发表机构
Washington University St. Louis; Vanderbilt University; University of Rennes; Vanderbilt University Medical Center（华盛顿大学圣路易斯分校; 范德堡大学; 雷恩大学; 范德堡大学医学中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出利用医学图像和电生理测量构建耳蜗植入物电-神经接口的患者特异性数字孪生，其神经健康估计与言语识别率强相关，并初步显示基于数字孪生的重映射可提升单词识别率。
AI 中文摘要
耳蜗植入物是一种神经假体，利用电极阵列通过电脉冲直接刺激听觉神经纤维。已知影响结果的一个因素是电-神经接口的质量。在这项工作中，我们提出了一种流程，能够创建精确且患者特异性的数字孪生，以模拟耳蜗植入物的电-神经接口。我们利用医学图像和来自患者设备的电生理测量来构建数字孪生。我们通过将神经响应测量的预测与临床测量和言语识别结果进行比较来验证我们的数字孪生。在29例病例的分析中，数字孪生估计的神经健康与单词识别率（R=0.75，p<1e-5）强相关，在21例病例的分析中，与噪声中句子识别率（R=0.78，p<1e-4）强相关。我们还报告了基于数字孪生的患者重映射的初步发现，涉及6名受试者，单词识别率平均提高了10个百分点。这些数字孪生有潜力为临床医生提供对个体耳蜗植入接受者电-神经接口的前所未有的视角，当用作程序定制的基础时，可能导致听力结果的改善。
英文摘要
The cochlear implant is a neural prosthetic that uses an array of electrodes to directly stimulate auditory nerve fibers with electrical impulses. One factor that is known to impact outcomes is the quality of the electro-neural interface. In this work, we propose a pipeline that permits creating accurate and patient-specific digital twins that model the cochlear implant electro-neural interface. We leverage medical images and electrophysiological measurements from the patient's device for digital twin construction. We validate our digital twins by comparing predictions of neural response measurements to clinical measurements and speech recognition outcomes. Neural health estimated by the digital twin correlates strongly with word recognition rates (R=0.75, p<1e-5) in an analysis of 29 cases and sentence in noise recognition rates (R=0.78, p<1e-4) in an analysis of 21 cases. We also report preliminary findings with digital twin-based patient remapping with 6 subjects, where word recognition rates improved on average by 10 percentage points. These digital twins have the potential to provide clinicians with an unprecedented window into the electro-neural interface for individual cochlear implant recipients, which could lead to improved hearing outcomes when used as a foundation for program customization.
