AIMC Topic: Hearing Loss, Noise-Induced
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Comparison between logistic regression and machine learning algorithms on prediction of noise-induced hearing loss and investigation of SNP loci.
Scientific reports
May 2, 2025
To compare the comprehensive performance of conventional logistic regression (LR) and seven machine learning (ML) algorithms in Noise-Induced Hearing Loss (NIHL) prediction, and to investigate the single nucleotide polymorphism (SNP) loci significant...
Applying machine learning algorithms to explore the impact of combined noise and dust on hearing loss in occupationally exposed populations.
Scientific reports
Mar 17, 2025
This study aimed to explore the combined impacts of occupational noise and dust on hearing and extra-auditory functions and identify associated risk factors via machine learning techniques. Data from 14,145 workers (627 with occupational noise-induce...
Research on noise-induced hearing loss based on functional and structural MRI using machine learning methods.
Scientific reports
Jan 26, 2025
Noise-induced hearing loss (NIHL) is a common occupational condition. The aim of this study was to develop a classification model for NIHL on the basis of both functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sM...
Hearing loss prediction equation for Iranian truck drivers using neural network algorithm.
Work (Reading, Mass.)
Nov 25, 2024
BACKGROUND:  Given the high prevalence of hearing loss among truck drivers, using artificial neural networks (ANNs) to predict and detect contributing factors early can aid managers significantly.
Risk analysis of noise-induced hearing loss of workers in the automobile manufacturing industries based on back-propagation neural network model: a cross-sectional study in Han Chinese population.
BMJ open
May 17, 2024
OBJECTIVES: This study aims to predict the risk of noise-induced hearing loss (NIHL) through a back-propagation neural network (BPNN) model. It provides an early, simple and accurate prediction method for NIHL.
Predicting noise-induced hearing loss with machine learning: the influence of tinnitus as a predictive factor.
The Journal of laryngology and otology
May 9, 2024
OBJECTIVES: This study aimed to determine which machine learning model is most suitable for predicting noise-induced hearing loss and the effect of tinnitus on the models' accuracy.
Noise exposure during robot-assisted total knee arthroplasty.
Archives of orthopaedic and trauma surgery
May 4, 2022
The aim of the study was to examine the noise exposure for operating theater staff during total knee arthroplasty (TKA) with three different robot systems. There is already evidence that noise exposure during TKA performed manually exceeds recommende...
Contributions and limitations of using machine learning to predict noise-induced hearing loss.
International archives of occupational and environmental health
Jan 25, 2021
PURPOSE: Noise-induced hearing loss (NIHL) is a global issue that impacts people's life and health. The current review aims to clarify the contributions and limitations of applying machine learning (ML) to predict NIHL by analyzing the performance of...
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...
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...
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