AIMC Topic: Hearing Loss, Noise-Induced

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Prediction of hearing loss among the noise-exposed workers in a steel factory using artificial intelligence approach.

International archives of occupational and environmental health Nov 29, 2014
PURPOSE: Prediction of hearing loss in noisy workplaces is considered to be an important aspect of hearing conservation program. Artificial intelligence, as a new approach, can be used to predict the complex phenomenon such as hearing loss. Using art...
Humans Neural Networks, Computer Adult Cross-Sectional Studies Male Occupational Exposure Predictive Value of Tests Iran Auditory Threshold Occupational Diseases Audiometry Hearing Loss, Noise-Induced Metallurgy Noise, Occupational Steel
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Morphologic clustering of earcanals using deep learning algorithm to design artificial ears dedicated to earplug attenuation measurement.

The Journal of the Acoustical Society of America Dec 1, 2022
Designing earplugs adapted for the widest number of earcanals requires acoustical test fixtures (ATFs) geometrically representative of the population. Most existing ATFs are equipped with unique sized straight cylindrical earcanals, considered repres...
Humans Artificial Intelligence Deep Learning Canada Cluster Analysis Hearing Loss, Noise-Induced Ear Protective Devices
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Development of an automatic classifier for the prediction of hearing impairment from industrial noise exposure.

The Journal of the Acoustical Society of America Apr 1, 2019
The ISO-1999 [(2013). International Organization for Standardization, Geneva, Switzerland] standard is the most commonly used approach for estimating noise-induced hearing trauma. However, its insensitivity to noise characteristics limits its practic...
Humans Female Aged Adult Male Middle Aged Support Vector Machine Acoustic Stimulation Manufacturing Industry Hearing Loss, Noise-Induced Noise, Occupational
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Machine Learning Models for the Hearing Impairment Prediction in Workers Exposed to Complex Industrial Noise: A Pilot Study.

Ear and hearing Jan 1, 2019
OBJECTIVES: To demonstrate the feasibility of developing machine learning models for the prediction of hearing impairment in humans exposed to complex non-Gaussian industrial noise.
Humans Machine Learning Female Young Adult Adult Male Middle Aged Risk Assessment China Pilot Projects Support Vector Machine Feasibility Studies Audiometry, Pure-Tone Hearing Loss, Noise-Induced Noise, Occupational
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