The cochlear implant (CI) is a neural prosthetic that is the standard-of-care treatment for severe-toprofound hearing loss. CIs consist of an electrode array inserted into the cochlea that electrically stimulates auditory nerve fibers to induce the sensation of hearing. Competing stimuli occur when multiple electrodes stimulate the same neural pathways. This is known to negatively impact hearing outcomes. Previous research has shown that image-processing techniques can be used to analyze the CI position in CT scans to estimate the degree of competition between electrodes based on the CI user's unique anatomy and electrode placement. The resulting data permits an algorithm or expert to select a subset of electrodes to keep active to alleviate competition. Expert selection of electrodes using this data has been shown in clinical studies to lead to significantly improved hearing outcomes for CI users. Currently, we aim to translate these techniques to a system designed for worldwide clinical use, which mandates that the selection of active electrodes be automated by robust algorithms. Previously proposed techniques produce optimal plans with only 48% success rate. In this work, we propose a new graph-based approach. We design a graph with nodes that represent electrodes and edge weights that encode competition between electrode pairs. We then find an optimal path through this graph to determine the active electrode set. Our method produces results judged by an expert to be optimal in over 95% of cases. This technique could facilitate widespread clinical translation of image-guided cochlear implant programming methods.

No abstract

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.

Objective. The cochlear implant is a neural prosthesis designed to directly stimulate auditory nerve fibers to induce the sensation of hearing in those experiencing severe-to-profound hearing loss. After surgical implantation, audiologists program the implant’s external processor with settings intended to produce optimal hearing outcomes. The likelihood of achieving optimal outcomes increases when audiologists have access to tools that objectively present information related to the patient’s own anatomy and surgical outcomes. This includes visualizations like the one presented here, termed the activation region overlap image, which is designed to decrease subjectivity when determining amounts of overlapping stimulation between implant electrodes. Approach. This visualization uses estimates of electric field strength to indicate spread of neural excitation due to each electrode. Unlike prior visualizations, this method explicitly defines regions of nerves receiving substantial stimulation from each electrode to help clinicians assess the presence of significant overlapping stimulation. A multi-reviewer study compared this and an existing technique on the consistency, efficiency, and optimality of plans generated from each method. Statistical significance was evaluated using the two-sided Wilcoxon rank sum test. Main results. The study showed statistically significant improvements in consistency (p < 10–12), efficiency (p < 10−15), and optimality (p < 10−5) when generating plans using the proposed method versus the existing method. Significance. This visualization addresses subjectivity in assessing overlapping stimulation between implant electrodes, which currently relies on reviewer estimates. The results of the evaluation indicate the provision of such objective information during programming sessions would likely benefit clinicians in making programming decisions.