Development and Validation of a Portable Hearing Self-Testing System Based on a Notebook Personal Computer

A Multicenter Validity Study of Four Smartphone Hearing Test Apps in Optimized and Home Environments

OBJECTIVE

Pure tone audiometry (PTA) is the gold standard for hearing assessment. However, it requires access to specialized equipment. Smartphone audiometry applications (apps) have been developed to perform automated threshold audiometry and could allow patients to perform self-administered screening or monitoring. This study aimed to assess the validity and feasibility of patients using apps to self-assess hearing thresholds at home, with comparison to PTA.

METHODS

A multi-center, prospective randomized study was conducted amongst patients undergoing PTA in clinics. Participants were randomly allocated to one of four publicly-available apps designed to measure pure tone thresholds. Participants used an app once in optimal sound-treated conditions and a further three times at home. Ear-specific frequency-specific thresholds and pure tone average were compared using Pearson correlation coefficient. The percentage of app hearing tests with results within ±10 dB of PTA was calculated. Patient acceptability was assessed via an online survey.

RESULTS

One hundred thirty-nine participants submitted data. The results of two at-home automated smartphone apps correlated strongly/very strongly with PTA average and their frequency-specific median was within ±10 dB accuracy. Smartphone audiometry performed in sound-treated and home conditions were very strongly correlated. The apps were rated as easy/very easy to use by 90% of participants and 90% would be happy/very happy to use an app to monitor their hearing.

CONCLUSION

Judicious use of self-performed smartphone audiometry was both valid and feasible for two of four apps. It could provide frequency-specific threshold estimates at home, potentially allowing assessments of patients remotely or monitoring of fluctuating hearing loss.

LEVEL OF EVIDENCE

2 Laryngoscope, 134:2864-2870, 2024.

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

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.