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Cochlear implants (CIs) are considered the standard-of-care treatment for profound sensory-based hearing loss. After CI surgery, an audiologist will adjust the CI processor settings for CI recipients to improve overall hearing performance. However, this programming procedure can be long and may lead to suboptimal outcomes due to the lack of objective information. In previous research, our group has developed methods that use patient-specific electrical characteristics to simulate the activation pattern of auditory nerves when they are stimulated by CI electrodes. However, estimating those electrical characteristics require extensive computation time and resources. In this paper, we proposed a deep-learning-based method to coarsely estimate the patient-specific electrical characteristics using a cycle-consistent network architecture. These estimates can then be further optimized using a limited range conventional searching strategy. Our network is trained with a dataset generated by solving physics-based models. The results show that our proposed method can generate high-quality predictions that can be used in the patient-specific model and largely improves the speed of constructing models.
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