Rapid antimicrobial susceptibility testing for mixed bacterial infection in urine by AI-stimulated Raman scattering metabolic imaging

Urinary tract infection with mixed microorganisms may lead to false-positive resistance detection. Current antimicrobial susceptibility testing (AST) performed in clinical laboratories is based on bacterial culture and takes a long time for mixed bacterial infections. Here, we propose a machine learning-based single-cell metabolism inactivation concentration (ML-MIC) model to achieve rapid AST for mixed bacterial infections. Using E. coli and S. aureus as a demonstration of mixed bacteria, we performed feature extraction and multi-feature analysis on stimulated Raman scattering (SRS) images of bacteria with the ML-MIC model to determine the subtypes and AST of the mixed bacteria. Furthermore, we assessed the AST of mixed bacteria in urine and obtained single-cell metabolism inactivation concentration in only 3 ​h. Collectively, we demonstrated that SRS imaging of bacterial metabolism can be extended to mixed bacterial infection cases for rapid AST.