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The standard-of-care treatment to restore sound perception for individuals with severe-to-profound sensorineural hearing loss is the Cochlear Implant (CI) — a small, surgically-inserted electronic device that bypasses most of the mechanism of unaided acoustic hearing to directly stimulate Auditory Nerve Fibers (ANFs). Although many individuals experience success with these devices, a significant portion of recipients receive only marginal benefits. Biophysical models of ANFs have been developed that could be used in an image-guided treatment pipeline for patient-customized CI interventions. However, due to the difficult nature of determining neuron properties in humans, existing models rely on parameters derived from animal studies that were subsequently adapted to human models. Additionally, it is well-established that individual neurons of a single type can be non-homogeneous. In this research, we present a sensitivity analysis of a set of parameters used in one existing fiber model to (1) establish the influence of these parameters on predicted neural activity and (2) explore whether incorporation of these properties as patient-specific tunable parameters in a neural health optimization algorithm can produce a more comprehensive picture of ANF health when used in an image-guided treatment pipeline.
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