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: 10468986
• 负责人: Susan Davis Emmett
• 金额: $ 18.55万
• 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/10468986
• 关键词: Achievement; Address; Advocacy; Affect; Alaska; Algorithms; Audiology; Award; Caring; Cellular Phone; Child; Child Care; Clinical; Cluster randomized trial; Community Health Aides; Computer software; Country; Data; Data Science; Data Scientist; Database Management Systems; Development; Device Designs; Device or Instrument Development; Devices; Diagnostic; Ear; Ecosystem; Employment Opportunities; Engineering; Environment; Evaluation; Feasibility Studies; Feedback; Funding; Future; Goals; Hearing; High Prevalence; Impairment; Individual; Infection; Information Systems; Infrastructure; International; Language Development; Machine Learning; Masks; Methodology; Methods; Neonatal Screening; Nursery Schools; Performance; Periodicity; Persons; Phase; Population; Preschool Child; Prevalence; Preventive healthcare; Public Health; Research; Resource-limited setting; Risk; Schools; Secure; Site; Software Engineering; South Africa; South African; Tablets; Technology; Testing; Training; Triage; Tympanometry; Universities; Validation; Widespread Disease; World Health Organization; childhood hearing loss; cloud based; cost; design; digital; ear infection; early childhood; empowered; engineering design; health information technology; hearing impairment; hearing screening; improved; innovation; large datasets; low and middle-income countries; mHealth; machine learning algorithm; member; middle ear disorder; programs; prototype; public health relevance; recruit; rural Alaska; school health; screening; screening program; software development; teacher community; technology development; tool; user centered design; validation studies

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.

抽象的 听力损失是全球第二大损害。儿童期听力损失会影响终生 对低收入和中等收入国家 (LMIC) 的个人影响尤为严重。高达75%的童年 由于感染相关听力损失的发生率很高，中低收入国家的听力损失是可以预防的。学校听证会 在资源匮乏的环境中，筛查对于识别儿童听力损失至关重要，在这些环境中，新生儿筛查 不可用。然而，大多数筛选程序仅使用纯音筛选，不能识别中间音 耳病在感染相关听力损失高发人群中普遍存在。这是因为 用于临床识别中耳疾病的鼓室导抗测试价格昂贵，并且是为经过培训的专业人员设计的。 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 将该技术转变为一种可以在中低收入国家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无法解决这一问题。我们的 研究团队由听力损失、听力学、数据科学、工程、用户领域的国际领导者组成 中低收入国家的集中设计和设备开发。我们还与比勒陀利亚大学hearX合作 开发唯一经过验证的 mHealth 纯音筛查设备的衍生公司。测试这个新设备 在适当的中低收入国家环境中，我们与 Global HEAR 的南非站点合作 Collaborative，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。我们最近在一项大型集群随机试验中记录了对该设备的需求 在阿拉斯加农村完成，鼓室导抗测试显着提高了学校听力筛查的准确性 在与感染相关的听力损失患病率较高的人群中。使用本次试验和试点资金的数据， 我们正在开发一种用于非专业筛查人员的机器学习鼓室导抗测试算法，以及早期的硬件原型 制作正在进行中。在目标 1 中，我们将使用以用户为中心的设计方法来完善硬件原型， 在实验室环境中进行测试期间，循环纳入南非团队成员的反馈。瞄准 2、我们将通过以用户为中心的设计来开发集成机器学习算法和 完善的硬件原型。由此产生的 mHealth 鼓室计将进入 R33 阶段。技术 通过将 mHealth 鼓室计与现有的集成，开发将在目标 3 和 4 中完成 健康信息技术和对南非 15 名学龄前儿童进行的早期可行性研究，以优化 为非专业用户设计的设备。在目标 5 中，我们将通过非专业筛查人员验证 mHealth 鼓室计 对南非 500 名学龄前儿童进行的临床表现研究。这项技术是通过 中低收入国家环境中的合作和测试将使教师和社区卫生工作者能够识别 处于可预防听力损失风险的儿童。全球 HEAR 合作组织将为未来提供基础设施 对中低收入国家的拟议设备进行研究，直接解决全球儿童听力损失的差异。