3D printed biomimetic cochleae and machine learning co-modelling provides clinical informatics for cochlear implant patients

Iek Man Lei, Chen Jiang, Chon Lok Lei, Simone Rosalie de Rijk, Yu Chuen Tam, Chloe Swords, Michael P. F. Sutcliffe, George G. Malliaras, Manohar Bance () and Yan Yan Shery Huang ()
Additional contact information
Iek Man Lei: University of Cambridge
Chen Jiang: University of Cambridge
Chon Lok Lei: University of Macau
Simone Rosalie de Rijk: University of Cambridge
Yu Chuen Tam: Addenbrookes Hospital
Chloe Swords: Development and Neurosciences
Michael P. F. Sutcliffe: University of Cambridge
George G. Malliaras: University of Cambridge
Manohar Bance: University of Cambridge
Yan Yan Shery Huang: University of Cambridge

Nature Communications, 2021, vol. 12, issue 1, 1-12

Abstract: Abstract 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.

Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-021-26491-6 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26491-6

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-021-26491-6

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().