ABSTRACT
A percentage (i.e., 5.6%) of Cochlear Implant (CI) users reportedly experience unwanted facial nerve stimulation (FNS). For some, the effort to control this problem results in changing stimulation parameters, thereby reducing their hearing performance. For others, the only viable solution is to deactivate the CI completely. A growing body of evidence in the form of case reports suggests that undesired FNS can be effectively addressed through re-implantation with an Oticon Medical (OM) Neuro-Zti implant. However, the root of this benefit is still unknown: is it due to surgical adjustments, such as varied array geometries and/or positioning, or does it stem from differences in stimulation parameters and/or grounding? The OM device exhibits two distinct features: (1) unique stimulation parameters, including anodic leading pulses and loudness controlled by pulse duration-not current-resulting in lower overall current amplitudes; and (2) unconventional grounding, including both passive (capacitive) discharge, which creates a pseudo-monophasic pulse shape, and a 'distributed-all-polar' (DAP) grounding scheme, which is thought to reduce current spread. Unfortunately, case reports alone cannot distinguish between surgical factors and these implant-related ones. In this paper, we present a novel follow-up study of two CI subjects who previously experienced FNS before re-implantation with Neuro-Zti implants. We used the Oticon Medical Research Platform (OMRP) to stimulate a single electrode in each subject in two ways: (1) with traditional monopolar biphasic cathodic-first pulses, and (2) with distinct OM clinical stimulation. We progressively increased the stimulation intensity until FNS occurred or the sound became excessively loud. Non-auditory/FNS sensations were observed with the traditional stimulation but not with the OM clinical one. This provides the first direct evidence demonstrating that stimulation parameters and/or grounding-not surgical factors-play a key role in mitigating FNS.
ABSTRACT
The robust delineation of the cochlea and its inner structures combined with the detection of the electrode of a cochlear implant within these structures is essential for envisaging a safer, more individualized, routine image-guided cochlear implant therapy. We present Nautilus-a web-based research platform for automated pre- and post-implantation cochlear analysis. Nautilus delineates cochlear structures from pre-operative clinical CT images by combining deep learning and Bayesian inference approaches. It enables the extraction of electrode locations from a post-operative CT image using convolutional neural networks and geometrical inference. By fusing pre- and post-operative images, Nautilus is able to provide a set of personalized pre- and post-operative metrics that can serve the exploration of clinically relevant questions in cochlear implantation therapy. In addition, Nautilus embeds a self-assessment module providing a confidence rating on the outputs of its pipeline. We present a detailed accuracy and robustness analyses of the tool on a carefully designed dataset. The results of these analyses provide legitimate grounds for envisaging the implementation of image-guided cochlear implant practices into routine clinical workflows.
