Automated objective outcome measures for clinical use in dysarthria

构音障碍临床应用的自动化客观结果测量

• 批准号: 10323563
• 负责人: Sherman Charles
• 金额: $ 73.44万
• 依托单位: AURAL ANALYTICS, INC.
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-07 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10323563
• 关键词: Acoustics; Adopted; Age; American Speech-Language-Hearing Association; Auditory; Benchmarking; Characteristics; Clinical; Clinical Research; Communication; Communication impairment; Communities; Computer software; Data; Data Collection; Development; Dysarthria; Evaluation; Familiarity; Feedback; Gender; Gold; Health; Health Insurance Portability and Accountability Act; Human; Impairment; Individual; Infrastructure; Intervention; Interview; Judgment; Language; Learning; Loudness; Measures; Motor; Neurologist; Outcome; Outcome Measure; Participant; Pathologist; Patient Care; Patient Recruitments; Patients; Performance; Pharmacologic Substance; Phase; Population; Population Distributions; Positioning Attribute; Production; Regression Analysis; Research; Resources; Sampling; Secondary to; Small Business Innovation Research Grant; Speech; Speech Acoustics; Speech Disorders; Speech Intelligibility; Speech Therapy; Surveys; System; Technology; Testing; Thinking; Time; Translating; Treatment Efficacy; Treatment outcome; Update; Work; base; career; clinical outcome measures; clinical practice; clinically relevant; commercialization; crowdsourcing; experience; impression; improved; insight; interest; mobile application; nervous system disorder; patient population; professional atmosphere; prospective; recruit; statistics; usability

Abstract / Summary The inability to engage in spoken communication is among the most debilitating of all human conditions. In the field of communication disorders, a speech-language pathologist’s (SLP’s) perceptual evaluation of the quality of speech production is the gold standard for assessment and for documenting treatment progress. However, decades of research have confirmed that auditory-perceptual judgments of speech are inherently biased, which compromises reliability. The reason is that the human perceptual system is adaptive, with perceptual bias accrued by working with an individual across multiple treatment sessions, or by working with patient populations across a career. Thus, to reliably document treatment outcomes subjectively, it is necessary to involve multiple, unfamiliar listeners. This is untenable in most clinical settings, which means that subjective impressions are made by the treating clinicians. The reliance on subjective evaluation directly undermines the quality of clinical practice and a clinician’s ability to demonstrate the efficacy of an intervention. Aural Analytics has developed new objective acoustic speech metrics that reliably measure speech in populations with neurological disorders. Its technology is based on a strong scientific premise and has been adopted early by pharmaceutical companies and neurologists in clinical research. Aural Analytics has collected and analyzed tens of thousands of speech samples using its technology, and the results are demonstrating that its measures are robust, reliable, and more sensitive to longitudinal changes in speech than are other existing outcome measures. We successfully completed a Phase I SBIR project with the aim of translating our technology to SLP clinical practice. This Phase II proposal naturally builds on our previous work by connecting the automated app-based outcome measures completed in Phase I to three complementary clinical benchmarks. Specifically, SA1 will validate the Aural Analytics speech measures against the American Speech-Language-Hearing Association’s (ASHA) Functional Communication Measures (FCM) for motor speech; the Sentence Intelligibility Test; and expert ratings of speech characteristics. In addition, age and gender-based norms for all objective measures will be obtained from collection of data from 600 new healthy participants. In SA2, Aural Analytics will conduct a usability study with practicing speech-language pathologists to assess real world utility and refine the user experience. The deliverable of this proposal will be a fully-functional mobile application, validated by practicing SLPs in a clinical setting, with real time speech outcome metrics validated with respect to existing community-accepted measures. This will result in objective outcomes that fit into the workflow of the professional standard, thereby expediting our path to commercialization.

摘要/总结 无法进行口头交流是所有人类状况中最令人衰弱的状况之一。 沟通障碍领域，言语病理学家 (SLP) 对质量的感知评估 言语产生的能力是评估和记录治疗进展的黄金标准。 数十年的研究证实，言语的听觉感知判断本质上是有偏见的， 其原因是人类感知系统具有适应性，具有感知能力。 通过在多个治疗过程中与个人合作或与患者合作而产生的偏见 因此，为了可靠地主观记录治疗结果，有必要 涉及多个不熟悉的听众，这在大多数临床环境中是站不住脚的，这意味着主观性。 对主观评价的依赖直接破坏了忠诚者的印象。 临床实践的质量和临床医生证明干预措施有效性的能力。 Aural Analytics 开发了新的客观声学语音指标，可以可靠地测量以下环境中的语音： 其技术基于强有力的科学前提，并已得到广泛应用。 早期被制药公司和神经科医生在临床研究中采用。 并利用其技术分析了数万个语音样本，结果表明 与其他方法相比，其测量方法稳健、可靠，并且对语音的纵向变化更敏感 我们成功完成了第一阶段 SBIR 项目，旨在转化我们的成果。 该第二阶段提案自然地建立在我们之前的工作基础上，通过连接。 在第一阶段完成的基于应用程序的自动化结果测量到三个补充临床 具体来说，SA1 将针对美国人验证 Aural Analytics 语音测量。 言语听力协会 (ASHA) 运动功能沟通测量 (FCM) 言语；句子清晰度测试；以及言语特征的专家评级。 所有客观措施的基于性别的规范将从收集 600 名新健康人的数据中获得 在 SA2 中，听觉分析将与执业言语病理学家一起进行可用性研究。 评估现实世界的效用并完善用户体验。该提案的可交付成果将是 全功能移动应用程序，通过在临床环境中练习 SLP 进行验证，具有实时语音功能 根据现有社区接受的措施验证结果指标这将产生客观的结果。 符合专业标准工作流程的结果，从而加快我们的道路 商业化。