ArticleSimulation and synthesis in medical imaging : ... International Workshop, SASHIMI ..., held in conjunction with MICCAI ..., proceedings. SASHIMI (Workshop)2020
Auditory Nerve Fiber Health Estimation Using Patient Specific Cochlear Implant Stimulation Models.
Article in Simulation and synthesis in medical imaging : ... International Workshop, SASHIMI ..., held in conjunction with MICCAI ..., proceedings. SASHIMI (Workshop), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- DABS-MS: deep atlas-based segmentation using the Mumford-Shah functional.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Model-Based Inference of Electrode Distance and Neuronal Density from Measured Detection Thresholds in Cochlear Implant Listeners.Journal of the Association for Research in Otolaryngology : JARO · 2025Article
- Comparison of response properties of the electrically stimulated auditory nerve reported in human listeners and in animal models.Hearing research · 2022Review
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Authors and funding
3 authors.
Funding
Abstract
Cochlear implants (CIs) restore hearing using an array of electrodes implanted in the cochlea to directly stimulate auditory nerve fibers (ANFs). Hearing outcomes with CIs are dependent on the health of the ANFs. In this research, we developed an approach to estimate the health of ANFs using patient-customized, image-based computational models of CI stimulation. Our stimulation models build on a previous model-based solution to estimate the intra-cochlear electric field (EF) created by the CI. Herein, we propose to use the estimated EF to drive ANF models representing 75 nerve bundles along the length of the cochlea. We propose a method to detect the neural health of the ANF models by optimizing neural health parameters to minimize the sum of squared differences between simulated and the physiological measurements available via patients' CIs. The resulting health parameters provide an estimate of the health of ANF bundles. Experiments with 8 subjects show promising model prediction accuracy, with excellent agreement between neural stimulation responses that are clinically measured and those that are predicted by our parameter optimized models. These results suggest our modeling approach may provide an accurate estimation of ANF health for CI users.
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