Evidence map›Paper›PMID 34716306›Full record

ArticleNature communications2021

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, Yan Yan Shery Huang

Open access · goldAbstract readVideo-Audio Media
In one paragraph

Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
4.1field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed, 57 citations in OpenAlex.

  1. Three-dimensional printing and cochlear implantation: a systematic review.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026
    Review
  2. Article
  3. Review
  4. Article
  5. 3D Printing for Neural Repair: Bridging the Gap in Regenerative Medicine.Advanced materials (Deerfield Beach, Fla.) · 2025
    Article
  6. Article
  7. Review
  8. 3D printed microneedles: revamping transdermal drug delivery systems.Drug delivery and translational research · 2025
    Review
  9. Emerging Wearable Acoustic Sensing Technologies.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Review
  10. Article
  11. Review
  12. Review
  13. Review
  14. Review
  15. Review
  16. 3D Bioprinting in Otolaryngology: A Review.Advanced healthcare materials · 2023
    Review
  17. Review
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 3 institutions in 3 countries.

Iek Man LeiDepartment of Engineering, University of Cambridge, Cambridge, United Kingdom.
Chen JiangDepartment of Engineering, University of Cambridge, Cambridge, United Kingdom.
Chon Lok LeiInstitute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macau.ORCID 0000-0003-0904-554X
Simone Rosalie de RijkDepartment of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom.ORCID 0000-0001-7962-5473
Yu Chuen TamEmmeline Centre for Hearing Implants, Addenbrookes Hospital, Cambridge, United Kingdom.ORCID 0000-0001-6473-4538
Chloe SwordsDepartment of Physiology, Development and Neurosciences, Cambridge, United Kingdom.ORCID 0000-0002-0431-4491
Michael P F SutcliffeDepartment of Engineering, University of Cambridge, Cambridge, United Kingdom.
George G MalliarasDepartment of Engineering, University of Cambridge, Cambridge, United Kingdom.ORCID 0000-0002-4582-8501
Manohar BanceDepartment of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom. mlb59@cam.ac.uk.
Yan Yan Shery HuangDepartment of Engineering, University of Cambridge, Cambridge, United Kingdom. yysh2@cam.ac.uk.ORCID 0000-0003-2619-730X
University of Cambridge · GBAddenbrooke's Hospital · GBUniversity of Macau · MO

Funding

Wellcome TrustWellcome Trust (Wellcome) 204845/Z/16/Z
6 · The paper itself

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.

Indexed as

Biomimetic MaterialsCochlear ImplantsMachine LearningPrinting, Three-DimensionalCochleaCochlear ImplantationDielectric SpectroscopyHumansNeural Networks, ComputerPrecision MedicineReproducibility of ResultsX-Ray Microtomography

Identifiers

PMID34716306
PMCPMC8556326
OpenAlexW3209298262

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.