Computer-Guided Electrode Selection for Hearing Loss
Recruiting in Palo Alto (17 mi)
Age: 18+
Sex: Any
Travel: May Be Covered
Time Reimbursement: Varies
Trial Phase: Academic
Recruiting
Sponsor: NYU Langone Health
Disqualifiers: Communicative, cognitive disorders, others
No Placebo Group
Trial Summary
What is the purpose of this trial?The goal of the present study is to use computationally driven models of speech understanding in CI users to guide the search for which combination of active electrodes can yield the best speech understanding for a specific patient. It is hypothesized that model-recommended settings will result in significantly better speech understanding than standard-of-care settings.
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 data supports the effectiveness of the treatment Cochlear implant?
Research shows that using a computer-guided method to select which electrodes in a cochlear implant are active can significantly improve hearing outcomes. This method helps avoid overlapping stimulation of nerve pathways, which can otherwise reduce the effectiveness of the implant.
12345Is the computer-guided electrode selection for hearing loss generally safe for humans?
Cochlear implants, which are similar to the treatment being studied, have been associated with some adverse events like patient injury and device malfunction, but serious events like death are rare. Safety data from various studies suggest that while there are risks, they are generally well understood and managed.
678910How does the computer-guided electrode selection for cochlear implants differ from other treatments for hearing loss?
This treatment is unique because it uses computer-guided technology to automatically select which electrodes in a cochlear implant should be active, based on imaging techniques. This helps avoid overlapping stimulation of nerve pathways, which can improve hearing outcomes compared to traditional methods where an expert manually selects the electrodes.
2341112Eligibility Criteria
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.Inclusion Criteria
I am over 18, speak English, have normal hearing, and can consent.
I am over 18, have a cochlear implant for at least a year, speak English, and can consent.
Exclusion Criteria
Not meeting the inclusion criteria above
Trial Timeline
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
Participant Groups
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.
2Treatment groups
Experimental Treatment
Active Control
Group I: Cochlear implant subjectsExperimental Treatment1 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 subjectsActive Control1 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.
Find a Clinic Near You
Research Locations NearbySelect from list below to view details:
NYU Langone HealthNew York, NY
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Who Is Running the Clinical Trial?
NYU Langone HealthLead Sponsor
National Institute on Deafness and Other Communication Disorders (NIDCD)Collaborator
References
Machine Learning Approach for Screening Cochlear Implant Candidates: Comparing With the 60/60 Guideline. [2023]To develop a machine learning-based referral guideline for patients undergoing cochlear implant candidacy evaluation (CICE) and to compare with the widely used 60/60 guideline.
Automatic selection of the active electrode set for image-guided cochlear implant programming. [2020]Cochlear implants (CIs) are neural prostheses that restore hearing by stimulating auditory nerve pathways within the cochlea using an implanted electrode array. Research has shown when multiple electrodes stimulate the same nerve pathways, competing stimulation occurs and hearing outcomes decline. Recent clinical studies have indicated that hearing outcomes can be significantly improved by using an image-guided active electrode set selection technique we have designed, in which electrodes that cause competing stimulation are identified and deactivated. In tests done to date, an expert is needed to perform the electrode selection step with the assistance of a method to visualize the spatial relationship between electrodes and neural sites determined using image analysis techniques. We propose to automate the electrode selection step by optimizing a cost function that captures the heuristics used by the expert. Further, we propose an approach to estimate the values of parameters used in the cost function using an existing database of expert electrode selections. We test this method with different electrode array models from three manufacturers. Our automatic approach generates acceptable active electrode sets in 98.3% of the subjects tested. This approach represents a crucial step toward clinical translation of our image-guided CI programming system.
Automatic Cochlear Duct Length Estimation for Selection of Cochlear Implant Electrode Arrays. [2022]Cochlear duct length (CDL) can be automatically measured for custom selection of cochlear implant (CI) electrode arrays.
Selecting electrode configurations for image-guided cochlear implant programming using template matching. [2020]Cochlear implants (CIs) are neural prostheses that restore hearing using an electrode array implanted in the cochlea. After implantation, the CI processor is programmed by an audiologist. One factor that negatively impacts outcomes and can be addressed by programming is cross-electrode neural stimulation overlap (NSO). We have proposed a system to assist the audiologist in programming the CI that we call image-guided CI programming (IGCIP). IGCIP permits using CT images to detect NSO and recommend deactivation of a subset of electrodes to avoid NSO. We have shown that IGCIP significantly improves hearing outcomes. Most of the IGCIP steps are robustly automated but electrode configuration selection still sometimes requires manual intervention. With expertise, distance-versus-frequency curves, which are a way to visualize the spatial relationship learned from CT between the electrodes and the nerves they stimulate, can be used to select the electrode configuration. We propose an automated technique for electrode configuration selection. A comparison between this approach and one we have previously proposed shows that our method produces results that are as good as those obtained with our previous method while being generic and requiring fewer parameters.
[Cochlear implants in children]. [2006]The development of surgically implantable hearing aids that are placed directly in the cochlea where they send electrical impulses to the cochlear nerve is a major break-through for patients whose hearing loss is so severe as to make conventional electroacoustic hearing aids ineffectual. Initially used only in adults, this method has gradually been extended to pediatric patients. To benefit from a cochlear implant, the patient must fulfill a number of criteria which are specified in this article. Following preoperative investigations, the decision is taken during a meeting of all the care providers involved, i.e., the surgeon, ENT phoniatrist or audiophonologist, hearing aid specialist, special education provider, speech therapist, psychologist, and other members of the health care staff. Team work is thus essential both before and after the procedure. The implant selected can be intracochlear or extracochlear and single-channel (one electrode) or multi-channel (several electrodes). Each team selects the implantation technique and type of implant they use according to their preferences and specific criteria. The authors use a multi-channel intracochlear system except in the rare instances where complete ossification of the cochlea requires use of an intracochlear mono-channel system. They have inserted implants in 29 patients to date. The cochlear implant has unquestionably had a significant impact of the life of these patients.
