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

项目总结/摘要 我们试图研究机器学习（ML）模型在现有的代理参与 众包系统，可以大大加快生物医学图像分析，而不会损失数据质量 在我们的R 01研究中，目标2-4。我们在之前R 01支持的工作中遇到了分析瓶颈， 试图揭示大脑中毛细血管停滞的机制，并需要量化停滞率， 2 PEF（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， 研究所（HCI）使用在线公民科学平台Stall Catchers进行众包分析，该平台 将分析典型数据集的时间从数月缩短到几周。除了使几个 公布的结果，35，000名Stall Catchers志愿者已经产生了超过140万高质量的“人群” 注释，在最近的一次机器学习竞赛中作为丰富的训练集， 50个不同的ML模型，表现出广泛的灵敏度和偏差分布。这些模型本身 符合我们严格的分析要求。然而，如果我们能赋予这些模型足够的动力， 作为真正的失速捕手球员参与，那么我们就可以测试混合（人类/机器） 当答案使用我们的 现有的“群众智慧”算法。开发一个开源工具包，用于将ML模型转换为 公民科学“机器人”将为有效地将甚至不合格的ML模型集成到 现有的群体动力分析管道，而无需密集的重新设计。加速生物医学 以这种方式进行的数据分析可以使其他生物医学研究人员从较小的培训中获得直接价值 用更少的时间和资源设置和调查更多的假设。该项目可以实现低开销 使用不完美的ML模型的半自动化途径，可以更快地利用ML，同时减少 依赖人类认知资源，并提供实现全自动分析的途径， 改进的ML模型作为CitSci机器人加入到人群中。在这一追求中的成功将使我们能够将 全职CitSci机器人进入Stall Catchers，这可能会使我们可以进行的毛细血管停滞研究数量增加一倍。 在给定的一年内进行，以阐明一个更完整的毛细血管失速的机理模型。这将 加快我们确定有针对性的干预措施的能力，减少副作用，减轻认知障碍， 在牵连痴呆症，如阿尔茨海默氏病的损害，同时有助于发展， 混合智能方法，具有广泛的实用性，用于生物医学数据分析。

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.