Development and Pilot Testing of Smartphone-Based Hearing Test Application

Abstract

Background: Identifying and treating hearing loss can help improve communication skills, which often leads to improved quality of life. Many people do not seek medical treatment and, therefore, go undiagnosed for an extended period before realizing they have hearing loss. This study presents a self-administered, low-cost, smartphone-based hearing test application (HearTest) to quantify the pure-tone hearing thresholds of a user. The HearTest application can be used with commercially available smartphone devices and an earphone with the mentioned specification. Methods: Air-conduction-based pure-tone audiometry for the smartphone application was designed and implemented to detect hearing thresholds using a traditional “10 dB down and 5 dB up” approach. Employed smartphone-earphone combination was calibrated with respect to a GSI-61 audiometer and insert earphone ER-3A to maintain clinical standards with the help of subjective testing on 20 normal-hearing (NH) participants. Results: Further subjective testing on 14 participants with NH and retesting on five participants showed that HearTest achieves high-accuracy audiogram within clinically acceptable limits (≤10 dB HL mean difference) when compared with the reference clinical audiometer. Hardware challenges and limitations in air-conduction-based hearing tests through smartphones and ways to improve their accuracy and reliability are discussed. Conclusion: The proposed smartphone application provides a simple, affordable, and reliable means for people to learn more about their hearing health without needing access to a formal clinical facility.

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

“…After clustering identical approaches, 27 approach clusters remained, including 2 that used machine learning. Extracted data items and grading of results on approaches are provided in Multimedia Appendix 4 [ 21 , 23 , 27 - 30 , 32 - 89 ]. The specifications of the reported accuracy, reliability, and time efficiency are described in Table 1 .…”

“…Most of the automated techniques (14/27, 52%) expressed accuracy in RMSD. Other types of analyses used average differences and SD (10/27, 37%), average thresholds and SD (1/27, 4%) [ 32 ], linear regression and correlation coefficients (1/27, 4%) [ 33 ], and analysis of variance (1/27, 4%) [ 34 ]. The types of analysis used can be seen in Multimedia Appendix 5 [ 23 , 32 - 37 , 39 , 40 , 43 , 45 , 48 - 50 , 57 - 59 , 65 , 67 , 68 , 70 , 74 , 77 , 81 , 83 - 85 ].…”

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

“…28 Automated audiometry was applied across a range of populations. All approaches were applied to adults except for Patel et al 29 who only included children in their study. Eight approaches were validated in children, including three approaches that designed a child-friendly user-interface.…”

“…Eight approaches were validated in children, including three approaches that designed a child-friendly user-interface. [29][30][31] Other test populations were elderly, 32 veterans, 33 and persons exposed to occupational noise 34 or ototoxic substances. 35 Automated audiometry has also been applied as an alternative in low-resource environments for traditional manual audiometry.…”

“…Six approaches used an unconventional calibration technique. Patel et al 29 determined a reference equivalent threshold level (RETSPL) for air conduction for the specific phone-headphone combination using manual audiometry as a reference. Masalski et al 44 used reference levels for calibration for smartphone and transducer combinations, collected in uncontrolled conditions in normal-hearing persons.…”