Artificial Intelligence-Based Imaging Transcoding System for Multiplex Screening of Viable Foodborne Pathogens

Multiplex detection of viable foodborne pathogens is critical for food safety and public health, yet current assays suffer trade-offs between cost, assay complexity, sensitivities, and the specificity between live and dead bacteria. We herein developed a sensing method using artificial intelligence transcoding (SMART) for rapid, sensitive, and multiplex profiling of foodborne pathogens. The assay utilizes the programmable polystyrene (PS) microspheres to encode different pathogens, inducing subsequent visible signals under conventional microscopy that can be analyzed using a customized, artificial intelligence-computer vision, which was trained to decode the intrinsic properties of PS microspheres to reveal the numbers and types of pathogens. Our approach enabled the rapid and simultaneous detection of multiple bacteria from egg samples of <102 CFU/mL without DNA amplification and showed strong consistency with the standard microbiologic and genotypic methods. We adopted our assay through phage-guided targeting to enable the discrimination between live and dead bacteria.