Model-based Cochlear Implant Programming

基于模型的人工耳蜗编程

• 批准号: 10405540
• 负责人: Jack Noble
• 金额: $ 60.16万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-06-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10405540
• 关键词: Acoustic Nerve; Address; Anatomic Models; Audiology; Auditory; Biofeedback; Characteristics; Clinical; Cochlea; Cochlear Implants; Computer Models; Computer software; Custom; Devices; Distant; Ear; Electrodes; Esthesia; Fiber; Frequencies; Future; Goals; Health; Hearing; Hour; Implant; Implanted Electrodes; Individual; Lead; Measures; Methods; Modeling; Nerve; Nerve Fibers; Operative Surgical Procedures; Outcome; Patients; Pattern; Performance; Population; Process; Recommendation; Refractory; Research; Resolution; Resort; Sensory; Signal Transduction; Site; Speech; System; Techniques; Technology; Testing; Time; Uncertainty; Work; base; clinical translation; design; electrical potential; experience; experimental study; hearing impairment; hearing restoration; image processing; implantation; improved; improved outcome; individual patient; neural stimulation; neuroprosthesis; novel; novel strategies; programs; recruit; relating to nervous system; restoration; simulation; sound; standard of care; tool

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.

项目摘要 该项目的总体目标是开发和验证人工耳蜗的患者特定计算模型 植入物（CI）刺激并使用这些模型来创建基于患者的，基于模型的CI编程 （MOCIP）优化植入物绩效的策略。顺式是一种使用的神经假体设备 植入电极刺激听觉神经并引起听力。超过500,000名收件人 在全球范围内，CI被认为是严重基于感觉的听力损失的护理标准。 尽管这些设备的结果取得了非常成功，但大量的CI接收者 体验不良的语音理解，即使是表现最好的人，也可以恢复正常的听觉 忠诚是罕见的。据估计，只有5％的人可以从该技术中受益的人， 在很大程度上，由于结果的不确定性高度。结果的大部分变异性 带有顺式是由于亚最佳的电性界面（ENI）所致；但是，估计患者的方法 到目前为止，特定的ENI是不可靠的。 这项研究的总体假设是，可以准确估计患者特异性ENI 使用特定于患者的计算模型获得，用于自定义CI设置以改进，更少 可变植入物的性能。要检验这一假设，首先是新型图像处理和特定于患者 解剖模型是使用生物反馈信号调整的，并允许通过确定ENI进行估算 哪些听觉神经纤维健康且本地化，每个电极刺激哪些神经纤维，将 可以开发和验证。接下来，旨在解决患者的摩托普策略的表现 ENI中发现的次优条件将在临床上测试。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策略仅需要 CI上的设置改变，它们与现有的设备技术合作，不需要进一步手术，并且是 可逆。如果成功，可以客观地指导CIS编程的一套Mocip技术 在此中将开发优化的设置并改善新的CI和现有CI收件人的听力恢复 项目。