Performance and Reliability Evaluation of an Automated Bone-Conduction Audiometry Using Machine Learning.

ABSTRACT: To date, pure-tone audiometry remains the gold standard for clinical auditory testing. However, pure-tone audiometry is time-consuming and only provides a discrete estimate of hearing acuity. Here, we aim to address these two main drawbacks by developing a machine learning (ML)-based approach for fully automated bone-conduction (BC) audiometry tests with forehead vibrator placement. Study 1 examines the occlusion effects when the headphones are positioned on both ears during BC forehead testing. Study 2 describes the ML-based approach for BC audiometry, with automated contralateral masking rules, compensation for occlusion effects and forehead-mastoid corrections. Next, the performance of ML-audiometry is examined in comparison to manual and conventional BC audiometry with mastoid placemen

SUBMITTER: Wallaert N 

PROVIDER: S-EPMC11703668 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature