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Performance and Reliability Evaluation of an Automated Bone-Conduction Audiometry Using Machine Learning.

Nicolas Wallaert^1,2, Antoine Perry³, Hadrien Jean²

¹Department of Otorhinolaryngology-Head and Neck Surgery, Rennes University Hospital, Rennes, France.

Trends in Hearing

|November 3, 2024

Summary

This summary is machine-generated.

A new machine learning (ML) approach automates bone-conduction (BC) audiometry using forehead placement, offering a faster and more reliable hearing test. This ML-audiometry shows performance comparable to traditional methods for adults.

Keywords:

audiometry automated test bayesian learning contralateral masking psychoacoustics threshold

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Area of Science:

Audiology
Biomedical Engineering
Machine Learning

Background:

Pure-tone audiometry is the standard but is time-consuming and provides discrete hearing acuity estimates.
Limitations of current bone-conduction (BC) audiometry include time commitment and discrete data points.

Purpose of the Study:

To develop a machine learning (ML)-based approach for fully automated BC audiometry with forehead vibrator placement.
To address the time-consuming nature and discrete estimates of conventional audiometry.

Main Methods:

Developed an ML-based approach for automated BC audiometry with forehead placement.
Incorporated automated contralateral masking, occlusion effect compensation, and forehead-mastoid corrections.
Evaluated performance against manual conventional BC audiometry and assessed test-retest reliability.

Main Results:

No significant performance difference was found between automated ML-audiometry and manual conventional audiometry.
Automated ML-audiometry demonstrated high test-retest reliability.
The ML approach proved effective for both normal-hearing and hearing-impaired adult listeners.

Conclusions:

Automated ML-based BC audiometry with forehead placement is a viable and reliable alternative to conventional methods.
This approach offers a more efficient and accurate assessment of hearing acuity.
Findings support the use of ML-audiometry for a broad range of adult hearing statuses.