Review on cochlear implant electrode array tip fold-over and scalar deviation. [2022]Determine the occurrence rate of cochlear implant (CI) electrode tip fold-over and electrode scalar deviation as reported in patient cases with different commercial electrode types.
Trends in cochlear implant complications: implications for improving long-term outcomes. [2013]To review worldwide data on cochlear implant adverse events, test for significant trends over a 10-year period and discuss possible reasons behind such trends. To evaluate the suitability of the Manufacturer and User Facility Device Experience (MAUDE) database for analysis of trends in cochlear implant adverse events.
Machine learning for pattern detection in cochlear implant FDA adverse event reports. [2021]Importance: Medical device performance and safety databases can be analyzed for patterns and novel opportunities for improving patient safety and/or device design. Objective: The objective of this analysis was to use supervised machine learning to explore patterns in reported adverse events involving cochlear implants. Design: Adverse event reports for the top three CI manufacturers were acquired for the analysis. Four supervised machine learning algorithms were used to predict which adverse event description pattern corresponded with a specific cochlear implant manufacturer and adverse event type. Setting: U.S. government public database. Participants: Adult and pediatric cochlear patients. Exposure: Surgical placement of a cochlear implant. Main Outcome Measure: Classification prediction accuracy (% correct predictions). Results: Most adverse events involved patient injury (n = 16,736), followed by device malfunction (n = 10,760), and death (n = 16). The random forest, linear SVC, naïve Bayes and logistic algorithms were able to predict the specific CI manufacturer based on the adverse event narrative with an average accuracy of 74.8%, 86.0%, 88.5% and 88.6%, respectively. Conclusions & relevance: Using supervised machine learning algorithms, our classification models were able to predict the CI manufacturer and event type with high accuracy based on patterns in adverse event text descriptions.
Complications of cochlear implants: a MAUDE database study. [2023]A retrospective cross-sectional analysis was conducted of the US Food and Drug Administration's MAUDE (Manufacturer and User Facility Device Experience) database, to evaluate the complication profile of cochlear implantation according to manufacturer.
An evaluation framework for research platforms to advance cochlear implant/hearing aid technology: A case study with CCi-MOBILE. [2022]Cochlear implants (CIs) and hearing aids (HAs) are advanced assistive hearing devices that perform sound processing to achieve acoustic to acoustic/electrical stimulation, thus enabling the prospects for hearing restoration and rehabilitation. Since commercial CIs/HAs are typically constrained by manufacturer design/production constraints, it is necessary for researchers to use research platforms (RPs) to advance algorithms and conduct investigational studies with CI/HA subjects. While previous CI/HA research platforms exist, no study has explored establishing a formal evaluation protocol for the operational safety and reliability of RPs. This study proposes a two-phase analysis and evaluation paradigm for RPs. In the acoustic phase 1 step, a signal processing acoustic space is explored in order to present a sampled set of audio input content to explore the safety of the resulting output electric/acoustic stimulation. In the parameter phase 2 step, the configurable space for realizable electrical stimulation pulses is determined, and overall stimulation reliability and safety are evaluated. The proposed protocol is applied and demonstrated using Costakis Cochlear Implant Mobile. Assessment protocol observations, results, and additional best practices for subsampling of the acoustic and parameter test spaces are discussed. The proposed analysis-evaluation protocol establishes a viable framework for assessing RP operational safety and reliability. Guidelines for adapting the proposed protocol to address variability in RP configuration due to experimental factors such as custom algorithms, stimulation techniques, and/or individualization are also considered.
Towards a Complete In Silico Assessment of the Outcome of Cochlear Implantation Surgery. [2022]Cochlear implantation (CI) surgery is a very successful technique, performed on more than 300,000 people worldwide. However, since the challenge resides in obtaining an accurate surgical planning, computational models are considered to provide such accurate tools. They allow us to plan and simulate beforehand surgical procedures in order to maximally optimize surgery outcomes, and consequently provide valuable information to guide pre-operative decisions. The aim of this work is to develop and validate computational tools to completely assess the patient-specific functional outcome of the CI surgery. A complete automatic framework was developed to create and assess computationally CI models, focusing on the neural response of the auditory nerve fibers (ANF) induced by the electrical stimulation of the implant. The framework was applied to evaluate the effects of ANF degeneration and electrode intra-cochlear position on nerve activation. Results indicate that the intra-cochlear positioning of the electrode has a strong effect on the global performance of the CI. Lateral insertion provides better neural responses in case of peripheral process degeneration, and it is recommended, together with optimized intensity levels, in order to preserve the internal structures. Overall, the developed automatic framework provides an insight into the global performance of the implant in a patient-specific way. This enables to further optimize the functional performance and helps to select the best CI configuration and treatment strategy for a given patient.
Clinical Applicability of a Preoperative Angular Insertion Depth Prediction Method for Cochlear Implantation. [2020]Evaluation of the accuracy and clinical applicability of a single measure cochlear implant angular insertion depth prediction method.