Computer-Guided Electrode Selection for Hearing Loss

Overseen By Elad Sagi

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: NYU Langone Health

Disqualifiers: Communicative, cognitive disorders, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: 8

Duration: 24 weeks

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial explores ways to improve speech understanding for people with cochlear implants by using computer-guided models to select the best electrodes for each individual. Researchers believe these custom settings will enhance speech comprehension more effectively than standard settings. The trial includes two groups: one with cochlear implants and another with normal hearing for listening tests. Ideal participants are adults with a cochlear implant who have used it for at least one year and have no other communication or cognitive disorders. As an unphased trial, this study offers participants the chance to contribute to innovative research that could enhance cochlear implant technology.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications.

What prior data suggests that this method is safe for cochlear implant users?

Research has shown that cochlear implants are generally safe for individuals with severe hearing loss. Studies indicate that these devices are durable, with over 96% functioning well after 10 years and 91% after 20 years, demonstrating their long-term reliability.

Additionally, evidence shows that cochlear implants enhance speech understanding and overall quality of life, even for older adults. These benefits come with few serious complications, making the implants manageable. One study found that cochlear implants are safe and effective for improving hearing.

Overall, the data suggests that cochlear implants are a safe choice with a strong history of enhancing hearing. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about this trial because it aims to refine how cochlear implants are used for hearing loss. Unlike traditional approaches, which often rely on broad or uniform settings for all patients, this trial focuses on computer-guided electrode selection. This method could personalize and optimize the implant experience, potentially leading to clearer sound perception and improved quality of life for recipients. By tailoring the electrode settings to each individual's needs, researchers hope to enhance the effectiveness of cochlear implants beyond what current methods can achieve.

What evidence suggests that this computer-guided electrode selection is effective for improving speech understanding in cochlear implant users?

Research has shown that cochlear implants can greatly improve hearing and speech understanding for people with severe hearing loss. In this trial, participants with cochlear implants will undergo tests to assess their hearing and speech recognition. Studies indicate that early implantation leads to better long-term hearing and even aids in phone use. One study found that practicing listening with cochlear implants improves speech recognition in adults. Additionally, cochlear implants have been linked to better educational outcomes and quality of life over time. These findings suggest that cochlear implants effectively enhance hearing and speech abilities for those who need them. ^{3 4 6 7 8}

Who Is on the Research Team?

Elad Sagi, MD

Principal Investigator

NYU Langone Health

Are You a Good Fit for This Trial?

This trial is for adults over 18 with hearing loss who have had a cochlear implant for at least one year. Participants must speak English, be able to give informed consent, and not have other communication or cognitive disorders.

Exclusion Criteria

Not meeting the inclusion criteria above

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Baseline

Initial assessments and baseline measurements are conducted

1 day

1 visit (in-person)

Treatment

Participants undergo testing with model-recommended and standard CI settings over multiple visits

24 weeks

7 visits (in-person)

Follow-up

Participants are monitored for safety and effectiveness after treatment

4 weeks

What Are the Treatments Tested in This Trial?

Interventions

• Cochlear implant

Trial Overview

The study is testing a computer model that suggests the best settings for cochlear implants in individuals. It aims to see if these personalized settings improve speech understanding more than standard settings do.

How Is the Trial Designed?

Group I: Cochlear implant subjects Experimental Treatment 1 Intervention

Participates in 7 visits over a six-month duration. Subjects will be given several tests that require them to listen to sounds presented to their cochlear implant and answer questions about those sounds.

Group II: Normal hearing subjects Active Control 1 Intervention

Participates in 1 visit lasting 3 hours. Will be given several tests that require you to listen to sounds and answer questions about those sounds. The sounds will be distorted in ways that approximate how a cochlear implant sounds.

Who Is Running the Clinical Trial?

NYU Langone Health

Lead Sponsor

Trials

1,431

Recruited

838,000+

National Institute on Deafness and Other Communication Disorders (NIDCD)

Collaborator

Trials

377

Recruited

190,000+

Published Research Related to This Trial

The proposed image-guided cochlear implant programming (IGCIP) system uses CT images to identify and reduce cross-electrode neural stimulation overlap, leading to significantly improved hearing outcomes for patients with cochlear implants.

An automated technique for selecting electrode configurations has been developed, which performs as well as previous methods but is more generic and requires fewer parameters, enhancing the efficiency of the programming process.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Selecting electrode configurations for image-guided cochlear implant programming using template matching. Zhang, D., Zhao, Y., Noble, JH., et al.[2020]

A machine learning-based referral guideline for cochlear implant candidacy evaluation demonstrated higher sensitivity (0.96) and specificity (1.00) compared to the traditional 60/60 guideline, which had a sensitivity of 0.91 and specificity of 0.42, based on a study of 772 adults.

The machine learning model showed consistent accuracy (0.96) across multiple bootstrapped iterations, indicating its potential for generalizability and effectiveness in identifying candidates for cochlear implants.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Machine Learning Approach for Screening Cochlear Implant Candidates: Comparing With the 60/60 Guideline. Patro, A., Perkins, EL., Ortega, CA., et al.[2023]

Cochlear implants, which send electrical impulses directly to the cochlear nerve, are a significant advancement for patients with severe hearing loss who cannot benefit from traditional hearing aids, and they are now being used in pediatric patients as well.

