Evaluation of Portable Tablet-Based Audiometry in a South Indian Population

et al. 2022

J Med Internet Res

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.

Section: Resultsmentioning

confidence: 99%

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

Wasmann¹,

Pragt²,

et al. 2022

J Med Internet Res

“…The New Early Warning Test (NEWT), which is incorporated inside an active communication earplug, serves as a reliable and efficient method to measure auditory thresholds, especially in the presence of high background noise Whitton et al, 2016 81 This study provides a proof of concept that several self-administered, automated hearing measurements at home are statistically equivalent to conventional audiometry in the clinic. Yeung et al, 2015 30,[82][83][84][85][86]…”

Section: Newtmentioning

confidence: 99%

Automated and machine learning approaches in diagnostic hearing assessment: a scoping review

Pragt¹,

Wasmann²,

et al. 2021

Preprint

Hearing loss affects one in five people worldwide and is estimated to affect one in four by 2050. Treatment relies on accurate diagnosis of hearing loss, but this first step is out of reach for more than 80% of those affected. Increasingly automated digital approaches are being developed for self-administered hearing assessment without professionals’ direct involvement. This scoping review provides an overview of automated approaches, based on 56 reports from 2012 until June 2021, adding to the 29 published prior to 2012. Twenty-seven automated approaches were identified. An increasing number of approaches report similar accuracy as manual hearing assessments. Machine learning approaches are more efficient and personal digital devices make assessments more affordable and accessible. Validity can be enhanced by quality surveillance including noise monitoring and detecting inconclusive results. Employed within identified limitations, automated hearing assessments can support task-shifting, self-care, and clinical care pathways.

Digital Approaches to Automated and Machine Learning Assessments of Hearing: Scoping Review (Preprint)

Wasmann¹,

Pragt²,

et al. 2021

Preprint

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. CLINICALTRIAL