Computational Evaluation of Cochlear Implant Surgery Outcomes Accounting for Uncertainty and Parameter Variability

Autor: Nerea Mangado, Jordi Pons-Prats, Martí Coma, Pavel Mistrík, Gemma Piella, Mario Ceresa, Miguel Á. González Ballester
Přispěvatelé: Universitat Politècnica de Catalunya. Departament de Física
Rok vydání: 2017
Předmět:
0301 basic medicine
computational modeling
Computer science
Physiology
medicine.medical_treatment
Monte Carlo method
Surgical planning
lcsh:Physiology
surgical outcomes prediction
automatic framework
0302 clinical medicine
Cochlear implant
uncertainty analysis
Uncertainty analysis
Original Research
education.field_of_study
lcsh:QP1-981
Automatic framework
Computational modeling
probabilistic collocation method
Engineering
Mechanical
Probabilistic collocation method
Monte carlo
Algorithm
Finite element method
Engineering
Civil
Population
finite element models
Elements finits
Mètode dels
Engineering
Multidisciplinary
03 medical and health sciences
Surgical outcomes prediction
Physiology (medical)
Multiple imputation (Statistics)
medicine
Engineering
Ocean
Uncertainty quantification
education
Engineering
Aerospace
Engineering
Biomedical
monte carlo
Probabilistic logic
cochlear implant
Computer Science
Software Engineering
Confidence interval
Engineering
Marine
Engineering
Manufacturing
Monte Carlo
Mètode de
030104 developmental biology
Engineering
Industrial
030217 neurology & neurosurgery
Ciències de la salut [Àrees temàtiques de la UPC]
Zdroj: Frontiers in Physiology
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
Frontiers in Physiology, Vol 9 (2018)
Scipedia Open Access
Scipedia SL
Recercat. Dipósit de la Recerca de Catalunya
instname
ISSN: 1664-042X
Popis: 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. 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.
Databáze: OpenAIRE
Externí odkaz: