Deep Learning‐Enabled Multiplexed Point‐of‐Care Sensor using a Paper‐Based Fluorescence Vertical Flow Assay

Abstract

AbstractMultiplexed computational sensing with a point‐of‐care serodiagnosis assay to simultaneously quantify three biomarkers of acute cardiac injury is demonstrated. This point‐of‐care sensor includes a paper‐based fluorescence vertical flow assay (fxVFA) processed by a low‐cost mobile reader, which quantifies the target biomarkers through trained neural networks, all within <15 min of test time using 50 µL of serum sample per patient. This fxVFA platform is validated using human serum samples to quantify three cardiac biomarkers, i.e., myoglobin, creatine kinase‐MB, and heart‐type fatty acid binding protein, achieving less than 0.52 ng mL−1 limit‐of‐detection for all three biomarkers with minimal cross‐reactivity. Biomarker concentration quantification using the fxVFA that is coupled to neural network‐based inference is blindly tested using 46 individually activated cartridges, which shows a high correlation with the ground truth concentrations for all three biomarkers achieving >0.9 linearity and <15% coefficient of variation. The competitive performance of this multiplexed computational fxVFA along with its inexpensive paper‐based design and handheld footprint makes it a promising point‐of‐care sensor platform that can expand access to diagnostics in resource‐limited settings.