Evaluation of a Fast Method to Measure High-Frequency Audiometry Based on Bayesian Learning.

Abstract

This study aimed to assess the validity of a high-frequency audiometry tool based on Bayesian learning to provide a reliable, repeatable, automatic, and fast test to clinics. The study involved 85 people (138 ears) who had their high-frequency thresholds measured with three tests: standard audiometry (SA), alternative forced choice (AFC)-based algorithm, and Bayesian active (BA) learning-based algorithm. The results showed median differences within ±5 dB up to 10 kHz when comparing the BA with the other two tests, and median differences within ±10 dB at higher frequencies. The variability increased from lower to higher frequencies. The BA showed lower thresholds compared to the SA at the majority of the frequencies. The results of the different tests were consistent across groups (age, hearing loss, and tinnitus). The data for the BA showed high test-retest reliability (>90%). The time required for the BA was shorter than for the AFC (4 min vs. 13 min). The data suggest that the BA test for high-frequency audiometry could be a good candidate for clinical screening. It would add reliable and significant information without adding too much time to the visit.