Automatic Sound Management 3.0 in a Single-unit Audio Processor

NCT ID: NCT04918654

Last Updated: 2025-11-21

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

20 participants

Study Classification

OBSERVATIONAL

Study Start Date

2021-06-25

Study Completion Date

2023-12-11

Brief Summary

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Cochlear implants are routine treatment for people with severe hearing loss. This study tests the RONDO 3, MED-EL's newest single-unit audio processor which comes with new features (Beamformer, Wind-Noise reduction, Ambient noise reduction, Transient noise reduction, Adaptive intelligence).

Detailed Description

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MED-EL Cochlear Implants (CI) provide auditory sensations via electrical stimulation of the auditory pathways for severely to profoundly hearing-impaired individuals who obtain little or no benefit from acoustic amplification in the best aided condition. Front-end processing of acoustic signals picked up by the audio processor is routinely applied to provide optimal hearing performance under varying listening conditions. Automatic Sound Management (ASM) was introduced by MED-EL with the TEMPO+ audio processor and provided advanced compression and automatic gain management. With the SONNET audio processor, MED-EL introduced ASM 2.0, which added wind noise reduction and microphone directionality to the front-end processing. With the SONNET 2 audio processor, ASM 3.0 was implemented and the features ambient noise reduction, transient noise reduction and an adaptive intelligence (AI) were added. The RONDO 3 implements now ASM 3.0.

This study investigates the impact of ASM 3.0 as implemented in the RONDO 3 on CI users' speech performance and their subjective quality of hearing.

Conditions

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Cochlear Implants

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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Study Subjects

No interventions assigned to this group

Eligibility Criteria

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Inclusion Criteria

* A minimum of 18 years old
* Experienced user of a RONDO 3 (≥ 3 months) for the ear to be tested
* Post-lingual onset of severe to profound sensory-neural hearing loss on the ear to be tested
* A minimum of 40% speech recognition in the Freiburg Monosyllables Test in quiet at 65 dB SPL for the ear to be tested (at the last time tested in clinical routine)
* Signed and dated Informed Consent Form (ICF) before the start of any study-specific procedure.

* CI user with contralateral hearing equal to or better than 30 dB (pure-tone average over the following frequencies: 250, 500, 1000, and 2000 Hz)
* Implanted with C40X, or C40C on the ear to be tested
* Implanted with an Auditory Brainstem Implant (ABI) or split electrode array
* Known allergic reactions to components of the investigational medical device (RONDO 3) or the SONNET 2
* Anything that, in the opinion of the Investigator, would (a) place the subject at increased risk, (b) preclude the subject's full compliance with or completion of the study
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Hannover Medical School

OTHER

Sponsor Role collaborator

MED-EL Elektromedizinische Geräte GesmbH

INDUSTRY

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Andreas Büchner, Prof. Dr.

Role: PRINCIPAL_INVESTIGATOR

Hals-Nasen-Ohrenklinik der Medizinischen Hochschule Hannover

Locations

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MHH - Medizinische Hochschule Hannover

Hanover, , Germany

Site Status

Countries

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Germany

References

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Buchner A, Rottmann T, Lenarz T, Lesinski-Schiedat A, Aschbacher E, Fruhauf F, Bracker T, Billinger-Finke M. Clinical improvement of speech perception in noise with Automatic Sound Management 3.0. Hear Res. 2025 Oct 4;468:109445. doi: 10.1016/j.heares.2025.109445. Online ahead of print.

Reference Type RESULT
PMID: 41115327 (View on PubMed)

Other Identifiers

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RND3_MED-EL_CRD_2020_03

Identifier Type: -

Identifier Source: org_study_id

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