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/摘要 无法进行口头交流是人类所有疾病中最令人衰弱的一种。在 沟通障碍领域的言语语言病理学家(SLP‘s)的知觉质量评价 言语产出是评估和记录治疗进展的金标准。然而， 几十年的研究证实，对言语的听觉-知觉判断天生就是有偏见的， 这会影响可靠性。原因是人类的知觉系统是适应性的，与知觉 由于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 职业生涯中的人口数量。因此，要在主观上可靠地记录治疗结果，有必要 让多个不熟悉的听众参与进来。这在大多数临床环境中是站不住脚的，这意味着主观的 印象是由治疗的临床医生产生的。对主观评价的依赖直接破坏了 临床实践的质量和临床医生证明干预效果的能力。 Autal Analytics开发了新的客观语音度量标准，可以可靠地测量语音 患有神经性疾病的人群。它的技术基于强大的科学前提，并一直 制药公司和神经学家在临床研究中很早就采用了这种方法。音频分析公司收集了 并利用其技术对数万个语音样本进行了分析，结果显示 它的测量比其他方法更健壮、可靠，对语音的纵向变化更敏感 现有的结果衡量标准。我们成功地完成了SBIR第一阶段项目，目的是将我们的 技术应用于SLP临床实践。这个第二阶段的提案自然是建立在我们之前工作的基础上，通过连接 在第一阶段到第三阶段补充临床完成的基于APP的自动化结果测量 基准。具体地说，SA1将针对美国人验证听觉分析语音测量 言语-语言-听力协会(AHA)针对运动的功能性沟通措施(FCM) 语音；句子可理解性测试；以及语音特征的专家评级。此外，年龄和 所有客观措施的性别标准将从收集600名新健康人群的数据中获得 参与者。在SA2中，听觉分析将与执业的语音语言病理学家一起进行可用性研究 评估现实世界的效用并改进用户体验。这项提案的交付成果将是 功能齐全的移动应用程序，通过在临床环境中实践SLP进行验证，具有实时语音 根据现有的社区接受的衡量标准进行验证的结果指标。这将产生客观的结果 符合专业标准工作流程的结果，从而加快了我们实现 商业化。