Al-Supported In-Home Brain Assessments for Older Adults and Persons with Alzheimer's Disease

项目摘要 500多万美国人患有阿尔茨海默病--#年第六大致死原因 美国和唯一无法预防、治愈或死亡的主要死因 大大减慢了。早期准确的诊断可以节省高达7.9万亿美元的医疗成本 和护理费用，因此是一项迫切的需求。然而，为了健康去医院 评估对每个人来说都可能是繁重的，特别是对于老年人来说， 机动性、视力和/或认知，以及不同的交通和回家机会 照顾者。基于家庭的评估可能是一种解决方案，但现有的评估工具 对于AD-包括PET、MRI和脑脊液或血浆中的生物标记物- 笨重、昂贵或侵入性的，使它们不适合家庭使用，甚至不适合 定期筛查目的。一种易于使用的家庭筛查测试，具有良好的 因此，识别AD的敏感度和特异度将具有巨大的价值和填充 未得到满足的临床需求。目前还不存在这样的技术。 在过去的15年里，我们的团队一直在开发电池供电的可穿戴设备 促进应用的神经血管和生理监测技术 范围从无缝围术期监测到太空飞行中的大脑评估。 这些设备有可能成为一家移动诊所，支持基于大脑的 在家中进行评估，并已在许多远程和自我部署中使用 设置。然而，还没有对老年人使用它们的可行性进行评估 成年人，阿尔茨海默病患者。 在这个项目中，我们建议实现以下具体目标。目标1：生成 经过调整的NINcan版本，使老年人或AD患者可以收集高质量的大脑 以及家里的生理数据。目标2：从家庭数据生成和共享数据库 老年人和AD患者在休息和认知任务中的收集 麻省理工学院附属的MADRC核心。目标3：使用基于变压器的人工 智能(AI)模型，用于(I)识别疑似AD患者，(Ii)预测认知能力 测试分数，和(Iii)预测血浆tau水平，全部使用神经和血管 在家里自我(或照顾者)部署录音期间收集的生物标记物。这个 该项目的结果将有助于使两人更容易进行专注于大脑的AD相关测试 患者和临床医生，并可能潜在地建议更具成本效益和 AD的可持续未来医疗保健模式。

Project Summary More than 5 million Americans are living with AD—the sixth leading cause of death in the U.S. and the only leading cause of death that cannot be prevented, cured, or substantially slowed. Early and accurate diagnosis can save up to $7.9 trillion in medical and care costs and is therefore a critical need. However, going to the hospital for health evaluations can be onerous for everyone, particularly so for older adults with limited mobility, vision and/or cognition, as well as variable access to transportation and home caregivers. Home-based assessment could be a solution, but existing assessment tools for AD—including PET, MRI, and biomarkers in cerebrospinal fluid or plasma—are cumbersome, expensive, or invasive, making them ill-suited for home use or even regular screening purposes. An easy-to-use, home-based screening test with good sensitivity and specificity for identifying AD would thus be of tremendous value and fill an unmet clinical need. No such technology currently exists. Over the past 15 years, our group has been developing battery-powered wearable neurovascular and physiological monitoring technologies to facilitate applications ranging from seamless perioperative monitoring to brain assessment in spaceflight. These devices have the potential to act as a ‘mobile clinic’ to support brain-based assessments at home, and have been utilized in numerous remote and self-deployed settings. They have not, however, been assessed for their feasibility for use by older adults, AD patients. In this project, we propose to achieve the following specific aims. Aim 1: Generate an adapted version of NINscan so older adults or AD patients can collect high quality brain and physiological data at home. Aim 2: Generate and share a database from home data collections during rest and cognitive tasks in older adults and AD patients recruited from the MassAITC-affiliated MADRC Core. Aim 3: Use transformer- based artificial intelligence (AI) models to (i) identify individuals with suspected AD, (ii) predict cognitive testing scores, and (iii) predict plasma tau levels, all using the neural and vascular biomarkers collected during self- (or caregiver-) deployed recordings at home. The results of this project will help make brain-focused AD-relevant testing easier for both patients and clinicians, and could potentially suggest a more cost-effective and sustainable future health care model for AD.