Recent mobile and automated audiometry technologies have allowed for the democratization of hearing healthcare and enables non-experts to deliver hearing tests. The problem remains that a large number of such users are not trained to interpret audiograms. In this work, we outline the development of a data-driven audiogram classification system designed specifically for the purpose of concisely describing audiograms. More specifically, we present how a training dataset was assembled and the development of the classification system leveraging supervised learning techniques. We show that three practicing audiologists had high intra- and inter-rater agreement over audiogram classification tasks pertaining to audiogram configuration, symmetry and severity. The system proposed here achieves a performance comparable to the state of the art, but is significantly more flexible. Altogether, this work lays a solid foundation for future work aiming to apply machine learning techniques to audiology for audiogram interpretation.
Scientific Reports
Department of Systems and Computer Engineering

Charih, F. (François), Bromwich, M. (Matthew), Mark, A.E. (Amy E.), Lefrançois, R. (Renée), & Green, J. (2020). Data-Driven Audiogram Classification for Mobile Audiometry. Scientific Reports, 10(1). doi:10.1038/s41598-020-60898-3