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3D printed biomimetic cochleae and machine learning co-modelling provides clinical informatics for cochlear implant patients

ABSTRACT: Cochlear implants restore hearing in patients with severe to profound deafness by delivering electrical stimuli inside the cochlea. Understanding stimulus current spread, and how it correlates to patient-dependent factors, is hampered by the poor accessibility of the inner ear and by the lack of clinically-relevant in vitro, in vivo or in silico models. Here, we present 3D printing-neural network co-modelling for interpreting electric field imaging profiles of cochlear implant patients. With tuneable electro-anatomy, the 3D printed cochleae can replicate clinical scenarios of electric field imaging profiles at the off-stimuli positions. The co-modelling framework demonstrated autonomous and robust predictions of patient profiles or cochlear geometry, unfolded the electro-anatomical factors causing current spread, assisted on-demand printing for implant testing, and inferred patients’ in vivo cochlear tissue resistivity (estimated mean = 6.6 kΩcm). We anticipate our framework will facilitate physical modelling and digital twin innovations for neuromodulation implants. Current spread hampers the efficacy of neuromodulation, while existing animal, in vitro and in silico models have failed to give patient-centric insights. Here the authors employ 3D printing and machine learning to advance clinical predictions of current spread for cochlear implant patients.

SUBMITTER: Lei I

PROVIDER: S-EPMC8556326 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectivesThis study aimed to create an objective predictive model for assessing the functional status of the cochlear nerve (CN) in individual cochlear implant (CI) users.DesignStudy participants included 23 children with cochlear nerve deficiency (CND), 29 children with normal-sized CNs (NSCNs), and 20 adults with various etiologies of hearing loss. Eight participants were bilateral CI users and were tested in both ears. As a result, a total of 80 ears were tested in this study. All participants used Cochlear Nucleus CIs in their test ears. For each participant, the CN refractory recovery function and input/output (I/O) function were measured using electrophysiological measures of the electrically evoked compound action potential (eCAP) at three electrode sites across the electrode array. Refractory recovery time constants were estimated using statistical modeling with an exponential decay function. Slopes of I/O functions were estimated using linear regression. The eCAP parameters used as input variables in the predictive model were absolute refractory recovery time estimated based on the refractory recovery function, eCAP threshold, slope of the eCAP I/O function, and negative-peak (i.e., N1) latency. The output variable of the predictive model was CN index, an indicator for the functional status of the CN. Predictive models were created by performing linear regression, support vector machine regression, and logistic regression with eCAP parameters from children with CND and the children with NSCNs. One-way analysis of variance with post hoc analysis with Tukey's honest significant difference criterion was used to compare study variables among study groups.ResultsAll three machine learning algorithms created two distinct distributions of CN indices for children with CND and children with NSCNs. Variations in CN index when calculated using different machine learning techniques were observed for adult CI users. Regardless of these variations, CN indices calculated using all three techniques in adult CI users were significantly correlated with Consonant-Nucleus-Consonant word and AzBio sentence scores measured in quiet. The five oldest CI users had smaller CN indices than the five youngest CI users in this study.ConclusionsThe functional status of the CN for individual CI users was estimated by our newly developed analytical models. Model predictions of CN function for individual adult CI users were positively and significantly correlated with speech perception performance. The models presented in this study may be useful for understanding and/or predicting CI outcomes for individual patients.

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3D printed biomimetic cochleae and machine learning co-modelling provides clinical informatics for cochlear implant patients

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