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Masking and Demasking Agents

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Dynamically Masked Audiograms With Machine Learning Audiometry.

Katherine L Heisey¹, Alexandra M Walker^1,2, Kevin Xie^1,3

¹Department of Biomedical Engineering, Laboratory of Sensory Neuroscience and Neuroengineering, Washington University in St. Louis, St. Louis, Missouri, USA.

Ear and Hearing

|November 2, 2020

Summary

This summary is machine-generated.

This study introduces a new, faster, and more accurate automated machine learning audiogram procedure with dynamic masking. This method simplifies hearing threshold testing, especially for individuals with asymmetric hearing loss.

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Area of Science:

Audiology
Machine Learning in Healthcare
Signal Processing

Background:

Evaluating hearing in individuals with asymmetric hearing requires masking noise in the better ear to prevent inaccurate results.
Current masking protocols are complex, time-consuming, and can be confusing for both clinicians and patients.
Automated audiogram procedures offer potential for increased efficiency, but require effective masking strategies.

Purpose of the Study:

To determine the accuracy and efficiency of an automated machine learning audiogram procedure utilizing dynamic masking.
To compare the performance of machine learning-based dynamic masking with traditional masking methods.

Main Methods:

A dynamically masked automated audiogram procedure was developed and tested on 29 participants with diverse hearing abilities.
Normal-hearing participants underwent both masked and unmasked machine learning audiograms.
Participants with hearing loss received a standard audiogram with clinical masking, followed by the masked machine learning audiogram.

Main Results:

Machine learning audiogram threshold estimates showed strong agreement with traditional methods in both normal-hearing and hearing-impaired listeners.
Mean absolute differences in threshold estimates were low (3.4 dB for normal hearing, 4.9 dB and 2.6 dB for asymmetric hearing loss).
The automated method was generally faster than manual procedures, with no documented instances of tones detected by the non-test ear.

Conclusions:

Dynamically masked automated audiograms accurately estimate true hearing thresholds.
This novel approach significantly reduces testing time compared to current clinical masking procedures.
Dynamic masking offers a more efficient and effective alternative for evaluating individuals with asymmetric hearing and can benefit all patients.