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: 10640837
• 负责人: Sherman Charles
• 金额: $ 71.25万
• 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 2024-03-06
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10640837
• 关键词: Acoustics; Adopted; Age; American Speech-Language-Hearing Association; Auditory; Benchmarking; Binding; Characteristics; Clinical; Clinical Research; Communication; Communication impairment; Communities; Computer software; Data; Data Collection; Development; Dysarthria; Evaluation; Familiarity; Feedback; Gender; 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; 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