3D printed biomimetic cochleae and machine learning co-modelling provides clinical informatics for cochlear implant patients.
Lei, Iek Man
Sutcliffe, Michael PF
Springer Science and Business Media LLC
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Lei, I. M., Jiang, C., Lei, C. L., de Rijk, S. R., Tam, Y. C., Swords, C., Sutcliffe, M. P., et al. (2021). 3D printed biomimetic cochleae and machine learning co-modelling provides clinical informatics for cochlear implant patients.. Nat Commun, 12 (1) https://doi.org/10.1038/s41467-021-26491-6
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.
Cochlea, Humans, Cochlear Implantation, Reproducibility of Results, Cochlear Implants, Biomimetic Materials, X-Ray Microtomography, Dielectric Spectroscopy, Printing, Three-Dimensional, Machine Learning, Precision Medicine, Neural Networks, Computer
Wellcome Trust (204845/Z/16/Z)
European Research Council (758865)
External DOI: https://doi.org/10.1038/s41467-021-26491-6
This record's URL: https://www.repository.cam.ac.uk/handle/1810/332230
Attribution 4.0 International
Licence URL: https://creativecommons.org/licenses/by/4.0/