The only existing approach for assessing the risk of developing acute ischemic stroke (AIS) necessitates that individuals possess a strong understanding of their health status. Our research gathered compelling evidence in favor of our hypothesis, suggesting that the likelihood of developing AIS can be assessed by analyzing the green autofluorescence (AF) of the skin and fingernails. Utilizing machine learning-based analyses of AF images, we found that the area under the curve (AUC) for distinguishing subjects with three risk factors from those with zero, one, or two risk factors was 0.79, 0.76, and 0.75, respectively. Our research has revealed that green AF serves as an innovative biomarker for assessing the risk of developing AIS. Our method is objective, non-invasive, efficient, and economic, which shows great promise to boost a technology for screening natural populations for risk of developing AIS.
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| http://dx.doi.org/10.1002/jbio.202300473 | DOI Listing |
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