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Model-based cochlear implant programming

Model-based cochlear implant programming
Project Number:
Project Lead:
Jack Noble
Award Organization:
National Institutes of Health
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.



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Contact: Jack NobleEmail

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