During　global　outbreaks　such　as　COVID-19,　regular　nucleic　acid　amplification　tests　(NAATs)　have　posed　unprecedented　burden　on　hospital　resources.　Data　of　traditional　NAATs　are　manually　analyzed　post　assay.　Integration　of　artificial　intelligence　(AI)　with　on-chip　assays　give　rise　to　novel　analytical　platforms　via　data-driven　models.　Here,　we　combined　paper　microfluidics,　portable　optoelectronic　system　with　deep　learning　for　SARS-CoV-2　detection.　The　system　was　quite　streamlined　with　low　power　dissipation.　Pixel　by　pixel　signals　reflecting　amplification　of　synthesized　SARS-CoV-2　templates　(containing　ORF1ab,　N　and　E　genes)　can　be　real-time　processed.　Then,　the　data　were　synchronously　fed　to　the　neural　networks　for　early　prediction　analysis.　Instead　of　the　quantification　cycle　(Cq)　based　analytics,　reaction　dynamics　hidden　at　the　early　stage　of　amplification　curve　were　utilized　by　neural　networks　for　predicting　subsequent　data.　Qualitative　and　quantitative　analysis　of　the　40-cycle　NAATs　can　be　achieved　at　the　end　of　22nd　cycle,　reducing　time　cost　by　45%.　In　particular,　the　attention　mechanism　based　deep　learning　model　trained　by　microfluidics-generated　data　can　be　seamlessly　adapted　to　multiple　clinical　datasets　including　readouts　of　SARS-CoV-2　detection.　Accuracy,　sensitivity　and　specificity　of　the　prediction　can　reach　up　to　98.1%,　97.6%　and　98.6%,　respectively.　The　approach　can　be　compatible　with　the　most　advanced　sensing　technologies　and　AI　algorithms　to　inspire　ample　innovations　in　fields　of　fundamental　research　and　clinical　settings.　©　2023