3D printed biomimetic cochleae and machine learning co-modelling provides clinical informatics for cochlear implant patients.
Lei, Iek Man
Springer Science and Business Media LLC
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Lei, I. M., Jiang, C., Lei, C. L., De Rijk, S., Tam, Y. C., Swords, C., Sutcliffe, M., 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
Funder: W D Armstrong Trust; the Macao Postgraduate Scholarship Fund
Funder: UM Macao Fellowship; the Clarendon Scholarship Fund
Funder: Baroness de Turckheim Fund, Trinity College Cambridge
Funder: the Cambridge Hearing Trust; the Evelyn Trust
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.
Article, /692/700, /692/308/1426, /692/308/575, /639/166/985, /119, /9, /123, /128, /139, article
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/1810/330049