Validating an objective assessment of speech outcomes of children with cleft palate pre and post secondary surgery

验证对腭裂儿童二次手术前后言语结果的客观评估

• 批准号: 10667250
• 负责人: Visar Berisha
• 金额: $ 37.02万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10667250
• 关键词: 5 year old; 7 year old; Acoustics; Age; Algorithms; Artificial Intelligence; Behavior Therapy; Birth; Characteristics; Child; Cleft Palate; Cleft lip with or without cleft palate; Clinical; Clinical Research; Data; Databases; Evaluation; Exhibits; Functional disorder; Funding; Genetic Transcription; Goals; Gold; Language; Measurement; Measures; Modeling; Monitor; Multi-Institutional Clinical Trial; National Institute of Dental and Craniofacial Research; Operative Surgical Procedures; Oral; Outcome; Palate; Pathologist; Population Distributions; Production; Protocols documentation; Research; Sampling; Schedule; Secondary to; Speech; Speech Sound; Speech-Language Pathology; Statistical Models; Surgical Management; System; Time; Training; United States National Institutes of Health; Validation; artificial intelligence algorithm; automated algorithm; base; cleft lip and palate; craniofacial; decision research; effectiveness validation; follow-up; impression; palate repair; pressure; primary outcome; prospective; technology validation; tool; two-dimensional

Project Summary/Abstract Perceptual assessment of hypernasality is considered a critical component when evaluating the speech of children with cleft lip and/or palate (CLP). However, most speech-language pathologists (SLPs) do not receive formal training for perceptual evaluation of speech and, as a result, research shows that the subjective ratings are inherently biased to the perceiver and exhibit considerable variability. In this project, we aim to validate an artificial intelligence (AI) algorithm that automatically evaluates speech along two dimensions deemed to be critically important by the Americleft Speech Outcomes Group (ASOG), namely hypernasality and speech acceptability. An AI algorithm was developed in an NIH-funded R21 (DE026252, PIs Scherer & Berisha) based on an existing database of 5-7 year olds with CLP from the Americleft Speech Outcomes Project. The AI tool was validated on a small prospective sample of 5-7 year olds with CLP. This proposal has two aims. The first aim will extend the speech samples to children 3-5 years of age with CLP to cover the typical ages when decisions about secondary palatal management are considered and to collect comparison speech samples from noncleft children within this age range. The second aim will validate the AI tool with a cross-sectional and longitudinal sample of children who are receiving a secondary palatal management in a multi-center clinical trial (VPI-OPS, Dr. Thomas Sitzman, PI). The results will provide validation of the automated tool to objectively quantify and track speech production in children with CLP.

项目总结/摘要 在评估发音时，对鼻音过强的感知评估被认为是一个关键组成部分。 唇腭裂儿童（CLP）。然而，大多数言语语言病理学家（SLP）不 接受正式的语音感知评估培训，因此，研究表明， 主观评价固有地偏向于感知者，并且表现出相当大的可变性。在这个项目中， 我们的目标是验证一种人工智能（AI）算法，该算法可以自动评估语音沿着两个 被美国左翼演讲成果小组（ASOG）认为至关重要的维度，即 鼻音过重和言语可接受性。在NIH资助的R21（DE 026252， PI谢勒& Berisha）基于来自Americleft Speech的5-7岁CLP奥尔兹的现有数据库 成果项目。AI工具在5-7岁CLP奥尔兹的小前瞻性样本上进行了验证。 这项建议有两个目的。第一个目标是将语音样本扩展到3-5岁的儿童， CLP将涵盖考虑二级腭部管理决定时的典型年龄， 从这个年龄段的非唇裂儿童中收集比较语音样本。第二个目标将 用接受中学教育的儿童的横截面和纵向样本验证人工智能工具 多中心临床试验中的腭部管理（VPI-OPS，托马斯西茨曼博士，PI）。结果将 提供自动化工具的验证，以客观地量化和跟踪儿童的言语产生 关于CLP