Data-Driven Audiogram Classification for Mobile Audiometry

Abstract

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.

Figure 1

The Rapid Audiogram Annotation Environment has (A) an efficient user-interface and (B) a scalable cloud architecture.

Figure 2

Intra-rater reliability calculated from 50 audiogram replicates (error bars represent the standard error from the mean).

Figure 3

Inter-rater reliability between three professional audiologists for 270 audiograms (error bars represent the standard error from the mean).

Figure 4

Configuration classification decision forest.