Model-based Cochlear Implant Programming

项目摘要 该项目的总体目标是开发和验证耳蜗的患者特定计算模型， 植入（CI）刺激，并使用这些模型创建患者定制的基于模型的CI编程 （MOCIP）优化植入物性能的策略。CI是一种神经假体装置， 植入电极以刺激听觉神经并诱导听觉。有超过50万的接受者 在世界范围内，CI被认为是重度至极重度感觉性听力损失的标准治疗。 虽然这些设备的结果非常成功，但相当数量的CI接受者 经验差的语言理解，即使是最好的表演者，恢复正常的听觉， 忠诚是罕见的。据估计，只有5%的人谁可以受益于这项技术追求植入， 这在很大程度上是由于结果的高度不确定性。结果的很大一部分可变性 与CI是由于次优的电神经接口（ENI）;然而，估计患者的方法- 到目前为止，特定ENI是不可靠的。 本研究的首要假设是，可以准确估计患者特异性ENI。 使用患者特定的计算模型获得，并用于自定义CI设置，以改善和减少 不同的植入物性能。为了检验这一假设，首先，新颖的图像处理和患者特异性 解剖模型，其使用生物反馈信号进行调整，并允许通过确定 哪些听觉神经纤维是健康的，以及定位哪些神经纤维被每个电极刺激， 开发和验证。接下来，患者定制的MOCIP策略的性能，旨在解决 将对ENI中发现的次优条件进行临床测试。最后，MOCIP技术将自动化 并集成到可以部署到临床工作流程中的软件中。由于MOCIP战略只需要 CI上的设置变更，它们与现有器械技术配合使用，不需要进一步手术， 可逆的如果成功，一套MOCIP技术可以客观地指导CI的编程， 优化设置和改善新的和现有的CI接受者的听力恢复将在此 项目

Project Summary The overarching goal of this project is to develop and validate patient-specific computational models of cochlear implant (CI) stimulation and to use these models to create patient-customized, MOdel-based CI Programming (MOCIP) strategies that optimize implant performance. CIs are a neuroprosthetic devices that use an array of implanted electrodes to stimulated the auditory nerve and induce hearing sensation. With over 500,000 recipients worldwide, CI are considered the standard of care treatment for severe-to-profound sensory-based hearing loss. While results with these devices have been remarkably successful, a significant number of CI recipients experience poor speech understanding, and, even among the best performers, restoration to normal auditory fidelity is rare. It is estimated that only 5% of those who could benefit from this technology pursue implantation, in large part due to the high-degree of uncertainty in outcomes. A substantial portion of the variability in outcomes with CIs is due to a sub-optimal electro-neural interface (ENI); however, approaches for estimating the patient- specific ENI have thus far been unreliable. The overarching hypothesis of this study is that an accurate estimation of the patient-specific ENI can be obtained with patient-specific computational models and used to customize CI settings for improved and less variable implant performance. To test this hypothesis, first, novel image processing and patient-specific anatomical models, which are tuned using biofeedback signals and permit estimating the ENI by determining which auditory nerve fibers are healthy and localizing which nerve fibers are stimulated by each electrode, will be developed and validated. Next, the performance of patient-customized MOCIP strategies that aim to address sub-optimal conditions found in the ENI will be clinically tested. Finally, MOCIP techniques will be automated and integrated into software that can be deployed into the clinical workflow. Since MOCIP strategies require only a change of settings on the CI, they work with existing device technology, do not require further surgery, and are reversible. If successful, a suite of MOCIP techniques that can objectively guide the programming of CIs towards optimized settings and improve hearing restoration for new and existing CI recipients will be developed in this project.