3D printed biomimetic cochleae and machine learning co-modelling provides clinical informatics for cochlear implant patients
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Cochlear implants (CIs) restore hearing in patients with severe to profound deafness by delivering electrical stimuli inside the cochlea. Understanding CI stimulus spread, and how it correlates to patient-dependent factors, is hampered by the poor accessibility of the inner ear and by the lack of suitable in vitro, in vivo or in silico models. Here, we present 3D printing-neural network co-modelling for interpreting clinical electric field imaging (EFI) profiles of CI patients. With tuneable electro-anatomy, the 3D printed cochleae were shown to replicate clinical scenarios of EFI profiles at the off-stimuli positions. The co-modelling framework demonstrated autonomous and robust predictions of patient EFI or cochlear geometry, unfolded the electro-anatomical factors causing CI stimulus spread, assisted on-demand printing for CI testing, and inferred patients’ in vivo cochlear tissue resistivity (estimated mean = 6.6 kΩcm) by CI telemetry. We anticipate our framework will facilitate physical modelling and digital twin innovations for electrical prostheses in healthcare.
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Wellcome Trust (204845/Z/16/Z)