Rapid culture-free diagnosis of clinical pathogens via integrated microfluidic-Raman micro-spectroscopy

This study presents a rapid, culture-free diagnostic platform to support timely treatment of antimicrobial resistance. By combining microfluidic pathogen enrichment, Raman micro-spectroscopy, and deep-learning analysis, the system can identify infections directly from clinical samples and deliver results within 20 minutes, even at very low pathogen levels. Trained on a large database of bacterial and fungal single-cell fingerprints, the model achieved around 95% accuracy in laboratory tests and showed strong agreement with conventional culture methods in a 305-patient clinical study. Although further validation is required, the approach demonstrates the potential of integrated, AI-enabled diagnostics to significantly speed up and improve infection detection in the fight against AMR.