Machine Learning-Assisted Ag-TiO₂ SERS Platform for Intraoperative Osteomyelitis Diagnosis

Abstract

Osteomyelitis is a progressive bone infection with high disability and recurrence rates. However, current diagnostic approaches, including bacterial culture, imaging, and serological assays, suffer from poor sensitivity, low specificity, and delayed turnaround, making intraoperative evaluation particularly challenging. Here, we present an Ag-TiO₂-based surface-enhanced Raman scattering (SERS) platform for rapid, label-free detection of osteomyelitis using minimal volumes of wound saline irrigation fluid (WSIF). Benefiting from the synergistic interplay between electromagnetic field amplification and enhanced interfacial charge-transfer interactions, the Ag-TiO₂ 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 infection 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.