Improving communication outcomes in children with cleft palate in rural India

项目总结/摘要 唇腭裂儿童的治疗包括唇腭裂的一期手术修复， 其次是二级治疗，如咽瓣手术和语言治疗的目的， 改善言语效果和生活质量。印度每年出生的儿童数量位居世界第二 与中电在世界上，仅次于中国。在印度等中低收入国家， 患有唇腭裂的儿童生活在农村地区，在唇腭裂初次手术损伤后仍得不到治疗。 印度医学研究理事会（ICMR）进行的一项研究报告说，50-55%的儿童 CLP需要后续手术来改善语言，但绝大多数这些儿童没有得到适当的治疗。 有限的临床服务。在印度的大多数农村CLP案例中，干预在小学后结束 手术和患者及其家属接受作为障碍的结果的较差的言语结果。 这是不幸的，因为很多时候，这些言语问题是可以通过二次手术或 语言治疗;然而，资源和知识，以确定谁将受益于这些 干预措施集中在城市地区，农村地区的干预措施不知道额外的治疗 是可能.为了确定是否有必要进行二次手术或语言治疗，临床医生会倾听 儿童讲话时的鼻音过重。这是演讲中的一个微妙的暗示， 存在可纠正的导致言语清晰度降低的咽喉功能障碍。这是一个高度 农村诊所的社区卫生工作者没有接受过培训。因此，在本发明中， 农村地区的儿童得不到二级干预措施的好处， 言语效果和生活质量。 我们提出了一个基于人工智能（AI）的移动的健康（mHealth）应用程序， 识别将从改善沟通的干预措施中受益的CLP儿童 在印度农村地区取得的成果。这是一个多学科的合作项目， 印度理工学院达瓦德和全印度言语和听力研究所。我们 将使用现有的卡纳达语（印度的主要语言）CLP儿童语音数据库， 为印度语言开发一个hypnasality预测算法;我们将把这个算法整合到 移动健康应用程序（R21：SA 1）。我们将与AIISH合作，对该应用程序进行初步临床评估， 算法（R21：SA 2），然后根据该评估（R33：SA 3）完善应用程序和算法。最后， 我们将向社区卫生工作者分发移动健康应用程序，以便在农村诊所进行评估（SA 4和 SA5）。将实现应用程序开发以及应用程序初始验证和可用性的里程碑 从R21到R33。

Project Summary / Abstract The treatment of children with cleft lip and palate (CLP) involves the primary surgical repairment of the cleft, followed by secondary treatments like pharyngeal flap surgery and speech therapy for the purpose of improving speech outcomes and quality of life. India has the second highest number of children born yearly with CLP in the world, second only to China. In lower- and middle-income countries like India, most of the children with CLP live in rural areas and remain untreated after the primary surgical impairment of the cleft. A study conducted from Indian Council of Medical Research (ICMR), reported that 50-55% of children with CLP require follow up surgery to improve speech, but the vast majority of these children are not treated due to limited access to clinical services. In most rural CLP cases in India, intervention ends post-primary surgery and patients and their families accept poor speech outcomes as a consequence of the disorder. This is unfortunate as, many times, these speech problems are correctable via the secondary surgery or speech therapy; however, the resources and know-how for determining who would benefit from these interventions are clustered in urban areas and those in rural regions are unaware that additional treatment is possible. To determine if secondary surgery or speech therapy are necessary, clinicians listen for signs of hypernasality in the speech produced by children. This is a subtle cue in the speech that indicates the presence of correctible velopharyngeal dysfunction resulting in reduced speech intelligibility. This is a highly specialized skill that community healthcare workers in rural clinics have not been trained in. As a result, children in rural areas go without the benefit of secondary interventions that have the potential to improve speech outcomes and quality of life. We propose an artificial intelligence (AI)-based mobile health (mHealth) application for the identification of children with CLP who would benefit from interventions to improve communication outcomes in the rural areas of India. This is a multidisciplinary, collaborative project between Arizona State University, the Indian Institute of Technology Dharwad, and All India Institute of Speech and Hearing. We will use an existing database of speech from children with CLP in Kannada, a primary language in India, to develop a hypernasality prediction algorithm for the Indian language; we will integrate this algorithm within an mHealth app (R21: SA1). We will work with AIISH to perform an initial in-clinic evaluation of the app and the algorithms (R21: SA2), then refine the app and algorithms based on this evaluation (R33: SA3). Finally, we will disseminate the mHealth app to community health workers for evaluation in rural clinics (SA4 and SA5). Milestones for development of the app and initial validation and usabililty of the app will be achieved in order to move from the R21 to the R33.