Personalization of Hearing Aids through Bayesian Preference Elicitatio
Not yet recruiting
- Conditions
- deafnesshearing loss10019243
- Registration Number
- NL-OMON32265
- Lead Sponsor
- Academisch Medisch Centrum
- Brief Summary
Not available
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- Pending
- Sex
- Not specified
- Target Recruitment
- 35
Inclusion Criteria
Normal hearing (threshold <20 dB HL)
Moderately sensorineurally hearing impaired (20
Exclusion Criteria
large hearing loss (threshold>75 dB HL)
hearing impairment other then sensorineural
age over 80
mental/physical problems
Study & Design
- Study Type
- Observational non invasive
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method <p>The primary outcome is the viability of the newly developed procedure for<br /><br>clinical practice.</p><br>
- Secondary Outcome Measures
Name Time Method <p>Secondary outcome variables are the preference of hearing-impaired listeners<br /><br>for a hearing-aid setting, as well as speech intelligibility for this setting.</p><br>
Related Research Topics
Explore scientific publications, clinical data analysis, treatment approaches, and expert-compiled information related to the mechanisms and outcomes of this trial. Click any topic for comprehensive research insights.
How does Bayesian preference elicitation improve hearing aid personalization compared to traditional audiometric fitting?
What auditory biomarkers are used in NL-OMON32265 to predict patient response to hearing aid adjustments?
What adverse events are associated with Bayesian-based hearing aid personalization in sensorineural hearing loss?
How does HearClip's Bayesian algorithm compare to machine learning approaches in audiological device optimization?
Which molecular pathways in cochlear function might influence outcomes of hearing aid personalization strategies?