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dc.contributor.authorLei, Iek Man
dc.contributor.authorJiang, Chen
dc.contributor.authorLei, Chon Lok
dc.contributor.authorde Rijk, Simone Rosalie
dc.contributor.authorTam, Yu Chuen
dc.contributor.authorSwords, Chloe
dc.contributor.authorSutcliffe, Michael PF
dc.contributor.authorMalliaras, George G
dc.contributor.authorBance, Manohar
dc.contributor.authorHuang, Yan Yan Shery
dc.date.accessioned2022-01-06T12:57:01Z
dc.date.available2022-01-06T12:57:01Z
dc.date.issued2021-10-29
dc.identifier.issn2041-1723
dc.identifier.otherPMC8556326
dc.identifier.other34716306
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/332230
dc.description.abstractCochlear 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.
dc.languageeng
dc.publisherSpringer Science and Business Media LLC
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceessn: 2041-1723
dc.sourcenlmid: 101528555
dc.subjectCochlea
dc.subjectHumans
dc.subjectCochlear Implantation
dc.subjectReproducibility of Results
dc.subjectCochlear Implants
dc.subjectBiomimetic Materials
dc.subjectX-Ray Microtomography
dc.subjectDielectric Spectroscopy
dc.subjectPrinting, Three-Dimensional
dc.subjectMachine Learning
dc.subjectPrecision Medicine
dc.subjectNeural Networks, Computer
dc.title3D printed biomimetic cochleae and machine learning co-modelling provides clinical informatics for cochlear implant patients.
dc.typeArticle
dc.date.updated2022-01-06T12:57:00Z
prism.issueIdentifier1
prism.publicationNameNat Commun
prism.volume12
dc.identifier.doi10.17863/CAM.79676
dcterms.dateAccepted2021-10-06
rioxxterms.versionofrecord10.1038/s41467-021-26491-6
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidLei, Chon Lok [0000-0003-0904-554X]
dc.contributor.orcidde Rijk, Simone Rosalie [0000-0001-7962-5473]
dc.contributor.orcidTam, Yu Chuen [0000-0001-6473-4538]
dc.contributor.orcidSwords, Chloe [0000-0002-0431-4491]
dc.contributor.orcidMalliaras, George G [0000-0002-4582-8501]
dc.contributor.orcidHuang, Yan Yan Shery [0000-0003-2619-730X]
dc.identifier.eissn2041-1723
pubs.funder-project-idWellcome Trust (204845/Z/16/Z)
pubs.funder-project-idEuropean Research Council (758865)
cam.issuedOnline2021-10-29


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International