Data-Driven Audiogram Classification for Mobile Audiometry
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.
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