Deep-Learning-Assisted Microfluidic Immunoassay via Smartphone-Based Imaging Transcoding System for On-Site and Multiplexed Biosensing

Point-of-care testing (POCT) with multiplexed capability, ultrahigh sensitivity, affordable smart devices, and user-friendly operation is critically needed for clinical diagnostics and food safety. This study presents a deep-learning-assisted microfluidic immunoassay platform that uses a smartphone-based imaging transcoding system, polystyrene microsphere-based encoding, and artificial-intelligence-assisted decoding. Microspheres of varying sizes act as multiprobes, with their quantities correlating to target concentrations after an immunoreaction and separation–filtration within the microfluidic chip. A smartphone with intelligent decoding software captures images of multiprobes from the chip and performs classification, counting, and concentration calculations. The “encoding–decoding” strategy and integrated microfluidic chip design allow these processes to be completed in simple steps, eliminating the need for additional immunomagnetic separation. As a proof of concept, this platform successfully detected multiple respiratory viruses and antibiotics in various real samples with high sensitivity within 30 min, demonstrating great potential as a smart, universal toolkit for next-generation POCT applications.