Development and validation of novel tests in the DCT drawing analysis platform for the detection of Alzheimer’s Disease-related early cognitive

在 DCT 绘图分析平台中开发和验证新测试,用于检测与阿尔茨海默病相关的早期认知

基本信息

  • 批准号:
    10221367
  • 负责人:
  • 金额:
    $ 23.78万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-05-02 至 2022-02-28
  • 项目状态:
    已结题

项目摘要

Project Summary/Abstract Early detection of AD and AD-related dementias is critical for the development of novel therapeutic agents and for effective patient treatment and care. Linus Health has acquired a platform able to detect subtle changes in behavior indicative of early cognitive impairment by using a digital pen to record drawing motions and automatically analyzing that data using a combination of artificial intelligence and neuropsychological analysis. DCTclockTM, the first test on this platform, has been successfully launched and is able to detect subtle cognitive change in early Alzheimer’s Disease (AD) and AD-related dementias earlier than standard cognitive tests, correlate with known AD biomarkers such as amyloid and tau in preclinical-AD subjects, and detect known APOE genetic risk in cognitively healthy participants. The test is FDA-cleared to market for general cognitive assessment and is currently used in multinational clinical trials, research initiatives, and by practicing clinicians for patient care. Linus Health is now conducting a study involving ~200 participants ranging from cognitively healthy to demented to validate a tablet as input device for the drawing data, and to collect data on a set of novel tablet-based drawing tasks including: a symbol task, a visual retention task, a trail making task, a path-finding task, and a visuoconstructional task. We aim to analyze task data collected in the study to produce a set of easily-deployable, rapid, cost-effective, and sensitive tests to be used as cognitive digital biomarkers in clinical trials for AD and AD-related dementias. The first aim is to develop novel cognitive measures from these additional tests and obtain preliminary validation. Existing data will be split into training and testing sets. The training set will be analyzed to develop metrics, following an interpretable hierarchical analysis structure with low-level drawing measurements combined in a set of composite scales tied to a cognitive concept (i.e., information processing), and then further combined to obtain a simple 0-100 score for performance on the test. The testing set will be used to conduct preliminary validation consisting of a Receiver Operating Characteristic curve analysis, correlation measures to existing neuropsychological tests, and test-retest reliability measurements to ensure repeat testing stability. The second aim is to implement the developed metrics in the commercial Linus platform. Robust software will be created for the measurement, storage, and display of the novel metrics, setting a foundation to enable usage of the metrics by research and commercial partners.
项目概要/摘要 AD 和 AD 相关痴呆的早期检测对于开发新型治疗药物和治疗至关重要。 进行有效的患者治疗和护理。 Linus Health 获得了一个能够检测细微变化的平台 使用数字笔记录绘画动作表明早期认知障碍的行为 结合人工智能和神经心理学分析来自动分析这些数据。 DCTclockTM是该平台上的首次测试,已成功启动,能够检测到细微的变化。 早期阿尔茨海默病 (AD) 和 AD 相关痴呆的认知变化早于标准认知 测试,与临床前 AD 受试者中已知的 AD 生物标志物(如淀粉样蛋白和 tau 蛋白)相关联,并检测 认知健康参与者已知的 APOE 遗传风险。该测试经 FDA 批准可用于一般市场 认知评估目前用于跨国临床试验、研究计划以及实践 临床医生对患者进行护理。 Linus Health 目前正在进行一项研究,涉及约 200 名参与者,范围从 认知健康到精神错乱,验证平板电脑作为绘图数据的输入设备,并收集数据 一组新颖的基于平板电脑的绘图任务,包括:符号任务、视觉保留任务、轨迹制作任务、 寻路任务和视觉构造任务。 我们的目标是分析研究中收集的任务数据,以产生一组易于部署、快速、具有成本效益的、 以及在 AD 和 AD 相关痴呆症临床试验中用作认知数字生物标志物的敏感测试。 第一个目标是从这些额外的测试中开发新的认知测量并获得初步的结果 验证。现有数据将分为训练集和测试集。将分析训练集以开发 指标,遵循具有低级绘图测量的可解释的层次分析结构 组合成一组与认知概念(即信息处理)相关的复合量表,然后 进一步组合以获得测试表现的简单 0-100 分。测试集将用于 进行初步验证,包括接收者操作特征曲线分析、相关性 对现有神经心理学测试的测量,以及重测可靠性测量,以确保重复 测试稳定性。第二个目标是在商业 Linus 平台上实施开发的指标。 将创建强大的软件来测量、存储和显示新指标,设置一个 为研究和商业合作伙伴使用这些指标奠定了基础。

项目成果

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Sean Michael Tobyne其他文献

Sean Michael Tobyne的其他文献

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{{ truncateString('Sean Michael Tobyne', 18)}}的其他基金

Predicting Whole Brain Multi-Sensory Cognitive Control Networks: Relationship with Neuropsychological Test Performance and Repetitive Head Impact
预测全脑多感官认知控制网络:与神经心理学测试表现和重复头部冲击的关系
  • 批准号:
    9395139
  • 财政年份:
    2017
  • 资助金额:
    $ 23.78万
  • 项目类别:

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