Sensitivity and specificity of a method for diagnosis of military noise-induced hearing loss

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

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.

Prevention of Noise-Induced Hearing Loss Using Investigational Medicines for the Inner Ear: Previous Trial Outcomes Should Inform Future Trial Design

Significance: Noise-induced hearing loss (NIHL) is an important public health issue resulting in decreased quality of life for affected individuals, and significant costs to employers and governmental agencies. Recent Advances: Advances in the mechanistic understanding of NIHL have prompted a growing number of proposed, in-progress, and completed clinical trials for possible protections against NIHL via antioxidants and other drug agents. Thirty-one clinical trials evaluating prevention of either temporary or permanent NIHL were identified and are reviewed. Critical Issues: This review revealed little consistency in the noise-exposed populations in which drugs are evaluated or the primary outcomes used to measure NIHL prevention. Changes in pure-tone thresholds were the most common primary outcomes; specific threshold metrics included both average hearing loss and incidence of significant hearing loss. Changes in otoacoustic emission (OAE) amplitude were relatively common secondary outcomes. Extended high-frequency (EHF) hearing and speech-in-noise perception are commonly adversely affected by noise exposure but are not consistently included in clinical trials assessing prevention of NIHL. Future Directions: Multiple criteria are available for monitoring NIHL, but the specific criterion to be used to define clinically significant otoprotection remains a topic of discussion. Audiogram-based primary outcome measures can be combined with secondary outcomes, including OAE amplitude, EHF hearing, speech-in-noise testing, tinnitus surveys, and patient-reported outcomes. Standardization of test protocols for the above primary and secondary outcomes, and associated reporting criterion for each, would facilitate clinical trial design and comparison of results across investigational drug agents. Antioxid. Redox Signal. 36, 1171-1202.

Guidelines for Diagnosing and Quantifying Noise-Induced Hearing Loss

This paper makes recommendations for the diagnosis and quantification of noise-induced hearing loss (NIHL) in a medico-legal context. A distinction is made between NIHL produced by: steady broadband noise, as occurs in some factories; more impulsive factory sounds, such as hammering; noise exposure during military service, which can involve very high peak sound levels; and exposure to very intense tones. It is argued that existing diagnostic methods, which were primarily developed to deal with NIHL produced by steady broadband noise, are not adequate for the diagnosis of NIHL produced by different types of exposures. Furthermore, some existing diagnostic methods are based on now-obsolete standards, and make unrealistic assumptions. Diagnostic methods are proposed for each of the types of noise exposure considered. It is recommended that quantification of NIHL for all types of exposures is based on comparison of the measured hearing threshold levels with the age-associated hearing levels (AAHLs) for a non-noise exposed population, as specified in ISO 7029 (2017), usually using the 50^th percentile, but using another percentile if there are good reasons for doing so. When audiograms are available both soon after the end of military service and some time afterwards, the most recent audiogram should be used for diagnosis and quantification, since this reflects any effect of the noise exposure on the subsequent progression of hearing loss. It is recommended that the overall NIHL for each ear be quantified as the average NIHL across the frequencies 1, 2, and 4 kHz.

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

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.

Audiometric assessment of hearing loss sustained during military service

An analysis is presented of the audiograms, obtained using Telephonics TDH39 headphones (Huntington, NY), of 80 men claiming compensation for noise-induced hearing loss (NIHL) sustained during military service. A comparison with an independent database of audiograms collected using other headphones suggested that no adjustment was needed to the hearing threshold levels (HTLs) at 6 kHz to allow for the use of TDH39 headphones. The method of Moore [(2020). J. Acoust. Soc. Am. 148, 884-894] for diagnosing military noise-induced hearing loss (M-NIHL) gave a positive diagnosis for 92.5% of right ears and 97.5% of left ears. The mean HTLs were maximal and similar at 4, 6, and 8 kHz but with considerable individual variability. A comparison with age-expected HTLs showed that M-NIHL was typically greatest at 3, 4, 6, or 8 kHz but with considerable individual variability. M-NIHL values were positive from 0.5 to 8 kHz. The HTLs were significantly higher for the left than for the right ears, but the asymmetry varied across individuals and could usually be ascribed to specific features of the noise exposure. The asymmetry existed over the range from 0.5 to 8 kHz, supporting the idea that M-NIHL occurs over a wide frequency range. Tinnitus was reported by 76 of the 80 men.