Non-Contact Solution for Quantitative Clinical Management of MTD

MTD 定量临床管理的非接触式解决方案

• 批准号: 10256594
• 负责人: Jennifer Michele Vojtech
• 金额: $ 25.64万
• 依托单位: ALTEC, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2023-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10256594
• 关键词: Acoustics; Address; Affect; Agreement; Auditory; Auditory Perception; Behavior; Boston; COVID-19; Caring; Clinic; Clinical; Clinical Management; Communication; Complement; Computational algorithm; Computer software; Consumption; Databases; Development; Devices; Dysphonia; Endoscopy; Equilibrium; Exercise; Feedback; Foundations; Funding; Goals; Health; Image; Individual; Laboratories; Language; Language Disorders; Laryngeal muscle structure; Larynx; Lead; Loudness; Manipulative Therapies; Manuals; Massage; Measurable; Measures; Medical; Methods; Monitor; Muscle; Muscle Tension; National Institute on Deafness and Other Communication Disorders; Neck; Operative Surgical Procedures; Otolaryngologist; Outcome Measure; Pain; Palpation; Pathologist; Patients; Peer Review; Performance; Persons; Phase; Procedures; Protocols documentation; Quality of life; Research Personnel; Risk; Sampling; Series; Signal Transduction; Site; Small Business Innovation Research Grant; Social Interaction; Software Tools; Specialist; Speech; Speech Disorders; System; Technology; Testing; Time; Translating; Translations; United States National Institutes of Health; Universities; Visit; Voice; Voice Disorders; Work; automated algorithm; base; behavioral response; chronic pain; clinical care; clinical infrastructure; clinical practice; compliance behavior; cost; data management; design; evidence base; impression; improved; meetings; microphone; new technology; non-compliance; point of care; prevent; prototype; remote assessment; remote health care; signal processing; software development; sound; telehealth; tool; treatment response; vocal cord; vocalization; voice therapy

This Phase I SBIR will develop a new clinical software that can be used by Speech Language Pathologists (SLPs) to inform the assessment and treatment of Muscle Tension Dysphonia (MTD), one of the most common voice disorders in clinical practice. MTD is characterized by abnormal coactivation of muscles in and around the larynx that causes vocal strain - leading to discomfort and pain that impede vocal communication and may lead to surgery. While voice therapy can mitigate these health risks by reducing laryngeal tension and restoring muscle balance, the current standard for guiding therapy is by manual palpations and auditory-perceptual impressions – highly subjective methods with poor inter-rater reliability. The most direct approach to quantitative measures of laryngeal muscle tension is manual analysis of vocal fold images from an endoscopy – an invasive and time- consuming method that is not clinically viable for routine assessment and management of MTD. These methods are also incompatible with the recent need for technology that facilitates remote assessment in the wake of COVID-19. We will address these needs by developing a new non-contact software tool that provides accurate, valid, acoustic measures of MTD during speech exercises involving the offset and onset of vocalization. This approach has been shown in our prior NIH-funded studies to elicit measurable acoustic changes associated with the presence and absence of MTD and following voice therapy. Leveraging this clinical foundation, our team of signal processing experts at Altec Inc. will partner with speech researchers, SLPs and otolaryngologists at Boston University to translate our preliminary laboratory-based software into a turn-key clinical tool to support both in-person and remote assessment and management of MTD. We will do so by translating our existing time- consuming manual approach for measuring laryngeal tension into clinical software that uses fully-automated algorithms to achieve high accuracy when tested on an existing database of N=450 individuals with and without MTD (Aim 1). The automated algorithms will be complemented by software to guide patient compliance with the MTD-specific speech exercises and provide SLPs with quantitative outcome measures for a robust, clinically- viable implementation (Aim 2). Aim 3 will evaluate our MTD software prototype by N=4 SLPs in N=12 patients with MTD to demonstrate the proof of concept that: 1) it provides accurate measures compared to validated manual procedures; 2) it detects changes in laryngeal tension due to increased vocal effort or laryngeal massage therapy with >95% agreement to manual procedures; and 3) it achieves favorable ratings of feasibility, acceptability and perceived value by patients and SLPs. Phase II will advance our Phase I prototype with real- time calculations, an acoustic signal quality monitor, and a HIPPA-compliant data management infrastructure for clinical point-of-care or remote use. It’s reliance on low-cost, readily available, non-contact sensing provides a practical and effective tool to support in-person and remote clinical care for those with MTD that advances the NIDCD priorities for technologies to improve health among individuals with speech and language disorders. 1

第一阶段SBIR将开发一种新的临床软件，可供言语语言病理学家（SLP）使用。 为肌肉紧张性发声障碍（MTD）的评估和治疗提供信息，MTD是最常见的声音之一 临床实践中的疾病。MTD的特征是喉内和喉周围肌肉的异常共激活 导致声音紧张-导致不适和疼痛，阻碍声音交流，并可能导致 手术而声音治疗可以减轻这些健康风险，减少喉紧张和恢复肌肉 平衡，目前指导治疗的标准是通过手动触诊和触觉感知印象 - 高度主观的方法，评分者之间的可靠性差。最直接的量化方法 喉肌张力的最佳方法是对内窥镜检查的声带图像进行手动分析-这是一种侵入性和时间性的方法， 对于MTD的常规评估和管理而言，消耗方法在临床上不可行。这些方法 也不符合最近对技术的需求，这种技术有助于远程评估， 2019冠状病毒病。我们将通过开发一种新的非接触式软件工具来满足这些需求， 言语练习期间MTD的有效声学测量，涉及发声的抵消和开始。这 在我们先前的NIH资助的研究中，已经显示了一种方法，可以引起与 MTD的存在和不存在以及随后的语音治疗。利用这一临床基础，我们的团队 Altec Inc.的信号处理专家将与波士顿的语音研究人员、SLP和耳鼻喉科医生合作 大学将我们初步的基于实验室的软件转化为交钥匙临床工具，以支持 亲自和远程评估和管理MTD。我们要把我们现有的时间- 用于测量喉张力的消耗手动方法到使用全自动 算法，以实现高准确性时，测试现有的数据库N=450个人， MTD（目标1）。自动算法将由软件补充，以指导患者遵守 MTD特定的语言练习，并为SLP提供定量结果测量，以获得稳健的临床- 可行的实施（目标2）。目标3将通过N=12例患者的N=4个SLP评价我们的MTD软件原型 与MTD一起证明概念验证：1）与经验证的 手动程序; 2）它检测由于发声努力增加或喉部按摩引起的喉部张力变化 与手动程序的一致性>95%的治疗;以及3）其实现了有利的可行性评级， 患者和SLP的可接受性和感知价值。第二阶段将用真实的- 时间计算、声信号质量监控器和符合HIPPA标准的数据管理基础设施 用于临床护理点或远程使用。它依赖于低成本、容易获得的非接触式传感器， 一个实用有效的工具，支持对MTD患者的面对面和远程临床护理， NIDCD优先考虑改善言语和语言障碍患者健康的技术。 1