Literature DB >> 24165302

Validity of automated threshold audiometry: a systematic review and meta-analysis.

Faheema Mahomed1, De Wet Swanepoel, Robert H Eikelboom, Maggi Soer.   

Abstract

OBJECTIVES: A systematic literature review and meta-analysis on the validity (test-retest reliability and accuracy) of automated threshold audiometry compared with the gold standard of manual threshold audiometry was conducted.
DESIGN: A systematic literature review was completed in peer-reviewed databases on automated compared with manual threshold audiometry. Subsequently a meta-analysis was conducted on the validity of automated audiometry.
METHODS: A multifaceted approach, covering several databases and using different search strategies was used to ensure comprehensive coverage and to cross-check search findings. Databases included: MEDLINE, Scopus, and PubMed; a secondary search strategy was the review of references from identified reports. Reports including within-subject comparisons of manual and automated threshold audiometry were selected according to inclusion/exclusion criteria before data were extracted. For the meta-analysis weighted mean differences (and standard deviations) on test-retest reliability for automated compared with manual audiometry were determined to assess the validity of automated threshold audiometry.
RESULTS: In total, 29 reports on automated audiometry (method of limits and the method of adjustment techniques) met the inclusion criteria and were included in this review. Most reports included data on adult populations using air conduction testing with limited data on children, bone conduction testing and the effects of hearing status on automated audiometry. Meta-analysis test-retest reliability for automated audiometry was within typical test-retest variability for manual audiometry. Accuracy results on the meta-analysis indicated overall average differences between manual and automated air conduction audiometry (0.4 dB, 6.1 SD) to be comparable with test-retest differences for manual (1.3 dB, 6.1 SD) and automated (0.3 dB, 6.9 SD) audiometry. No significant differences (p > 0.01; summarized data analysis of variance) were seen in any of the comparisons between test-retest reliability of manual and automated audiometry compared with differences between manual and automated audiometry.
CONCLUSION: Automated audiometry provides an accurate measure of hearing threshold, but validation data are still limited for (1) automated bone conduction audiometry; (2) automated audiometry in children and difficult-to-test populations and; (3) different types and degrees of hearing loss.

Entities:  

Mesh:

Year:  2013        PMID: 24165302     DOI: 10.1097/01.aud.0000436255.53747.a4

Source DB:  PubMed          Journal:  Ear Hear        ISSN: 0196-0202            Impact factor:   3.570


  32 in total

1.  Distribution characteristics of normal pure-tone thresholds.

Authors:  Robert H Margolis; Richard H Wilson; Gerald R Popelka; Robert H Eikelboom; De Wet Swanepoel; George L Saly
Journal:  Int J Audiol       Date:  2015-05-04       Impact factor: 2.117

2.  Going wireless and booth-less for hearing testing in industry.

Authors:  Deanna K Meinke; Jesse A Norris; Brendan P Flynn; Odile H Clavier
Journal:  Int J Audiol       Date:  2016-12-15       Impact factor: 2.117

3.  Conjoint psychometric field estimation for bilateral audiometry.

Authors:  Dennis L Barbour; James C DiLorenzo; Kiron A Sukesan; Xinyu D Song; Jeff Y Chen; Eleanor A Degen; Katherine L Heisey; Roman Garnett
Journal:  Behav Res Methods       Date:  2019-06

Review 4.  Emerging Technologies, Market Segments, and MarkeTrak 10 Insights in Hearing Health Technology.

Authors:  Brent Edwards
Journal:  Semin Hear       Date:  2020-02-10

5.  Bayesian active probabilistic classification for psychometric field estimation.

Authors:  Xinyu D Song; Kiron A Sukesan; Dennis L Barbour
Journal:  Atten Percept Psychophys       Date:  2018-04       Impact factor: 2.199

6.  Distribution Characteristics of Air-Bone Gaps: Evidence of Bias in Manual Audiometry.

Authors:  Robert H Margolis; Richard H Wilson; Gerald R Popelka; Robert H Eikelboom; De Wet Swanepoel; George L Saly
Journal:  Ear Hear       Date:  2016 Mar-Apr       Impact factor: 3.570

7.  Comparing the Accuracy and Speed of Manual and Tracking Methods of Measuring Hearing Thresholds.

Authors:  Gayla L Poling; Theresa J Kunnel; Sumitrajit Dhar
Journal:  Ear Hear       Date:  2016 Sep-Oct       Impact factor: 3.570

8.  Home Hearing Test: Within-Subjects Threshold Variability.

Authors:  Robert H Margolis; Gene Bratt; M Patrick Feeney; Mead C Killion; George L Saly
Journal:  Ear Hear       Date:  2018 Sep/Oct       Impact factor: 3.570

9.  Online Machine Learning Audiometry.

Authors:  Dennis L Barbour; Rebecca T Howard; Xinyu D Song; Nikki Metzger; Kiron A Sukesan; James C DiLorenzo; Braham R D Snyder; Jeff Y Chen; Eleanor A Degen; Jenna M Buchbinder; Katherine L Heisey
Journal:  Ear Hear       Date:  2019 Jul/Aug       Impact factor: 3.570

10.  Fast, Continuous Audiogram Estimation Using Machine Learning.

Authors:  Xinyu D Song; Brittany M Wallace; Jacob R Gardner; Noah M Ledbetter; Kilian Q Weinberger; Dennis L Barbour
Journal:  Ear Hear       Date:  2015 Nov-Dec       Impact factor: 3.570

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