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Deep Learning-Assisted Surface-Enhanced Raman Scattering for Rapid Bacterial Identification.

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Summary

This study introduces a rapid diagnostic tool combining surface-enhanced Raman scattering (SERS) and deep learning (DL) for bloodstream infection (BSI) detection. The SERS-DL model accurately identifies bacterial Gram type, species, and antibiotic resistance, guiding early treatment.

Keywords:
Vision Transformer (ViT)bacterial identificationbloodstream infection (BSI)deep learningsurface-enhanced Raman scattering (SERS)transfer learning

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Area of Science:

  • Microbiology
  • Spectroscopy
  • Artificial Intelligence

Background:

  • Bloodstream infections (BSI) require rapid diagnosis for effective antibiotic treatment.
  • Conventional methods are slow, delaying critical clinical decisions.
  • New diagnostic approaches are needed for timely bacterial identification and antimicrobial susceptibility testing (AST).

Purpose of the Study:

  • To develop a rapid diagnostic model integrating surface-enhanced Raman scattering (SERS) with deep learning (DL) for bacterial identification in BSI.
  • To utilize Vision Transformer (ViT), a DL model, for analyzing SERS spectra to identify Gram type, species, and antibiotic resistance.
  • To assess the model's accuracy and efficiency using clinical blood samples.

Main Methods:

  • Collected 11,774 SERS spectra from eight common bacterial species in clinical blood samples.
  • Developed a SERS-Deep Learning (SERS-DL) model using Vision Transformer (ViT).
  • Employed transfer learning for antibiotic-resistant strain identification, pre-training on Gram-positive species.

Main Results:

  • The SERS-DL model achieved 99.30% accuracy for Gram type identification and 97.56% for species identification.
  • Demonstrated high accuracy (98.5%) in identifying methicillin-resistant *Staphylococcus aureus* (MRSA) and susceptible strains (MSSA) with a small dataset.
  • The model successfully identified bacterial characteristics directly from clinical blood samples without artificial introduction.

Conclusions:

  • The developed SERS-DL model offers a rapid and accurate method for identifying bacterial Gram type, species, and antibiotic resistance in BSI.
  • This approach has significant potential to guide early and appropriate antibiotic usage, improving patient outcomes.
  • The integration of SERS and ViT presents a promising advancement in clinical microbiological diagnostics.