Association of self-reported noise exposure and audiograms processed with algorithms proposed to quantify noise-induced hearing loss

Pudrith, Charles; Phillips, Susan L.; Labban, Jeffrey D.

doi:10.1080/14992027.2021.1983216

Cited by 4 publications

(4 citation statements)

References 24 publications

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“…Noise-induced hearing loss (NIHL) is commonly diagnosed based on the presence of a notch or bulge in the audiogram centered near 4 kHz ( Coles et al, 2000 ; Niskar et al, 2001 ; Phillips et al, 2010 ; Pudrith et al, 2022 ). Moore (2020) argued that such methods are not appropriate in cases of NIHL sustained during military service, which often involves exposure to very intense impulsive sound and does not always produce a clear notch or bulge in the audiogram.…”

Section: Introductionmentioning

confidence: 99%

“…Although the M-NIHL method was shown to have high sensitivity ( Lowe & Moore, 2021 ; Moore, 2020 ), its specificity was only moderate ( Moore & von Gablenz, 2021 ). This moderate specificity is probably a consequence of the high prevalence of mild hearing loss at high frequencies and the moderately high prevalence of small audiometric notches among the general population ( Pudrith et al, 2022 ; Schlauch & Carney, 2011 ). However, it may also be partly a consequence of some problems with the M-NIHL method.…”

Section: Introductionmentioning

confidence: 99%

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Modification of a Method for Diagnosing Noise-Induced Hearing Loss Sustained During Military Service

et al. 2022

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Moore (2020) proposed a method for diagnosing noise-induced hearing loss (NIHL) sustained during military service, based on an analysis of the shapes of the audiograms of military personnel. The method, denoted M-NIHL, was estimated to have high sensitivity but low-to-moderate specificity. Here, a revised version of the method, denoted rM-NIHL, was developed that gave a better balance between sensitivity and specificity. A database of 285 audiograms of military noise-exposed men was created by merging two previously used databases with a new database, randomly shuffling, and then splitting into two, one for development of the revised method and one for evaluation. Two comparable databases of audiograms of 185 non-exposed men were also created, again one for development and one for evaluation. Based on the evaluation databases, the rM-NIHL method has slightly lower sensitivity than the M-NIHL method, but the specificity is markedly higher. The two methods have similar overall diagnostic performance. If an individual is classified as having NIHL based on a positive diagnosis for either ear, the rM-NIHL method has a sensitivity of 0.98 and a specificity of 0.63. Based on a positive diagnosis for both ears, the rM-NIHL method has a sensitivity of 0.76 and a specificity of 0.95.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modification of a Method for Diagnosing Noise-Induced Hearing Loss Sustained During Military Service

et al. 2022

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“…All diagnostic methods include the requirement that there should have been exposure to noise with the potential to cause hearing loss, and it is assumed here that this requirement has been met. Most methods for diagnosing NIHL produced by exposure to steady broadband noise assess whether there is a notch or bulge in the audiogram centred near 4 kHz ( Coles et al, 2000 ; Niskar et al, 2001 ; Phillips et al, 2010 ; Pudrith et al, 2022 ). For example, the method of Coles et al (2000) , called here the CLB method, has two main requirements in terms of the audiogram: (a) a single measurement of the hearing threshold level (HTL) at 3, 4 or 6 kHz should be at least 10 dB greater than the HTL at 1 or 2 kHz, i.e.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of Methods for Diagnosing Noise-Induced Hearing Loss in Cases of Exposures Including Intense Low-Frequency Noise

Moore,

Cox

2024

Trends in Hearing

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Exposure to intense low-frequency sounds, for example inside tanks and armoured vehicles, can lead to noise-induced hearing loss (NIHL) with a variable audiometric pattern, including low- and mid-frequency hearing loss. It is not known how well existing methods for diagnosing NIHL apply in such cases. Here, the audiograms of 68 military personnel (mostly veterans) who had been exposed to intense low-frequency noise (together with other types of noise) and who had low-frequency hearing loss (defined as a pure-tone average loss at 0.25, 0.5 and 1 kHz ≥20 dB) were used to assess the sensitivity of three diagnostic methods: the method of Coles, Lutman and Buffin, denoted CLB, which depends on the identification of a notch or bulge in the audiogram near 4 kHz, and two methods specifically intended for diagnosing NIHL sustained during military service, the rM-NIHL method, which depends on the identification of a notch or bulge in the audiogram near 4 kHz and/or a hearing loss at high frequencies greater than expected from age alone, and the MLP(18) method based on a multi-layer perceptron. The proportion of individuals receiving a positive diagnosis for either or both ears, which provides an approximate measure of sensitivity, was 0.40 for the CLB method, 0.79 for the rM-NIHL method and 1.0 for the MLP(18) method. It is concluded that the MLP(18) method is suitable for diagnosing NIHL sustained during military service whether or not the exposure includes intense low-frequency sounds.

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“…An alternative approach is to base the diagnosis of NIHL on one or more audiograms obtained after the period of noise exposure has occurred. Many such methods involve identification of a notch or bulge in the audiogram centered near 4 kHz ( Coles et al, 2000 ; Niskar et al, 2001 ; Phillips et al, 2010 ; Pudrith et al, 2022 ). Notches typically occur following exposure to intense steady broadband noise, such as occurs in some factories ( Passchier-Vermeer, 1974 ; Smoorenburg, 1992 ).…”

Section: Introductionmentioning

confidence: 99%

Diagnosing Noise-Induced Hearing Loss Sustained During Military Service Using Deep Neural Networks

Moore

Schlittenlacher

2023

Trends in Hearing

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The diagnosis of noise-induced hearing loss (NIHL) is based on three requirements: a history of exposure to noise with the potential to cause hearing loss; the absence of known causes of hearing loss other than noise exposure; and the presence of certain features in the audiogram. All current methods for diagnosing NIHL have involved examination of the typical features of the audiograms of noise-exposed individuals and the formulation of quantitative rules for the identification of those features. This article describes an alternative approach based on the use of multilayer perceptrons (MLPs). The approach was applied to databases containing the ages and audiograms of individuals claiming compensation for NIHL sustained during military service (M-NIHL), who were assumed mostly to have M-NIHL, and control databases with no known exposure to intense sounds. The MLPs were trained so as to classify individuals as belonging to the exposed or control group based on their audiograms and ages, thereby automatically identifying the features of the audiogram that provide optimal classification. Two databases (noise exposed and nonexposed) were used for training and validation of the MLPs and two independent databases were used for evaluation and further analyses. The best-performing MLP was one trained to identify whether or not an individual had M-NIHL based on age and the audiogram for both ears. This achieved a sensitivity of 0.986 and a specificity of 0.902, giving an overall accuracy markedly higher than for previous methods.

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Association of self-reported noise exposure and audiograms processed with algorithms proposed to quantify noise-induced hearing loss

Cited by 4 publications

References 24 publications

Modification of a Method for Diagnosing Noise-Induced Hearing Loss Sustained During Military Service

Modification of a Method for Diagnosing Noise-Induced Hearing Loss Sustained During Military Service

Sensitivity of Methods for Diagnosing Noise-Induced Hearing Loss in Cases of Exposures Including Intense Low-Frequency Noise

Diagnosing Noise-Induced Hearing Loss Sustained During Military Service Using Deep Neural Networks

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