for surgical planning of cochlear implantation/HEAR-EU
local.citation.authorMangado, N.; Pons-Prats, J.; Coma, M.; Mistrik, P.; Piella, G.; Ceresa, M.; González Ballester, Miguel A.
local.citation.pubplaceLausanne
local.citation.publicationNameAdvanced HPC-based computational modeling in biomechanics and systems biology
local.citation.startingPage196
local.citation.endingPage209


Fitxers d'aquest items

Thumbnail

Aquest ítem apareix a les col·leccions següents

Mostra el registre d'ítem simple