Molecular Confinement‐Driven Programmable Multicolor Metal‐AIEgen Frameworks Combined With Artificial Intelligence for Enhanced Sensitivity of Lateral Flow Immunoassay

ABSTRACT Fluorescent metal–organic framework (MOF)‐based sensing platforms have emerged as promising tools for bioanalysis. However, their limited optical outputs and insufficient information encoding capacity remain major challenges in complex matrices detection. Here, we develop an artificial intelligence (AI)‐interpretable sensing platform by integrating programmable multicolor aggregation‐induced emission metal frameworks (MAFs) with dual‐hue lateral flow immunoassay (DH‐LFIA) for intelligent detection of Pseudomonas aeruginosa ( P. aeruginosa ). A highly emissive MAF was constructed using an AIE‐active ligand (TCPE) and Hf 4+ nodes. Additionally, donor‐acceptor interactions between TCPE and incorporated rhodamine B (RhB) enabled programmable multicolor emission through the Förster resonance energy transfer (FRET) mechanism. The optimized MAFR‐6 probe was subsequently incorporated into the LFIA, achieving a detection limit of 10 CFU/mL for P. aeruginosa . Moreover, an inner filter effect (IFE)‐driven DH‐LFIA platform was established to convert bacterial concentrations into visible color transitions, providing a highly sensitive with a 100‐fold improvement over conventional gold nanoparticles‐based lateral flow immunoassay (AuNPs‐LFIA). Furthermore, integration with a convolutional neural network (CNN) enabled autonomous interpretation of color‐coded signals and achieved 100% classification accuracy for P. aeruginosa in human blood, overcoming the limitations of subjective visual analysis. Collectively, this study provides a new strategy for advancing pathogen detection toward automation, precision, and intelligence.