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We demonstrate a multiplexed fluorescent vertical flow assay (fxVFA) processed by a hand-held reader and a deep learning-based algorithm for quantification of three biomarkers, i.e. myoglobin, creatine kinase-MB (CK-MB) and heart-type fatty acid binding protein (FABP) from human serum samples. fxVFA operation takes <15 min and requires 50 µL of serum sample. fxVFA achieved <0.52 ng/mL limits-of-detection for all three analytes with minimal cross-reactivity between the antigens. Furthermore, quantification performance of fxVFA was tested on 16 serum samples and fxVFA-predicted concentrations had >0.9 coefficients of determination and <15 % coefficients of variation with the respect to a standard ELISA test.
Conference Presentation
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Artem Goncharov, Hyou-Arm Joung, Rajesh Ghosh, Gyeo-Re Han, Zachary S. Ballard, Quinn Maloney, Alexandra Bell, Chew Tin Zar Aung, Omai B. Garner, Dino Di Carlo, Aydogan Ozcan, "Deep learning-enabled fluorescent point-of-care sensor for multiplexed quantification of biomarkers from serum," Proc. SPIE 12655, Emerging Topics in Artificial Intelligence (ETAI) 2023, 126550D (6 October 2023); https://doi.org/10.1117/12.2678193