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Hearing

Hearing

When we hear a sound, our nervous system is detecting sound waves—pressure waves of mechanical energy traveling through a medium. The frequency of the wave is perceived as pitch, while the amplitude is perceived as loudness.

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Related Experiment Video

Updated: May 9, 2025

Modified Experimental Conditions for Noise-Induced Hearing Loss in Mice and Assessment of Hearing Function and Outer Hair Cell Damage

Modified Experimental Conditions for Noise-Induced Hearing Loss in Mice and Assessment of Hearing Function and Outer Hair Cell Damage

Published on: February 10, 2023

Machine Learning Models Can Predict Tinnitus and Noise-Induced Hearing Loss.

Zahra Jafari^1,2,3,4,5, Ryan E Harari^6,7,5, Glenn Hole⁸

¹School of Communication Sciences and Disorders (SCSD), Dalhousie University, Halifax, Nova Scotia, Canada.

Ear and Hearing

|May 6, 2025

Summary

This summary is machine-generated.

Machine learning models effectively differentiate tinnitus and hearing loss types. Artificial neural networks excelled at predicting tinnitus, while random forest models accurately distinguished noise-induced from age-related hearing loss.

Keywords:

Age-related hearing loss Artificial Neural Networks Machine learning Noise-induced hearing loss Random forest Tinnitus

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Area of Science:

Auditory science
Medical artificial intelligence
Diagnostic technology

Background:

Machine learning (ML) applications in health sciences are widespread for prediction and classification.
However, ML's use in differentiating auditory disorders is limited.
This study addresses the gap in diagnosing tinnitus and distinguishing between noise-induced hearing loss (NIHL) and age-related hearing loss (ARHL).

Purpose of the Study:

To evaluate the efficacy of five ML models in distinguishing tinnitus from non-tinnitus.
To assess ML models' ability to differentiate NIHL from ARHL.
To identify optimal ML models for auditory disorder diagnostics.

Main Methods:

Utilized data from 928 Canadian adults (30-100 years) with diagnosed ARHL or NIHL.
Applied five ML models: artificial neural networks (ANNs), K-nearest neighbors, logistic regression, random forest (RF), and support vector machines.
Analyzed audiologic and demographic data, focusing on hearing loss patterns and tinnitus prevalence.

Main Results:

Tinnitus prevalence was over double in the NIHL group (27.85% constant, 18.55% intermittent) compared to ARHL (8.85% constant, 10.86% intermittent).
NIHL showed significantly greater hearing loss at medium- and high-band frequencies versus ARHL.
ANN achieved 70% accuracy for tinnitus prediction; RF achieved 90% AUC for NIHL vs. ARHL differentiation.

Conclusions:

ML models, particularly ANN and RF, enhance diagnostic precision for tinnitus and NIHL.
Findings suggest a framework for integrating ML into clinical audiology.
Future research should expand datasets and incorporate longitudinal data for broader applicability.