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
MetadataShow full item record
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.
Biomimetic Materials, Cochlea, Cochlear Implantation, Cochlear Implants, Dielectric Spectroscopy, Humans, Machine Learning, Neural Networks, Computer, Precision Medicine, Printing, Three-Dimensional, Reproducibility of Results, X-Ray Microtomography
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/
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