5.8 million Americans live with Alzheimer’s dementia, including 10% of all seniors 65 and older. Scientists at Cornell have discovered links between “stalls,” or clogged blood vessels in the brain, and Alzheimer’s. Stalls can reduce overall blood flow in the brain by 30%. The ability to prevent or remove stalls may transform how Alzheimer’s disease is treated.
Illustration by NIH ADEAR/Wikipedia/Public Domain
Stall Catchers is a citizen science project that crowdsources the analysis of Alzheimer’s disease research data provided by Cornell University’s Department of Biomedical Engineering. It resolves a pressing analytic bottleneck: for each hour of data collection, it would take an entire week to analyze the results in the lab, which means an entire experimental dataset would take 6-12 months to analyze.
Some portion of the data, the “low-hanging fruit,” maybe within reach of machine learning models that are able to distinguish between easy and difficult data and are applied only in cases where they have been validated to meet the researchers’ data quality requirements. If a machine learning classifier could be used for 50% of the data, it would double the analytic throughput of Stall Catchers and could achieve the original goal of analyzing the data 10x faster than the lab. This could ultimately put finding an Alzheimer’s treatment target in reach within the next year or two.
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Source: DrivenData