Cochlear implants (CIs) use an array of electrodes implanted in the cochlea to directly stimulate the auditory nerve. After surgery, CI recipients undergo many programming sessions with an audiologist who adjusts CI processor settings to improve performance. However, few tools exist to help audiologists know what settings will lead to better performance. In order to provide objective information to the audiologist for programming, our group has developed a system to permit estimating which auditory neural sites are stimulated by which CI electrodes. To do this, we have proposed physics-based models to calculate the electric field in the cochlea generated by electrical stimulation. However, solving these models require days of computation time and substantial computational resources. In this paper, we proposed a deep-learningbased method to estimate the patient-specific electric fields using a U-Net-like architecture with physics-based loss function. Our network is trained with a dataset generated by solving physics-based models and the results show that the proposed method can achieve similar accuracy with traditional method and largely improves the speed of estimating the intra-cochlear electric field.
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Cochlear implant electric field estimation using 3D neural networks
Semantic Scholar · Engineering · 2021
Abstract
Cochlear implants (CIs) use an array of electrodes implanted in the cochlea to directly stimulate the auditory nerve. After surgery, CI recipients undergo many programming sessions with an audiologist who adjusts CI processor settings to improve performance. However, few tools exist to help audiologists know what settings will lead to better performance. In order to provide objective information to the audiologist for programming, our group has developed a system to permit estimating which auditory neural sites are stimulated by which CI electrodes. To do this, we have proposed physics-based models to calculate the electric field in the cochlea generated by electrical stimulation. However, solving these models require days of computation time and substantial computational resources. In this paper, we proposed a deep-learningbased method to estimate the patient-specific electric fields using a U-Net-like architecture with physics-based loss function. Our network is trained with a dataset generated by solving physics-based models and the results show that the proposed method can achieve similar accuracy with traditional method and largely improves the speed of estimating the intra-cochlear electric field.