Mangado Nerea, Pons-Prats Jordi, Coma Martí, Mistrík Pavel, Piella Gemma, Ceresa Mario, González Ballester Miguel Á
BCNMedTech, Universitat Pompeu Fabra, Barcelona, Spain.
International Center for Numerical Methods in Engineering, Barcelona, Spain.
Front Physiol. 2018 May 23;9:498. doi: 10.3389/fphys.2018.00498. eCollection 2018.
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.
人工耳蜗植入（CI）是一种复杂的外科手术，可恢复重度耳聋患者的听力。植入设备的成功结果依赖于一组因素，其中一些因素不可预测或难以控制。电极阵列位置和骨骼电特性的不确定性使得难以准确计算植入物传递的电流传播以及由此产生的神经激活。在此背景下，我们使用不确定性量化方法来探索这些不确定性如何在CI计算模拟的所有阶段中传播。为此，我们采用了一个自动框架，涵盖从CI模型的有限元生成到植入物刺激引起的神经反应评估。为了估计模拟神经反应的置信区间，我们提出了两种方法。首先，我们通过统计形状模型对人群中耳蜗形态的变异性进行编码。这使我们能够使用蒙特卡罗采样生成一组虚拟患者，并根据统计分布为他们每个人分配一组参数值。该框架在高通量计算环境中实现并并行化，从而能够最大限度地利用可用计算资源。其次，我们进行一项针对特定患者的研究，以评估计算出的神经反应，以寻求最佳的植入后刺激水平。考虑到单一的耳蜗形态，使用概率配置方法传播组织电阻率和手术插入参数的不确定性，这减少了要评估的样本数量。结果表明，骨电阻率对CI结果的影响最大。结合耳蜗长度的变异性，对于电阻率值高的小耳蜗，获得的结果最差。然而，手术插入长度对CI结果的影响无法清晰观察到，因为其影响可能被其他考虑的参数所掩盖。虽然蒙特卡罗方法意味着高计算成本，但概率配置在精度和计算时间之间呈现出合适的权衡。结果表明，所提出的框架在手术规划决策和听力设置过程中都具有很大的帮助潜力。
