Model-based Cochlear Implant Programming

基于模型的人工耳蜗编程

基本信息

批准号：
10615769
负责人：
Jack Noble
金额：
$ 60.43万
依托单位：
VANDERBILT UNIVERSITY
依托单位国家：
美国
项目类别：
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/10615769
关键词：
Acceleration Acoustic Nerve Acoustic Stimulation Address Anatomic Models Audiology Auditory Biofeedback Characteristics Clinical Cochlea Cochlear Implants Computer Models Computer software Custom Devices Distant Ear Electrodes Esthesia Estimation Techniques Experimental Designs Fiber Frequencies Future Goals Health Hearing Hour Implant Implanted Electrodes Individual Maps 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 clinical translation design electrical potential experience hearing impairment hearing restoration image processing implantation improved improved outcome individual patient neural neural stimulation neuroprosthesis novel novel strategies programs recruit restoration simulation sound standard of care tool

项目摘要

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。

项目成果

期刊论文数量（19）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Atlas-based Segmentation of Intracochlear Anatomy in Metal Artifact Affected CT Images of the Ear with Co-trained Deep Neural Networks.

DOI：
10.1007/978-3-030-87202-1_2
发表时间：
2021-09
期刊：
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
影响因子：
0
作者：
Wang J;Su D;Fan Y;Chakravorti S;Noble JH;Dawant BM
通讯作者：
Dawant BM

Super-resolution segmentation network for inner-ear tissue segmentation.

用于内耳组织分割的超分辨率分割网络。

DOI：
10.1007/978-3-031-44689-4_2
发表时间：
2023
期刊：
Simulation and synthesis in medical imaging : ... International Workshop, SASHIMI ..., held in conjunction with MICCAI ..., proceedings. SASHIMI (Workshop)
影响因子：
0
作者：
Liu,Ziteng;Fan,Yubo;Lou,Ange;Noble,JackH
通讯作者：
Noble,JackH

Musical Sound Quality as a Function of the Number of Channels in Modern Cochlear Implant Recipients.

现代人工耳蜗植入者的音乐音质与通道数量的关系。

DOI：
10.3389/fnins.2019.00999
发表时间：
2019
期刊：
Frontiers in neuroscience
影响因子：
4.3
作者：
Berg,Katelyn;Noble,Jack;Dawant,Benoit;Dwyer,Robert;Labadie,Robert;Richards,Virginia;Gifford,René
通讯作者：
Gifford,René

Auditory Nerve Fiber Health Estimation Using Patient Specific Cochlear Implant Stimulation Models.