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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 30, 2025

Functional Imaging of Auditory Cortex in Adult Cats using High-field fMRI

Functional Imaging of Auditory Cortex in Adult Cats using High-field fMRI

Published on: February 19, 2014

Research on noise-induced hearing loss based on functional and structural MRI using machine learning methods.

Minghui Lv¹, Liping Wang¹, Ranran Huang¹

¹Imaging Department, Yantaishan Hospital, Yantai, China.

Scientific Reports

|January 26, 2025

Summary

This summary is machine-generated.

Machine learning models can now classify noise-induced hearing loss (NIHL) using brain imaging. Functional MRI (fMRI) data, combined with machine learning, shows high accuracy in identifying NIHL in individuals.

Keywords:

Functional magnetic resonance imaging Machine learning Noise-induced hearing loss Structural magnetic resonance imaging

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Last Updated: May 30, 2025

Functional Imaging of Auditory Cortex in Adult Cats using High-field fMRI

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Published on: February 19, 2014

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Published on: February 10, 2023

Functional Neuroimaging Using Ultrasonic Blood-brain Barrier Disruption and Manganese-enhanced MRI

Functional Neuroimaging Using Ultrasonic Blood-brain Barrier Disruption and Manganese-enhanced MRI

Published on: July 12, 2012

Area of Science:

Neuroimaging
Machine Learning
Occupational Health

Background:

Noise-induced hearing loss (NIHL) is a prevalent occupational health issue.
Current diagnostic methods may not fully capture the underlying neurological changes associated with NIHL.
Advanced neuroimaging and machine learning offer potential for improved NIHL classification.

Purpose of the Study:

To develop and evaluate a machine learning-based classification model for NIHL.
To integrate functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) data.
To identify optimal neuroimaging features for distinguishing NIHL patients from healthy individuals.

Main Methods:

Extracted fMRI indices (ALFF, fALFF, ReHo, DC) and sMRI indices (GMV, WMV, cortical thickness).
Utilized Least Absolute Shrinkage and Selection Operator (LASSO) for feature selection.
Employed Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR) for model development.

Main Results:

The SVM model integrating fMRI indices achieved the highest performance.
Achieved an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.97.
Demonstrated a classification accuracy of 95% for identifying NIHL.

Conclusions:

The SVM classification model integrating fMRI indicators shows significant potential for NIHL identification.
fMRI indicators play a complementary role in the classification of NIHL.
Incorporating multiple brain imaging indicators is crucial for robust NIHL classification models.