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Data-Driven Audiogram Classification for Mobile Audiometry
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DOI：10.1038/s41598-020-60898-3.png)
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Abstract
En 中文
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.
Keywords:
HEARING-LOSS
OLDER-ADULTS
CHILDREN
AGE
AUDIOLOGY
LANGUAGE
IMPAIRMENT
PREVALENCE
VALIDATION
AGREEMENT
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