Development of a \(\upmu \)CT-based Patient-Specific Model of the Electrically Stimulated Cochlea

  • Ahmet CakirEmail author
  • Benoit M. Dawant
  • Jack H. Noble
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)


Cochlear implants (CIs) are neural prosthetics that are used to treat sensory-based hearing loss. There are over 320,000 recipients worldwide. After implantation, each CI recipient goes through a sequence of programming sessions where audiologists determine several CI processor settings to attempt to optimize hearing outcomes. However, this process is difficult because there are no objective measures available to indicate what setting changes will lead to better hearing outcomes. It has been shown that a simplified model of electrically induced neural activation patterns within the cochlea can be created using patient CT images, and that audiologists can use this information to determine settings that lead to better hearing performance. A more comprehensive physics-based patient-specific model of neural activation has the potential to lead to even greater improvement in outcomes. In this paper, we propose a method to create such customized electro-anatomical models of the electrically stimulated cochlea. We compare the accuracy of our patient-specific models to the accuracy of generic models. Our results show that the patient-specific models are on average more accurate than the generic models, which motivates the use of a patient-specific modeling approach for cochlear implant patients.


Cochlear implant Modeling Auditory nerve 



This research has been supported by NIH grant R01DC014037. The content is solely the responsibility of the authors and does not necessarily represent the official views of this institute.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ahmet Cakir
    • 1
    Email author
  • Benoit M. Dawant
    • 1
  • Jack H. Noble
    • 1
  1. 1.Department of Electrical Engineering and Computer ScienceVanderbilt UniversityNashvilleUSA

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