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.
