mHealth Tympanometer: A Digital Innovation to Address Preventable Childhood Hearing Loss in Low- and Middle-Income Countries

摘要 听力损失是全球第二大损伤。儿童听力损失具有终生影响， 不成比例地影响低收入和中等收入国家(LMIC)的个人。高达75%的童年 由于感染相关听力损失的高发病率，LMICs中的听力损失是可以预防的。学校听证会 在低资源环境中，筛查对于识别儿童听力损失至关重要，在那里，新生儿筛查 不可用。然而，大多数筛查程序只使用纯音筛查，不识别中间 耳病广泛存在于感染相关听力损失的高发人群中。这是因为 鼓室导纳用于临床识别中耳疾病，价格昂贵，专为训练有素的专业人士设计。 我们的目标是开发和验证具有机器学习诊断支持的mHealth鼓室压力计 将这项技术转变为一种可在LMIC中广泛传播的低成本工具，其中 听力损失的负担是最大的，目前的听力筛查方法没有解决这一问题。我们的 研究团队由听力损失、听力、数据科学、工程、用户等领域的国际领先企业组成。 在LMIC中以设计和器件开发为中心。我们还与比勒陀利亚大学的earX大学合作 衍生公司，开发了唯一经过验证的mHealth纯音筛查设备。为了测试这个新设备 在适当的LMIC环境下，我们与来自Global Hear的南非站点合作 协作性，我们由来自28个国家和地区的合作者组成的联盟是唯一的国际研究网络 致力于听力损失。我们在最近的一次大型整群随机试验中记录了对该设备的需求 在阿拉斯加农村地区完成，那里的鼓室导纳显著提高了学校听力筛查的准确性 在感染相关性听力损失发病率较高的人群中。利用这项试验和试点资金的数据， 我们正在开发一种用于平层筛选器的机器学习鼓室导纳算法，并正在开发早期的硬件原型 编造工作正在进行中。在目标1中，我们将使用以用户为中心的设计方法来优化硬件原型， 在实验室环境中进行测试期间，周期性地纳入来自南非团队成员的反馈。在AIM 2、我们将通过以用户为中心的设计来开发软件，将机器学习算法和 精致的硬件原型。由此产生的mHealth鼓室导抗将进入R33阶段。技术 将在AIMS 3和4中通过将mHealth鼓室与现有的 南非15名学龄前儿童健康信息技术和早期可行性研究 面向外行用户的设备设计。在目标5中，我们将通过一个 南非500名学龄前儿童的临床表现研究。这项技术是通过 LMIC环境中的伙伴关系和测试将使教师和社区卫生工作者能够识别 儿童面临可预防的听力损失的风险。全球听力协作将为未来提供基础设施 在LMIC中使用建议的设备进行研究，直接解决全球儿童听力损失的差异。

ABSTRACT Hearing loss is the second leading impairment worldwide. Childhood hearing loss has lifelong implications and disproportionately affects individuals in low- and middle-income countries (LMICs). Up to 75% of childhood hearing loss in LMICs is preventable due to the high prevalence of infection-related hearing loss. School hearing screening is critical for identification of childhood hearing loss in low resource settings, where newborn screening is unavailable. However, most screening programs only use pure-tone screening that does not identify middle ear disease widespread in populations with a high prevalence of infection-related hearing loss. This is because tympanometry, used to clinically identify middle ear disease, is expensive and designed for trained professionals. Our goal is to develop and validate an mHealth tympanometer with machine learning diagnostic support to transform this technology into a low-cost tool that could be broadly disseminated in LMICs, where the burden of hearing loss is greatest and is not addressed by current hearing screening methodology. Our study team is comprised of international leaders in hearing loss, audiology, data science, engineering, user- centered design, and device development in LMICs. We have also partnered with hearX, a University of Pretoria spinout company that developed the only validated mHealth pure-tone screening device. To test this new device in an appropriate LMIC setting, we have partnered with the South African site from the Global HEAR Collaborative, our consortium of collaborators from 28 countries that is the only international research network dedicated to hearing loss. We documented the need for this device in a large cluster randomized trial recently completed in rural Alaska, where tympanometry significantly improved the accuracy of school hearing screening in a population with a high prevalence of infection-related hearing loss. Using data from this trial and pilot funding, we are developing a machine learning tympanometry algorithm for lay screeners, and early hardware prototype fabrication is underway. In Aim 1, we will refine the hardware prototype using a user-centered design approach, cyclically incorporating feedback from South African team members during testing in a lab environment. In Aim 2, we will develop software through user-centered design that integrates the machine learning algorithm and refined hardware prototype. The resulting mHealth tympanometer will advance to the R33 phase. Technology development will be completed in Aims 3 and 4 through integration of the mHealth tympanometer with existing health information technology and an early feasibility study in 15 preschool children in South Africa to optimize device design for lay users. In Aim 5, we will validate the mHealth tympanometer with lay screeners through a clinical performance study in 500 preschool children in South Africa. This technology, developed through partnership and testing in an LMIC setting, will empower teachers and community health workers to identify children at risk for preventable hearing loss. The Global HEAR Collaborative will provide infrastructure for future studies with the proposed device across LMICs, directly addressing disparities in childhood hearing loss globally.