Digital Approaches to Automated and Machine Learning Assessments of Hearing: Scoping Review.

ABSTRACT:

Background

Hearing loss affects 1 in 5 people worldwide and is estimated to affect 1 in 4 by 2050. Treatment relies on the accurate diagnosis of hearing loss; however, this first step is out of reach for >80% of those affected. Increasingly automated approaches are being developed for self-administered digital hearing assessments without the direct involvement of professionals.

Objective

This study aims to provide an overview of digital approaches in automated and machine learning assessments of hearing using pure-tone audiometry and to focus on the aspects related to accuracy, reliability, and time efficiency. This review is an extension of a 2013 systematic review.

Methods

A search across the electronic databases of PubMed, IEEE, and Web of Science was conducted to

SUBMITTER: Wasmann JW 

PROVIDER: S-EPMC8851345 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature