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

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.