Generalized predictive analysis of reactions in paper devices via graph neural networks.

Microfluidic technology facilitates high-throughput generation of time series data for biological and medical studies. Deep learning enables accurate, predictive analysis and proactive decision-making based on autonomous recognition of intricate pattern hidden in series. In this work, we first devised a paper-based microfluidic system for portable nucleic acid amplification test with economic energy consumption. Then, we employed Graph Neural Network (GNN), distinguished by its non-Euclidean data structure tailored for deep learning, with spatio-temporal attention mechanism to perform near-sensor predictive analysis of the on-chip reaction. Our findings demonstrated that the novel GNN model can provide accurate predictions of positive outcomes at the early stages of the reaction using less than one-third of the total reaction time. Then, the deep learning model trained by on-chip data was subsequently applied to more than 900 clinical plots. Generalization of the GNN model was successfully validated across different detection methods, diverse types of datasets and time series with variable length. Accuracy, sensitivity and specificity of the predictive approach were 96.5 %, 94.3 % and 99.0 % by utilizing the early half of reaction information. Finally, we compared the GNN model with various deep learning models. Despite differences in the prediction of negative samples among various models were minute, GNN obviously offered overall superior performance. This work ignites a cutting-edge application of deep learning in point-of-care and near-sensor tests. By harnessing the power of body area networks and edge/fog computing, our approach unlocks promising possibilities in diverse fields like healthcare and instrument science. [Display omitted] • Paper devices for economic on-chip amplification. • GNN analyzes data generated in paper devices and predicts reaction results quickly. • The deep learning model can work across platforms, methods & datasets. [ABSTRACT FROM AUTHOR]