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

mHealth 鼓室压力计：解决中低收入国家可预防的儿童听力损失问题的数字创新

• 批准号: 10614815
• 负责人: Susan Davis Emmett
• 金额: $ 10.05万
• 依托单位: UNIV OF ARKANSAS FOR MED SCIS
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10614815
• 关键词: mHealth; Tympanometer; Digital; Innovation; Address

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.

摘要 听力损失是世界上第二大损害。儿童听力损失具有终身影响， 对低收入和中等收入国家（LMICs）的影响尤为严重。高达75%的儿童期 低收入国家的听力损失是可以预防的，因为感染相关听力损失的患病率很高。学校听证会 筛查对于在低资源环境下识别儿童听力损失至关重要， 无法使用。然而，大多数筛查程序只使用纯音筛查，不识别中间 耳病在感染相关性听力损失患病率高的人群中普遍存在。这是因为 用于临床识别中耳疾病的鼓室测量法是昂贵的，并且是为受过训练的专业人员设计的。 我们的目标是开发和验证具有机器学习诊断支持的mHealth鼓室计 将这一技术转化为一种低成本的工具，可以在中低收入国家广泛传播， 听力损失的负担是最大的，并且当前的听力筛查方法尚未解决。我们 研究团队由听力损失，听力学，数据科学，工程，用户， 以LMIC为中心的设计和设备开发。我们还与比勒陀利亚的hearX大学合作 该公司开发了唯一经过验证的mHealth纯音筛查设备。来测试这个新装置 在适当的LMIC环境中，我们与全球HEAR的南非站点合作 合作，我们的合作者联盟来自28个国家，是唯一的国际研究网络 致力于听力损失。我们在最近的一项大型随机分组试验中记录了对这种设备的需求 在阿拉斯加的农村完成，那里的鼓室测量显著提高了学校听力筛查的准确性 感染相关性听力损失患病率高的人群中。利用这项试验和试点资金的数据， 我们正在开发一种用于普通筛查人员的机器学习鼓室图算法， 制造正在进行中。在目标1中，我们将使用以用户为中心的设计方法来改进硬件原型， 在实验室环境中进行测试期间，周期性地纳入南非团队成员的反馈。在Aim中 2.我们将通过以用户为中心的设计开发软件，该设计集成了机器学习算法， 精致的硬件原型由此产生的mHealth鼓室计将推进到R33阶段。技术 通过将mHealth鼓室计与现有的 在南非的15名学龄前儿童中进行了一项健康信息技术和早期可行性研究， 为外行用户设计的设备。在目标5中，我们将通过以下方式验证mHealth鼓室测试仪与层筛选器 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 儿童听力损失的风险可预防。全球HEAR协作将为未来提供基础设施 在中低收入国家使用拟议设备的研究，直接解决全球儿童听力损失的差异。