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.