A machine learning-assisted Ag-TiO2 SERS platform for intraoperative osteomyelitis diagnosis
Yunfan Chen1, Tengjiao Zhu2,3, Langran Wang2,3
1School of Materials Science and Technology, China University of Geosciences, Beijing 100083, China. lzhao@cugb.edu.cn.
Nanoscale
|March 5, 2026
Summary
This study introduces a novel Ag-TiO2-based surface-enhanced Raman scattering (SERS) platform for rapid osteomyelitis diagnosis. The advanced SERS technique combined with AI accurately identifies bone infections from minimal fluid samples.
Area of Science:
- Biomedical Engineering
- Analytical Chemistry
- Infectious Diseases
Background:
- Osteomyelitis diagnosis faces challenges due to low sensitivity, specificity, and delayed results from current methods.
- Intraoperative evaluation of osteomyelitis is particularly difficult, impacting surgical precision and patient outcomes.
- Existing diagnostic tools like bacterial culture and imaging are insufficient for rapid, accurate assessment.
Purpose of the Study:
- To develop a rapid, label-free diagnostic platform for osteomyelitis using surface-enhanced Raman scattering (SERS).
- To evaluate the clinical feasibility of SERS combined with artificial intelligence (AI) for intraoperative osteomyelitis detection.
- To investigate the underlying mechanisms of signal enhancement in the Ag-TiO2 SERS platform.
Main Methods:
- Development of a silver-titanium dioxide (Ag-TiO2) nanocomposite substrate for SERS analysis.
- Detection of osteomyelitis biomarkers in wound saline irrigation fluid (WSIF) using the SERS platform.
- Application of artificial intelligence (AI) models for classifying Raman spectra from infected, non-infected, and recovered patients.
Main Results:
- The Ag-TiO2 SERS platform demonstrated ultrasensitive and reproducible detection of biochemical changes in clinical samples.
- Synergistic effects of electromagnetic field amplification and charge-transfer interactions significantly enhanced Raman signal intensity.
- AI models successfully classified distinct Raman fingerprints, identifying metabolic alterations related to bacterial infection and immune response.
Conclusions:
- The integrated SERS-AI platform offers a promising strategy for rapid, label-free intraoperative diagnosis of osteomyelitis.
- This approach can improve surgical precision, reduce recurrence rates, and enhance patient management for bone infections.
- The study elucidates the mechanistic basis of SERS enhancement, paving the way for advanced diagnostic tools.
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