Reliability of Tablet-based Hearing Testing in Nicaraguan Schoolchildren: A Detailed Analysis

Magro, Isabelle; Clavier, Odile; Mojica, Karen; Rieke, Catherine C.; Eisen, Eric A.; Fried, Debra; Stein-Meyers, Anita; Fellows, Abigail; Buckey, Jay C.; Saunders, James E.

doi:10.1097/mao.0000000000002534

Cited by 13 publications

(10 citation statements)

References 25 publications

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“…However, pure-tone audiometry is a test based on sequence and step inspection, which is particularly suitable for the automation ( Margolis and Morgan, 2008 ). Automation is a powerful enabler for alternative diagnostic pathways, which can reduce testing costs without trained audiologists ( Eksteen et al, 2019 ) and testing outside a sound booth ( Brennan-Jones et al, 2016 ; Magro et al, 2020 ), and potentially benefit people with hearing loss in remote and economically underdeveloped areas ( Visagie et al, 2015 ; Sandström et al, 2020 ), to address the global need for an accessible hearing loss diagnosis ( Swanepoel et al, 2019 ; Sidiras et al, 2021 ; Wasmann et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Clinical comparison of two automated audiometry procedures

Liu

Du²,

Liu³

et al. 2022

Front. Neurosci.

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ObjectiveAutomated pure-tone audiometry has been shown to provide similar hearing threshold estimates to conventional audiometry, but lower correlations were reported at high and low frequencies in audiometric tests than those of manual tests, while the correlations were better in the middle frequencies. In this paper, we used the same equipment and different test procedures for automated testing, and compared the results with manual test results.DesignOne hundred subjects aged 18–36 years were randomly divided into two groups to perform air-conduction pure-tone audiometry (0.25, 0.5, 1, 2, 4, 8 kHz) using the ascending and shortened ascending protocols built-in to the automated audiometer, respectively. Recorded testing time, the total number of responses and the subject’s preference tests were compared with those of manual tests.ResultsSignificant difference was found at 250 Hz regarding the distribution of the absolute difference between the two automated and the manual thresholds. The testing time spend in the ascending method (9.8 ± 1.4 min, mean ± SD) was significantly longer than in the shorted ascending method (5.8 ± 0.9 min). The total numbers of responses of the ascending method (90.5 ± 10.8 times) and shorted ascending method (62.0 ± 11.4 times) were significantly different. Finally, no significant difference was found in preferences between automated and manual procedures.ConclusionThe shorted ascending method can save lots of testing time. The difference between the two automated thresholds at 250 Hz is caused by the different test procedures, and the difference at 8,000 Hz between the automated test and the manual test can be due to the transducer types and allowable differences in calibration.

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Section: Introductionmentioning

confidence: 99%

Clinical comparison of two automated audiometry procedures

Liu

Du²,

Liu³

et al. 2022

Front. Neurosci.

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“…-Masalski and Krecicki [41,64,65] WHATS is a mobile wireless automated hearing test system in occupational audiometry for obtaining hearing thresholds in diverse test locations without the use of a sound booth. WHATS d Meinke et al [66,67] It is a novel, subjective, test-based approach used to calibrate a smartphone-earphone combination with respect to the reference audiometer. HearTest e Patel et al [32] Specific Bekesy tracking patterns were identified in people who experienced difficulty converging to a reliable threshold.…”

Section: Home Hearing Testmentioning

confidence: 99%

Digital Approaches to Automated and Machine Learning Assessments of Hearing: Scoping Review

Wasmann¹,

Pragt²,

Eikelboom³

et al. 2022

J Med Internet Res

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Background Hearing loss affects 1 in 5 people worldwide and is estimated to affect 1 in 4 by 2050. Treatment relies on the accurate diagnosis of hearing loss; however, this first step is out of reach for >80% of those affected. Increasingly automated approaches are being developed for self-administered digital hearing assessments without the direct involvement of professionals. Objective This study aims to provide an overview of digital approaches in automated and machine learning assessments of hearing using pure-tone audiometry and to focus on the aspects related to accuracy, reliability, and time efficiency. This review is an extension of a 2013 systematic review. Methods A search across the electronic databases of PubMed, IEEE, and Web of Science was conducted to identify relevant reports from the peer-reviewed literature. Key information about each report’s scope and details was collected to assess the commonalities among the approaches. Results A total of 56 reports from 2012 to June 2021 were included. From this selection, 27 unique automated approaches were identified. Machine learning approaches require fewer trials than conventional threshold-seeking approaches, and personal digital devices make assessments more affordable and accessible. Validity can be enhanced using digital technologies for quality surveillance, including noise monitoring and detecting inconclusive results. Conclusions In the past 10 years, an increasing number of automated approaches have reported similar accuracy, reliability, and time efficiency as manual hearing assessments. New developments, including machine learning approaches, offer features, versatility, and cost-effectiveness beyond manual audiometry. Used within identified limitations, automated assessments using digital devices can support task-shifting, self-care, telehealth, and clinical care pathways.

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“…These biological reference sound levels have been collected in uncontrolled conditions in normal-hearing persons. Meinke et al, 2017 60,61…”

Section: Amtasmentioning

confidence: 99%

Automated and machine learning approaches in diagnostic hearing assessment: a scoping review

Pragt¹,

Wasmann²,

Eikelboom³

et al. 2021

Preprint

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Hearing loss affects one in five people worldwide and is estimated to affect one in four by 2050. Treatment relies on accurate diagnosis of hearing loss, but this first step is out of reach for more than 80% of those affected. Increasingly automated digital approaches are being developed for self-administered hearing assessment without professionals’ direct involvement. This scoping review provides an overview of automated approaches, based on 56 reports from 2012 until June 2021, adding to the 29 published prior to 2012. Twenty-seven automated approaches were identified. An increasing number of approaches report similar accuracy as manual hearing assessments. Machine learning approaches are more efficient and personal digital devices make assessments more affordable and accessible. Validity can be enhanced by quality surveillance including noise monitoring and detecting inconclusive results. Employed within identified limitations, automated hearing assessments can support task-shifting, self-care, and clinical care pathways.

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Reliability of Tablet-based Hearing Testing in Nicaraguan Schoolchildren: A Detailed Analysis

Cited by 13 publications

References 25 publications

Clinical comparison of two automated audiometry procedures

Clinical comparison of two automated audiometry procedures

Digital Approaches to Automated and Machine Learning Assessments of Hearing: Scoping Review

Automated and machine learning approaches in diagnostic hearing assessment: a scoping review

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