Stalled capillary flow: a novel mechanism for hypoperfusion in Alzheimer disease

项目摘要/摘要 我们试图研究基于代理的机器学习(ML)模型在现有的 众包系统，可以在不损失数据质量的情况下大幅加快生物医学图像分析速度 对于我们R01研究中的目标2-4。我们在之前的R01支持的工作中遇到了分析瓶颈， 旨在揭示大脑中毛细血管停滞的基础机制，并需要量化停滞率 2PEF(双光子激发荧光)图像堆栈。为了解决这个问题，我们与人类计算合作 该研究所(HCI)使用在线公民科学平台Stall Catcher众包这项分析，该平台 将分析典型数据集的时间从几个月减少到几周。除了支持以下几个 公布结果，3.5万名赶摊志愿者造就了140多万名高质量的“群众” 注释，在最近的一次机器学习竞赛中充当了丰富的训练集，导致了 在50个不同的ML模型中，显示出广泛的敏感度和偏差分布。这些车型本身都不是， 满足我们严格的分析要求。然而，如果我们能够赋予这些模型足够的代理来 作为真正的捕手球员参与进来，那么我们就可以检验混血(人/机器)的假设。 当使用我们的 现有的“群众智慧”算法。开发一个开源工具包，将ML模型转换为 公民科学“机器人”将提供一条直接途径，将不符合标准的ML模型有效地集成到 现有的大众支持的分析管道，而不需要密集的重新设计。加速生物医学 以这种方式进行数据分析可以让其他生物医学研究人员从较小的培训中立即获得价值 用更少的时间和资源设置和研究更多的假设。该项目可以实现低管理费用 使用不完美的ML模型实现半自动化的途径，这可能会更快地利用ML，同时减少 对人类认知资源的依赖，并提供了一条实现全自动分析的途径，如 改进的ML模型作为CitSci机器人加入了人群。这一追求的成功将使我们能够将 全职CitSci机器人进入失速捕手，这可能会使我们可以进行的毛细血管失速研究的数量翻一番 在某一年，为阐明更完整的毛细管失速机理模型所做的努力。这将会 加快我们确定有针对性的干预的能力，减少副作用，缓解认知障碍 被牵连的痴呆症的损害，如阿尔茨海默病，同时有助于促进 混合智能方法在生物医学数据分析中具有广泛的实用价值。

Project Summary / Abstract We seek to investigate the agent-based participation of machine learning (ML) models in an existing crowdsourcing system, which could substantially speed up biomedical image analysis without loss of data quality for Aims 2-4 in our R01 research. We encountered an analytic bottleneck in our prior R01-supported work, which seeks to reveal mechanisms that underlie capillary stalling in the brain and requires quantifying stall rates from 2PEF (2-photon excited fluorescence) image stacks. To address this, we partnered with the Human Computation Institute (HCI) to crowdsource the analysis using the online citizen science platform Stall Catchers, which has reduced the time to analyze a typical dataset from many months to just a few weeks. Beyond enabling several published results, 35,000 Stall Catchers volunteers have produced over 1.4 million high-quality “crowd” annotations, which served as a rich training set in a recent machine learning competition that led to the creation of fifty distinct ML models exhibiting a broad distribution of sensitivity and bias. None of these models, by itself, meets our stringent analytic requirements. However, if we could endow these models with sufficient agency to participate as bonafide Stall Catchers players, then we could test the hypothesis that hybrid (human/machine) ensembles will achieve the same data quality as human-only ensembles when answers are combined using our existing “wisdom of the crowd” algorithm. Developing an open source toolkit for transforming ML models into citizen science “bots” would enable a direct pathway for effectively integrating even substandard ML models into an existing crowd-powered analytic pipeline without requiring intensive re-engineering. Accelerating biomedical data analysis in this way could allow other biomedical researchers to derive immediate value from smaller training sets and investigate more hypotheses using less time and resources. This project could enable a low-overhead pathway for semi-automation using imperfect ML models, which could leverage ML sooner while reducing reliance on human cognitive resources, and provide a pathway for achieving fully automated analyses as improved ML models are added to the crowd as CitSci bots. Success in this pursuit would allow us to incorporate full-time CitSci bots into Stall Catchers, which could double the number of capillary stalling studies we can conduct in a given year toward elucidating a more complete mechanistic model of capillary stalling. This would speed up our ability to identify a targeted intervention with reduced side effects that could alleviate cognitive impairments in implicated dementias, such as Alzheimer’s disease while contributing to the advancement of hybrid intelligence methods with broad utility for biomedical data analysis.