TY  - JOUR
AU  - Wasmann, Jan-Willem
AU  - Pragt, Leontien
AU  - Eikelboom, Robert
AU  - Swanepoel, De Wet
PY  - 2022
DA  - 2022/2/2
TI  - Digital Approaches to Automated and Machine Learning Assessments of Hearing: Scoping Review
JO  - J Med Internet Res
SP  - e32581
VL  - 24
IS  - 2
KW  - audiology
KW  - automated audiometry
KW  - automatic audiometry
KW  - automation
KW  - digital health technologies
KW  - digital hearing health care
KW  - machine learning
KW  - remote care
KW  - self-administered audiometry
KW  - self-assessment audiometry
KW  - user-operated audiometry
KW  - digital health
KW  - hearing loss
KW  - digital hearing
KW  - digital devices
KW  - mobile phone
KW  - telehealth
AB  - 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 identify relevant reports from the peer-reviewed literature. Key information about each report’s scope and details was collected to assess the commonalities among the approaches. Results: A total of 56 reports from 2012 to June 2021 were included. From this selection, 27 unique automated approaches were identified. Machine learning approaches require fewer trials than conventional threshold-seeking approaches, and personal digital devices make assessments more affordable and accessible. Validity can be enhanced using digital technologies for quality surveillance, including noise monitoring and detecting inconclusive results. Conclusions: In the past 10 years, an increasing number of automated approaches have reported similar accuracy, reliability, and time efficiency as manual hearing assessments. New developments, including machine learning approaches, offer features, versatility, and cost-effectiveness beyond manual audiometry. Used within identified limitations, automated assessments using digital devices can support task-shifting, self-care, telehealth, and clinical care pathways. 
SN  - 1438-8871
UR  - https://www.jmir.org/2022/2/e32581
UR  - https://doi.org/10.2196/32581
UR  - http://www.ncbi.nlm.nih.gov/pubmed/34919056
DO  - 10.2196/32581
ID  - info:doi/10.2196/32581
ER  -