Computer-assisted, image-guided programming of Cochlear Implants

 描述(申请人提供)：到目前为止，已有超过32万名听力障碍患者接受了人工耳蜗术(CI)，在这种植入术中，旨在通过电刺激取代自然神经刺激的电极阵列被嵌入耳蜗术中。虽然人工智能用户的语音理解效果通常很好，但即使是最好的用户也抱怨说，自然听力的保真度很少被复制。此外，相当一部分人确实很穷。最近，我们开发了一系列图像处理算法，对CT图像进行操作，首次允许精确确定单个CI电极相对于其激活的神经的位置。这将从根本上改变CI接受者的术后管理，因为它也首次允许开发由客观成像数据提供信息的定制电极编程策略。我们已经开发了这样一种图像引导人工耳蜗植入编程(IGCIP)策略，我们已经证明，它可以在不需要额外手术的情况下显著改善长期CI用户的听力。我们方法的成功率也很显著，参与我们正在进行的研究的107名长期受助者77%的时间倾向于新的环境。为了扩大患者对这项技术的使用，我们现在提议完全自动化我们的算法，并开发可供临床医生在护理时间和地点使用的软件包。我们还建议在我们的机构和合作地点进行大规模的临床验证，以发现影响我们方法性能的因素并进一步改进它。

 DESCRIPTION (provided by applicant): To date, over 320,000 hearing impaired individuals have received cochlear implants (CIs) in which an electrode array designed to substitute natural nerve stimulation by electrical stimulation is threaded into the cochlea. While speech understanding outcomes are typically good among CI users, even the best users complain that the fidelity of natural hearing is rarely reproduced. Additionally, a significant minority do poorl. Recently, we have developed a series of image processing algorithms that operate on CT images and permit for the first time to determine precisely the position of individual CI electrodes with respect to the nerves they activate. This will fundamentally change the postoperative management of CI recipients because it permits, also for the first time, the development of custom electrode programing strategies that are informed by objective imaging data. We have developed such an Image-Guided Cochlear Implant Programming (IGCIP) strategy and we have demonstrated that it can substantially and sometime dramatically improve hearing in long term CI users without requiring an additional surgical procedure. The success rate of our approach is also remarkable with the new setting being preferred 77% of the time by the 107 long term recipients who have participated in our ongoing study. To broaden patient access to this technology, we now propose to completely automate our algorithms and to develop software packages that will be useable by clinicians at the time and point of care. We also propose to conduct a large scale clinical validation both at our institution and at collaborating sites to discover factors that affect the performance of our method and further improve it.