Development and Evaluation of a Novel Method for Adult Hearing Screening: Towards a Dedicated Smartphone App

Towards implementation of adult hearing screenng tests that can be delivered via a mobile app, we have recently designed a novel speech-in-noise test based on the following requirements: user-operated; language independent; fast; reliable; accu-rate; and viable for testing at a distance. This study addresses specific models to (i) investigate the ability of the test to identify ears with mild hearing loss using machine learning; and (ii) address the range of the output levels generated using different transducers. Our results demonstrate that the test classification perfor-mance using decision tree models is in line with the performance of validated speech-in-noise tests. We observed, on average, 0.75 accuracy, 0.64 sensitivity and 0.81 specificity. Regarding the analysis of output levels, we demonstrated substantial variability of transducers' characteristics and dynamic range, with headphones yielding higher output levels compared to earphones. These findings confirm the importance of a self-adjusted volume option. These results also sug-gest that earphones may not be suitable for test execution as the output levels may be relatively low, particularly for subjects with hearing loss or for those who skip the volume adjustment step. Further research is needed to fully address test per-formance, e.g. testing a larger sample of subjects, addressing different classifica-tion approaches, and characterizing test reliability in varying conditions using dif-ferent devices and transducers.