AIMC Topic: Audiometry, Pure-Tone
Clear Filters Showing 11 to 17 of 17 articles
Robust machine learning method for imputing missing values in audiograms collected in children.
International journal of audiology
Feb 27, 2021
OBJECTIVE: To assess the accuracy and reliability of a machine learning (ML) algorithm for predicting the full audiograms of hearing-impaired children relative to the common approach (CA).
The Role of Preoperative Steroids for Hearing Preservation Cochlear Implantation: Results of a Randomized Controlled Trial.
Audiology & neuro-otology
Jan 13, 2018
OBJECTIVES: To determine whether preoperative steroids can improve hearing outcomes in cochlear implantation (CI).
Performance and Reliability Evaluation of an Automated Bone-Conduction Audiometry Using Machine Learning.
Trends in hearing
Jan 1, 2024
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 develo...
Did You Hear That? Detecting Auditory Events with EEGNet.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2023
The behavioural nature of pure-tone audiometry (PTA) limits those who can participate in the test, and therefore those who can access accurate hearing threshold measurements. Event Related Potentials (ERPs) from brain signals has shown limited utilit...
Online Machine Learning Audiometry.
Ear and hearing
Jan 1, 2019
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 com...
Machine Learning Models for the Hearing Impairment Prediction in Workers Exposed to Complex Industrial Noise: A Pilot Study.
Ear and hearing
Jan 1, 2019
OBJECTIVES: To demonstrate the feasibility of developing machine learning models for the prediction of hearing impairment in humans exposed to complex non-Gaussian industrial noise.
Fast, Continuous Audiogram Estimation Using Machine Learning.
Ear and hearing
Jan 1, 2015
OBJECTIVES: Pure-tone audiometry has been a staple of hearing assessments for decades. Many different procedures have been proposed for measuring thresholds with pure tones by systematically manipulating intensity one frequency at a time until a disc...
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