Automating classification of ShoeBOX audiograms

清水诊所的ShoeBOX产品可以在现场和音箱外完成听力测试。它已被广泛采用，包括在工业环境中监测职业健康和安全的听力。一旦受试者在不同频率下的听力灵敏度被量化，下一步就是对听图进行分类，以表征任何检测到的听力损失。平坦型、倾斜型、上升型和缺口型等分类进一步规定了对称/不对称、病变部位和听力损失的整体严重程度。自动化这种听力图分类是迈向全自动测试和诊断听力学系统的第一步。该项目将利用机器学习和信号处理技术来创建数据驱动的自动化和半自动听力图分类系统，适合作为ShoeBOX系统的一部分部署。多亏了shoeboxes的广泛应用，清水诊所现在拥有了大量未分类的听力图数据集。将开发一个快速听力图注释系统，以便专家听力学家可以对这些听力图进行分类。这个丰富的黄金标准数据集将被挖掘，以开发一个智能的自动分类系统，使用机器学习的方法，包括规则提取和其他。还将开发半自动分类系统，用户可以查看推荐的分类，并根据自己的专业知识和其他上下文信息进行快速调整。最终交付的产品将包括音频标注环境的工作软件原型、半自动和全自动分类系统，以及详细描述其开发和评估的技术文档

Clearwater Clinicals ShoeBOX product enables audiometry testing to be completed in the field and outside of the sound booth. Ithas already been widely adopted, including monitoring hearing in the context of occupational health and safety in industrialsettings. Once a subjects hearing sensitivity has been quantified at various frequencies, the next step is to classify the audiogramin order to characterize any detected hearing loss. Classifications such as flat, sloping, rising and notched are further specified asbeing symmetric/asymmetric, the site of lesion, and the overall severity of hearing loss. Automating such audiogram classificationis the first step towards a fully automated testing and diagnostic audiology system. This project will leverage machine learning andsignal processing techniques to create data-driven automated and semi-automated audiogram classification systems, suitable fordeployment as part of the ShoeBOX system. Thanks to ShoeBOXs wide deployment, Clearwater Clinical now possesses a largedataset of unclassified audiograms. A rapid audiogram annotation system will be developed such that expert audiologists canclassify these audiograms. This rich gold standard dataset will be mined to develop an intelligent automated classification systemusing methods from machine learning including rule extraction and others. A semi-automated classification system will also bedeveloped, where users are able to view the recommended classification and make rapid adjustments based on their ownexpertise and additional contextual information. Final deliverables will include working software prototypes of the audiogramannotation environment, semi- and fully-automated classification systems, as well as technical documentation detailing theirdevelopment and evaluation..Ontario