An Interpretable SERS-AI Platform for Rapid and Quantitative Diagnosis of Polymicrobial UTIs: Powered by Positively Charged Plasmonic Nanoparticles and Attention-Based Deep Learning.

ABSTRACT: Polymicrobial urinary tract infections (UTIs) present diagnostic challenges due to overlapping symptoms and limitations of conventional methods. Although SERS and AI have shown potential for microbial diagnostics, existing approaches lack reproducibility, quantification capability, and interpretability-especially in complex clinical samples. Here, a label-free, interpretable SERS-AI platform for rapid identification and quantification of mixed urinary tract pathogens is proposed. A plasmonic substrate is engineered by combining Au@Ag core-shell nanoparticles with a positively charged bPEI surface, enabling electrostatic bacterial capture and stable SERS signal generation across diverse microbial mixtures. A convolutional neural network (CNN) enhanced with a convolutional block attention mo

SUBMITTER: Shen Z 

PROVIDER: S-EPMC12697763 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature