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

项目摘要 超过500万美国人患有AD，这是美国第六大死亡原因。 美国和唯一的主要死亡原因，不能预防，治愈，或 大幅放缓。早期和准确的诊断可以节省高达7.9万亿美元的医疗费用 和护理成本，因此是一个关键的需求。然而，去医院为了健康 评估对每个人来说都是繁重的，特别是对老年人来说， 移动性、视觉和/或认知能力，以及交通和家庭的可变访问 照顾者以家庭为基础的评估可能是一种解决办法，但现有的评估工具 包括PET、MRI和脑脊液或血浆中的生物标志物， 笨重、昂贵或侵入性，使其不适合家庭使用，甚至 定期检查的目的。一个易于使用的，以家庭为基础的筛选测试，具有良好的 因此，用于识别AD的灵敏度和特异性将具有巨大的价值和填补 未满足的临床需求。目前还没有这样的技术。 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， 神经血管和生理监测技术，以促进应用 从无缝的围手术期监测到航天飞行中的大脑评估。 这些设备有可能充当“移动的诊所”，以支持基于大脑的 在国内进行评估，并已在许多远程和自我部署的 设置.然而，尚未评估老年人使用这些设备的可行性。 成人AD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 NINscan的改编版本，使老年人或AD患者可以收集高质量的大脑 和生理数据目标2：从家庭数据生成和共享数据库 收集在休息和认知任务中的老年人和AD患者， 马萨诸塞州AITC附属的MADRC核心。目标3：使用基于Transformer的人工 智能（AI）模型，以（i）识别疑似AD的个体，（ii）预测认知功能 测试分数，和（iii）预测血浆tau水平，所有这些都使用神经和血管 在家中自我（或护理者）部署的记录期间收集的生物标志物。的 该项目的结果将有助于使以大脑为中心的广告相关测试更容易， 患者和临床医生，并可能提出一个更具成本效益， 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.