The Journal of the Acoustical Society of America
31046337
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...
International archives of occupational and environmental health
33491101
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...
Archives of orthopaedic and trauma surgery
35507089
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...
The Journal of the Acoustical Society of America
36586832
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...
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.
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.
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.
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...
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...
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...