Online Machine Learning Audiometry

Objectives: A confluence of recent developments in cloud computing, real-time web audio and machine learning psychometric function estimation has made wide dissemination of sophisticated turn-key audiometric assessments possible. The authors have combined these capabilities into an online (i.e., web-based) pure-tone audiogram estimator intended to empower researchers and clinicians with advanced hearing tests without the need for custom programming or special hardware. The objective of this study was to assess the accuracy and reliability of this new online machine learning audiogram method relative to a commonly used hearing threshold estimation technique also implemented online for the first time in the same platform. Design: The authors performed air conduction pure-tone audiometry on 21 participants between the ages of 19 and 79 years (mean 41, SD 21) exhibiting a wide range of hearing abilities. For each ear, two repetitions of online machine learning audiogram estimation and two repetitions of online modified Hughson-Westlake ascending-descending audiogram estimation were acquired by an audiologist using the online software tools. The estimated hearing thresholds of these two techniques were compared at standard audiogram frequencies (i.e., 0.25, 0.5, 1, 2, 4, 8 kHz). Results: The two threshold estimation methods delivered very similar threshold estimates at standard audiogram frequencies. Specifically, the mean absolute difference between threshold estimates was 3.24 ± 5.15 dB. The mean absolute differences between repeated measurements of the online machine learning procedure and between repeated measurements of the Hughson-Westlake procedure were 2.85 ± 6.57 dB and 1.88 ± 3.56 dB, respectively. The machine learning method generated estimates of both threshold and spread (i.e., the inverse of psychometric slope) continuously across the entire frequency range tested from fewer samples on average than the modified Hughson-Westlake procedure required to estimate six discrete thresholds. Conclusions: Online machine learning audiogram estimation in its current form provides all the information of conventional threshold audiometry with similar accuracy and reliability in less time. More importantly, however, this method provides additional audiogram details not provided by other methods. This standardized platform can be readily extended to bone conduction, masking, spectrotemporal modulation, speech perception, etc., unifying audiometric testing into a single comprehensive procedure efficient enough to become part of the standard audiologic workup. ACKNOWLEDGMENTS: Funding for this project was provided by NIH grants UL1 TR002345, T35 DC008765, T32 NS073547, NSF grant DGE-1745038, and the Center for Integration of Medicine and Innovative Technology (CIMIT). LLC. D. L. B. wrote the article; R. T. H. designed and conducted the experiments; N. M. helped design and conduct the experiments; X. D. S., K. A. S., J. C. D., and B. R. D. S. developed and implemented the algorithm; J. Y. C., E. A. D., J. M. B., K. L. H., and D. L. B. analyzed the data. All authors discussed the results and implications and commented on article revisions. D. L. B. has a patent pending on technology described in this article and has equity ownership in Bonauria, LLC. The authors have no other disclosures. Received December 18, 2017; accepted August 22, 2018. Address for correspondence: Dennis Barbour, Department of Biomedical Engineering, Washington University, One Brookings Dr., Campus Box 1097, Uncas Whitaker Hall Room 200E, St. Louis, MO 63130, USA. E-mail: dbarbour@wustl.edu Copyright © 2018 Wolters Kluwer Health, Inc. All rights reserved.

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