Welcome to the UPF Digital Repository

Computational evaluation of Cochlear implant surgery outcomes accounting for uncertainty and parameter variability

Show simple item record

dc.contributor.author Mangado López, Nerea
dc.contributor.author Pons-Prats, Jordi
dc.contributor.author Coma, Martí
dc.contributor.author Mistrik, Pavel
dc.contributor.author Piella Fenoy, Gemma
dc.contributor.author Ceresa, Mario
dc.contributor.author González Ballester, Miguel Ángel, 1973-
dc.date.accessioned 2018-08-30T07:28:14Z
dc.date.available 2018-08-30T07:28:14Z
dc.date.issued 2018
dc.identifier.citation Mangado N, Pons-Prats J, Coma M, Mistrik P, Piella G, Ceresa M, González Ballester MA. Computational evaluation of Cochlear implant surgery outcomes accounting for uncertainty and parameter variability. Front. Physiol. 2018;9:498. DOI: 10.3389/fphys.2018.00498
dc.identifier.issn 1664-042X
dc.identifier.uri http://hdl.handle.net/10230/35406
dc.description.abstract Cochlear 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.description.sponsorship This work was partly supported by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Program (MDM-2015-0502), by the AGAUR grant 2016-PROD-00047, the European Union Seventh Framework Program (FP7/2007-2013), Grant agreement 304857, HEAR-EU project and the QUAES Foundation Chair for Computational Technologies for Healthcare.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Frontiers
dc.relation.ispartof Frontiers of Physiology. 2018;9:498.
dc.rights © 2018 Mangado, Pons-Prats, Coma, Mistrík, Piella, Ceresa and González Ballester. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.title Computational evaluation of Cochlear implant surgery outcomes accounting for uncertainty and parameter variability
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.3389/fphys.2018.00498
dc.subject.keyword Cochlear implant
dc.subject.keyword Surgical outcomes prediction
dc.subject.keyword Automatic framework
dc.subject.keyword Uncertainty analysis
dc.subject.keyword Finite element models
dc.subject.keyword Computational modeling
dc.subject.keyword Monte carlo
dc.subject.keyword Probabilistic collocation method
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/304857
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse

My Account

Statistics

Compliant to Partaking