Online Machine Learning Audiometry

Abstract

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. The objective of this study is 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, standard deviation 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 respectively. The machine learning method generated estimates of both threshold and spread (i.e., the inverse of psychometric slope)

Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery

“…In voice-based analysis, AI is used to evaluate pathological voice conditions associated with vocal fold disorders, to analyze and decode phonation itself [67], to improve speech perception in noisy conditions, and to improve the hearing of pa-tients with CIs. In medical device-based analyses, AI is used to evaluate tissue and blood test results, as well as the outcomes of otorhinolaryngology-specific tests (e.g., polysomnography) [72,73,122] and audiometry [123,124]. AI has also been used to support clinical diagnoses and treatments, decision-making, the Table 4.…”

“…In voice-based analysis, AI is used to evaluate pathological voice conditions associated with vocal fold disorders, to analyze and decode phonation itself [ 67 ], to improve speech perception in noisy conditions, and to improve the hearing of patients with CIs. In medical device-based analyses, AI is used to evaluate tissue and blood test results, as well as the outcomes of otorhinolaryngology-specific tests (e.g., polysomnography) [ 72 , 73 , 122 ] and audiometry [ 123 , 124 ]. AI has also been used to support clinical diagnoses and treatments, decision-making, the prediction of prognoses [ 98 - 100 , 125 , 126 ], disease profiling, the construction of mass spectral databases [ 43 , 127 - 129 ], the identification or prediction of disease progress [ 101 , 105 , 107 - 110 , 130 ], and the confirmation of diagnoses and the utility of treatments [ 102 - 104 , 112 , 131 ].…”

Computational analysis based on audioprofiles: A new possibility for patient stratification in office-based otology

“…Approaches to even test hearing function in the absence of a physician or audiologist are possible using online machine learning audiometry consisting of a nonparametric Bayesian estimator of tone detection probability as a function of frequency and sound level. 20 Epidemiological and genetic studies can be applied to identify risk factors for age-related hearing impairment. 6 Usually, one phenotype is used to define the presence/ absence of hearing impairment due to genetics causes, ageing or to determine possible risk factors or causes.…”

eHealth Technologies Enable more Accessible Hearing Care

“…Using new methods of artificial intelligence could further optimize the testing to obtain more information in shorter periods. 28 Using trained nonprofessionals like CHWs to provide services requires quality control metrics that advise on the feasibility of test environments and operator test reliability. 21,29 Smartphone microphones allow accurate monitoring of environmental noise and can provide real-time feedback on compliance to maximum permissible ambient noise levels for operators and remote asynchronous interpretation of test reliability by professionals.…”