A machine learning-assisted Ag–TiO 2 SERS platform for intraoperative osteomyelitis diagnosis

substrate provides ultrasensitive detection with high spectral reproducibility, enabling reliable identification of subtle biochemical changes in complex clinical samples. Quantitative analysis indicates that strengthened charge-transfer processes significantly enhance molecular Raman responses, while plasmonic electromagnetic effects remain essential for overall signal amplification. Distinct Raman fingerprints of infected, non-infected, and recovered patients were successfully classified using artificial intelligence (AI) models, revealing metabolic alterations in amino acids, purines, proteins, and lipids associated with bacterial infections and immune response. This integration of SERS with AI not only elucidates the mechanistic basis of spectral enhancement but also demonstrates clinical feasibility for intraoperative osteomyelitis diagnosis, offering a promising strategy to improve surgical precision and reduce recurrence risk.