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Mining Audiograms to Improve the Interpretability of Automated Audiometry Measurements

机译：挖掘听力图以提高自动听力测量测量的可解释性

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摘要

Many people with hearing loss are unaware of it and do not seek benefit from available interventions such as hearing aids. This is in part due to the limited accessibility to qualified hearing healthcare providers in developing and developed countries alike. Automated audiometry, which has gained in popularity amidst the torrent of advances in telemedicine and mobile health, makes it possible to deliver basic hearing tests to remote or otherwise underserved communities at low cost. While this technology makes it possible to perform hearing assessments outside of a sound booth, many individuals administering the test are non-specialists, and thus, have a limited ability to interpret audiometric measurements and to make tailored recommendations. In this paper, we present the first steps towards the development of a flexible, supervised learning approach for the classification of audiograms in terms of their shape, severity and symmetry. More specifically, we outline our approach to building a set of non-redundant, annotation-ready audiograms from a much larger dataset. In addition, we present a Rapid Audiogram Annotation Environment (RAAE) designed specifically for the collection of audiogram annotations from a large community of expert audiologists. Preliminary results indicate that annotations provided through our environment are consistent leading to low intra-coder variability. Data gathered through the RAAE will form the basis of learning algorithms to help non-experts make better decisions from audiometric data.
机译：许多听力丧失的人没有意识到这一点，也没有寻求从助听器等现有干预措施中受益的信息。部分原因是在发展中国家和发达国家，合格的听力保健提供者都难以获得。自动遥测技术在远程医疗和移动医疗的迅猛发展中广受欢迎，它使得以低成本向偏远或服务欠缺的社区提供基本的听力测试成为可能。尽管这项技术可以在音棚外进行听力评估，但是许多执行该测试的人都是非专家，因此解释测听测量和提出定制建议的能力有限。在本文中，我们介绍了针对听力图的形状，严重性和对称性进行分类的灵活，监督学习方法的开发的第一步。更具体地说，我们概述了从更大的数据集中构建一组非冗余，可注解的听力图的方法。此外，我们还提供了一种快速听力图注释环境（RAAE），该环境是专门为从大量专业听力学家社区收集听力图注释而设计的。初步结果表明，通过我们的环境提供的注释是一致的，从而导致编码器内部的可变性较低。通过RAAE收集的数据将构成学习算法的基础，以帮助非专家根据测听数据做出更好的决策。

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