Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Ketogenic Diet Alleviates Colorectal Cancer by Attenuating Macrophage M2 Polarisation Triggered by Oncometabolite MMA Derived From the Gut Microbiota.

Cell proliferation·2026

Same author

Insertion Network for Image Sequence Correspondence Building.

Proceedings of SPIE--the International Society for Optical Engineering·2026

Same author

Simulation of apically grounded cochlear implant stimuli using neural stimulation models.

International journal of computer assisted radiology and surgery·2026

Same author

Multimodal strategies for diagnosing and treating thyroid cancer: Advances in basic and clinical research.

Translational oncology·2026

Same author

DermIDS: Dermatology imaging data structure for scalable and interoperable AI systems.

Proceedings of SPIE--the International Society for Optical Engineering·2026

Same author

Integrating 2D Dermatological Photography with 3D Anatomical Surfaces.

Proceedings of SPIE--the International Society for Optical Engineering·2026

Search research articles

Related Experiment Video

Updated: Mar 14, 2026

Robotic Cochlear Implantation for Direct Cochlear Access

Robotic Cochlear Implantation for Direct Cochlear Access

Published on: June 16, 2022

Automatic selection of the active electrode set for image-guided cochlear implant programming.

Yiyuan Zhao¹, Benoit M Dawant¹, Jack H Noble¹

¹Vanderbilt University , Department of Electrical Engineering and Computer Science, 2301 Vanderbilt Place, Nashville, Tennessee 37235, United States.

Journal of Medical Imaging (Bellingham, Wash.)

|October 6, 2016

Summary

This summary is machine-generated.

Automating cochlear implant (CI) programming improves hearing outcomes by optimizing electrode selection. This AI-driven approach reduces competing neural stimulation, enhancing CI effectiveness for users.

Keywords:

cochlear implant image-guided cochlear implant programming stimulation strategy

More Related Videos

Performing Intracochlear Electrocochleography During Cochlear Implantation

Performing Intracochlear Electrocochleography During Cochlear Implantation

Published on: March 8, 2022

Enhancing Electrode Location Assessment in Cochlear Implantation via Computed Tomography Image Fusion

Enhancing Electrode Location Assessment in Cochlear Implantation via Computed Tomography Image Fusion

Published on: January 17, 2025

Related Experiment Videos

Last Updated: Mar 14, 2026

Robotic Cochlear Implantation for Direct Cochlear Access

Robotic Cochlear Implantation for Direct Cochlear Access

Published on: June 16, 2022

Performing Intracochlear Electrocochleography During Cochlear Implantation

Performing Intracochlear Electrocochleography During Cochlear Implantation

Published on: March 8, 2022

Enhancing Electrode Location Assessment in Cochlear Implantation via Computed Tomography Image Fusion

Enhancing Electrode Location Assessment in Cochlear Implantation via Computed Tomography Image Fusion

Published on: January 17, 2025

Area of Science:

Biomedical Engineering
Neuroscience
Medical Imaging

Background:

Cochlear implants (CIs) restore hearing but can suffer reduced efficacy due to competing neural stimulation from multiple electrodes.
Current image-guided electrode selection requires expert intervention and visualization techniques.
Optimizing electrode activation is crucial for maximizing CI performance and patient outcomes.

Purpose of the Study:

To automate the image-guided active electrode set selection process for cochlear implants.
To develop a cost function that mimics expert heuristics for electrode deactivation.
To estimate cost function parameters using a database of expert selections.

Main Methods:

Developed an automated electrode selection method by optimizing a cost function.
Estimated cost function parameters using a database of expert-selected electrode sets.
Validated the automated approach across different electrode array models from three manufacturers.

Main Results:

The automated electrode selection method successfully generated acceptable active electrode sets in 98.3% of tested subjects.
The approach demonstrated effectiveness across various electrode array designs.
This automation significantly streamlines the CI programming workflow.

Conclusions:

Automated electrode selection using an optimized cost function is a viable and effective method for cochlear implant programming.
This technique significantly improves upon manual selection processes, reducing the need for expert intervention.
The developed system represents a key advancement toward the clinical translation of image-guided CI programming.