Related Experiment Video
Updated: Aug 6, 2026

Testing a Cochlear Implant Electrode Insertion Training System for Optimal Electrode Array Placement in Different Inner Ear Anatomies
Published on: February 6, 2026
3D-Printed Electroanatomic Twins of Cadaveric Cochleae: A Platform for Cochlear Implant Testing
Chloe Swords1,2, Iwan Vaughan Roberts2, Sita Tarini Clark2
1Department of Physiology, Development and Neuroscience University of Cambridge Cambridge UK.
Objectives:
Cochlear implants (CIs) can restore hearing to individuals with severe deafness, yet clinical outcomes vary widely because it remains difficult to predict how electrical currents interact with the unique anatomy of each cochlea. A central challenge in auditory rehabilitation is the lack of experimental systems that capture both the anatomical fidelity and the electrical properties of the human cochlea. To address this need, we developed electroanatomic twins: three-dimensional, 3D printed models of human cochleae that incorporate anatomically accurate features of the otic capsule and conductive biomimetic structures designed to reproduce tissue resistivity.
Methods:
Electroanatomic cochlea twins were generated from high resolution microCT scans of cadaveric specimens and from clinical CT scans, and they allowed reproducible, high-resolution mapping of voltage fields during CI stimulation.
Results:
These models reproduced intraoperative spread of current profiles with a root mean square error below 0.1 kΩ, closely matched cadaveric impedance spectra, and recapitulated spatial voltage distributions under monopolar, bipolar, and tripolar stimulation.
Conclusion:
By combining anatomical precision with electrical realism, electroanatomic twins provide a robust translational platform for systematic testing of electrode designs and programming strategies. This approach has the potential to guide individualized CI programming and thereby improve auditory outcomes, while also establishing a generalisable framework for modeling electroanatomic interactions that could accelerate the development of personalized bioelectronic therapies.
Level Of Evidence:
Level 3.