A multidisciplinary team approach is crucial for the successful implantation and follow-up care of cochlear implants, with 29 patients having received implants using a preferred multi-channel intracochlear system, demonstrating a positive impact on their quality of life.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

[Cochlear implants in children]. Garabedian, EN., Moatti, L., Roger, G., et al.[2006]

An automated technique for selecting cochlear implant electrodes has been developed, which can identify and deactivate electrodes that cause competing stimulation, leading to improved hearing outcomes.

In tests with various electrode array models from three manufacturers, this automated method successfully generated effective electrode sets in 98.3% of subjects, indicating its potential for clinical use in enhancing cochlear implant programming.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Automatic selection of the active electrode set for image-guided cochlear implant programming. Zhao, Y., Dawant, BM., Noble, JH.[2020]

A study analyzing adverse event reports for cochlear implants found that most incidents involved patient injury (16,736 cases) and device malfunction (10,760 cases), highlighting areas for potential safety improvements.

Using machine learning algorithms, researchers achieved high prediction accuracy (up to 88.6%) in identifying the cochlear implant manufacturer and type of adverse event based on the descriptions, suggesting that data analysis can enhance understanding of device performance and safety.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Machine learning for pattern detection in cochlear implant FDA adverse event reports. Crowson, MG., Hamour, A., Lin, V., et al.[2021]

This study introduces a new evaluation protocol for research platforms (RPs) used in cochlear implants (CIs) and hearing aids (HAs) to ensure their operational safety and reliability, which is crucial for advancing hearing restoration technologies.

The proposed two-phase analysis includes an acoustic phase to assess sound output safety and a parameter phase to evaluate the reliability of electrical stimulation, providing a structured approach to improve the design and testing of hearing devices.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

An evaluation framework for research platforms to advance cochlear implant/hearing aid technology: A case study with CCi-MOBILE. Shekar, RCMC., Hansen, JHL.[2022]

Cochlear implantation (CI) is a widely successful procedure, and the development of computational models can significantly enhance surgical planning by predicting patient-specific outcomes based on neural responses.

The study found that the positioning of the electrode within the cochlea greatly influences the performance of the CI, with lateral insertion being more effective, especially in cases of auditory nerve degeneration, suggesting tailored approaches for individual patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Towards a Complete In Silico Assessment of the Outcome of Cochlear Implantation Surgery. Mangado, N., Ceresa, M., Benav, H., et al.[2022]

A review of 31,857 adverse events from cochlear implants revealed that implants from manufacturer C had a higher incidence of complications, including infection (0.97%), cerebrospinal fluid leak (0.07%), extrusion (0.44%), and facial nerve stimulation (0.11%).

Manufacturer B's implants were linked to a higher rate of meningitis (0.07%), highlighting the importance of considering both patient risk factors and the specific manufacturer when evaluating the safety of cochlear implants.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Complications of cochlear implants: a MAUDE database study. Jinka, S., Wase, S., Jeyakumar, A.[2023]

In a review of 38 studies involving 3177 cochlear implant cases, the occurrence rate of electrode tip fold-over was found to be 1.57%, primarily associated with pre-curved electrodes.

The review also revealed a significant rate of electrode scalar deviation at 22.38%, with pre-curved electrodes showing a much higher occurrence (32%) compared to lateral-wall electrodes (6.7%), suggesting a need to reconsider the use of pre-curved electrodes in clinical practice for better patient outcomes.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Review on cochlear implant electrode array tip fold-over and scalar deviation. Dhanasingh, A., Jolly, C.[2022]

A retrospective analysis of cochlear implant adverse events over a 10-year period revealed a significant increase in cases with multiple or unknown causes, particularly gradual idiopathic loss of performance, indicating a shift in the nature of reported issues.

Despite improvements in manufacturing and surgical techniques, the rise in complex adverse events suggests a need for further research to understand and address these idiopathic cases to enhance cochlear implant outcomes.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Trends in cochlear implant complications: implications for improving long-term outcomes. Causon, A., Verschuur, C., Newman, TA.[2013]

An automatic method for measuring cochlear duct length (CDL) using CT images was evaluated in 309 ears, showing that manual measurements by experts often underestimate cochlea size and have high variability, with a mean difference of 1.15 mm between observers.

The automatic measurement approach provides more consistent and reliable results, which could improve the selection of cochlear implant electrode arrays, ultimately optimizing hearing outcomes for patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Automatic Cochlear Duct Length Estimation for Selection of Cochlear Implant Electrode Arrays. Rivas, A., Cakir, A., Hunter, JB., et al.[2022]

The study evaluated a method for predicting the angular insertion depth of cochlear implants in 50 patients, finding a theoretical prediction error of 5 degrees, but a much larger clinical prediction error of 70 degrees when including cases with extracochlear electrodes.

While the prediction method can assist in preoperative planning for cochlear implants, it has limitations, such as not being able to accurately predict extracochlear electrode placements and requiring training for effective use.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Clinical Applicability of a Preoperative Angular Insertion Depth Prediction Method for Cochlear Implantation. Rathgeb, C., Demattè, M., Yacoub, A., et al.[2020]

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