Authors
Allan Sun, Arian Nasser, Chaohao Chen, Yunduo Charles Zhao, Haimei Zhao, Zihao Wang, Wenlong Cheng, Pierre Qian & Lining Arnold Ju
Abstract
Addressing the demand for rapid and affordable coagulation testing in cardiovascular care, this study introduces a novel application of repurposed COVID-19 rapid antigen tests (RATs) as paper-based lateral flow assays (LFAs) combined with machine learning for coagulation status evaluation. Using a random forest classifier, the platform evaluates red blood cell (RBC) wicked diffusion distance in recalcified citrated whole blood to inform real-time anticoagulant dosing adjustments. This approach leverages post-pandemic resources and demonstrates a cost-effective, rapid, and smart strategy to optimize clinical decision-making in coagulation management.
Highlights
• Repurposed COVID-19 RATs provide an ideal platform for observing differences in blood coagulability.
• Random Forest image classification algorithms can facilitate rapid coagulation status assessment on a paper-based LFA platform.
