Model-based Cochlear Implant Programming

项目摘要 该项目的首要目标是开发和验证特定于患者的耳蜗计算模型 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是一种神经假体设备，使用一系列 植入电极刺激听神经，产生听觉感觉。有超过500,000名收件人 在世界范围内，CI被认为是严重到深度感觉性听力损失的护理治疗标准。 虽然这些设备的结果非常成功，但相当数量的CI接受者 言语理解能力差，即使是最好的表演者，也能恢复正常的听觉 富达是很少见的。据估计，在那些可以从这项技术中受益的人中，只有5%的人会进行植入， 这在很大程度上是由于结果高度不确定。结果的很大一部分可变性 对于cis，由于次优的神经电接口(Eni)；然而，估计患者的方法- 到目前为止，具体的弹性网卡一直不可靠。 这项研究的主要假设是，对患者特定的ENI的准确估计可以是 通过特定于患者的计算模型获得，并用于定制CI设置以改进和减少 可变的植入物性能。为了验证这一假设，首先，新的图像处理和特定于患者的 解剖学模型，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，并定位哪些神经纤维受到每个电极的刺激 需要开发和验证。接下来，患者定制的MOCIP战略的绩效，旨在解决 在弹性网卡中发现的次优条件将进行临床测试。最后，MOCIP技术将实现自动化 并集成到可部署到临床工作流程中的软件中。由于MOCIP战略只需要 更改配置项上的设置，它们与现有设备技术一起工作，不需要进一步手术，并且 可逆的。如果成功，一套MOCIP技术可以客观地指导CI的规划 将为新的和现有的CI接受者开发优化的设置和改进的听力恢复 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.