LifeBio-ALZ: AI driven digital biomarker engine leveraging natural conversation to widely scale accessibility for early detection and assessment of Alzheimers disease progression

阿尔茨海默病（AD）是老年人群中最常见的痴呆形式之一， 影响了全世界超过三千万人随着美国老年人口的不断增加，AD发病率 也会上升，因为没有神经保护疗法或治疗方法。常见症状包括记忆力减退，认知能力下降， 损伤定向障碍和精神问题传统上，诊断是通过以下几个方面的结合来实现的： 临床标准，如神经系统检查，精神状态测试和脑成像。然而，这些战略 对于早期AD或具有轻度症状的患者的检测具有挑战性，特别是在轻度认知障碍期间。 MCI期。精神状态测试和主观日志，由患者或护理人员保存，可以跟踪AD 进展，但敏感性和可靠性低。最强有力的AD生物标志物，包括 淀粉样蛋白β、tau蛋白和磷酸化tau都是通过需要侵入性腰椎穿刺的CSF获得的。 LifeBio-ALZ技术将提供一种方便、可访问且全面的数字生物标志物 和分析套件来检测和评估阿尔茨海默氏症的进展。该平台将整合一套评估 所有领域都通过一个以患者为中心的应用程序无缝捕获，让用户参与自然的视频聊天 通过智能数字助理进行对话。在简短但定期的会议中，一个人回答问题 遵循智能序列来评估意识，参与，认知，反应时间，言语模式， 情绪状态该平台将在对话过程中录制音频/视频。评估的类型和时间， 以及具体问题将根据AD阶段、个人人口统计数据和 以前的分析，以尽量减少用户负担，同时仍然提供丰富的数据的算法。数量特征 将从数字语音和眼球运动中提取跨多个领域的信息，然后用作输入， 一个检测和评估阿尔茨海默病进展的AI引擎。数据将在安全的云中聚合 临床医生可以访问仪表板可视化工具。 第一阶段将展示核心可行性。开发将建立在现有技术的强大技术基础上， LifeBio平台，以增加成功的可能性。目前，LifeBio以多种格式部署，包括Web， 手机和移动的应用程序来记录人们达到老年或面临危及生命的疾病的生活史 或者失忆自然语言处理工具将信息解析为家庭共享或使用的生活故事 通过工作人员个性化参与护理设施。虽然现有的技术提供了一个基础， 增强功能将在第一阶段执行。更具体地说，第一阶段的任务将首先更新平台架构 整合新的数据领域，建立智能有序的多维问题，并提高患者的 工作流接口。一旦增强的应用程序通过所有技术验证测试，它将被部署在一个领域， 广泛的AD患者人口统计学和分期的数据收集和可用性研究。最后，收集的数据 将用于构建和验证用于检测和评估阿尔茨海默病进展的AI引擎。

Alzheimer’s Disease (AD) is one of the most common forms of dementia to occur in elderly populations, affecting over 30 million individuals worldwide. As the U.S. elderly population continues to increase, AD incidence rises as well, as there is no neuroprotective therapy or cure. Common symptoms include memory loss, cognitive impairment, disorientation, and psychiatric issues. Traditionally, diagnosis is achieved through a combination of clinical criteria such as neurological examination, mental status tests & brain imaging. However, these strategies are challenging for detection of early AD or patients with mild symptoms, specifically during the mild cognitive impairment (MCI) stage. Mental status tests & subjective journals, kept by patients or caregivers, can track AD progression, but have low sensitivity and reliability. The most strongly established biomarkers for AD, including amyloid beta, tau protein, & phosphorylated tau, are all obtained thru CSF requiring invasive lumbar puncture. The LifeBio-ALZ technology will provide a convenient and accessible, yet comprehensive digital biomarker and analytics suite to detect & assess Alzheimer’s progression. The platform will integrate a suite of assessment domains all seamlessly captured through a single, patient-centric app that engages users in natural video chat conversation via smart digital assistant. During brief, but regular sessions, an individual answers questions following a smart sequence to evaluate awareness, engagement, cognition, reaction time, speech patterns, & emotional state. The platform will record audio/video during the conversation. Type and timing of assessments, as well as specific questions will be adaptively modulated based on AD stage, personal demographics and previous analytics to minimize user burden while still providing rich data for algorithms. Quantitative features across multiple domains will be extracted from digital speech and eye movements, and then used as inputs to an AI engine to detect and assess Alzheimer’s’ disease progression. Data will be aggregated in secure cloud storage with clinician access to dashboard visualization tools. Phase I will demonstrate core feasibility. Development will build on a strong tech foundation of an existing LifeBio platform to increase likelihood of success. Currently, LifeBio is deployed in several formats including web, phone, & mobile apps to record life histories of people reaching advanced age or facing life-threatening illnesses or memory loss. Natural language processing tools parse information into life stories shared by family or used by staff to personalize engagement in care facilities. While the existing tech provides a base, significant enhancements will be executed in Phase I. More specifically, Phase I tasks will first update platform architecture to integrate novel data domains, build on smart sequenced multidimensional questions, and enhance patient workflow interfaces. Once the enhanced app passes all technical verification testing, it will be deployed in a field data collection and usability study with wide ranging AD patient demographics and stages. Finally, collected data will be used to build and validate an AI engine for detection and assessment of Alzheimer’s progression.