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Accurate and fast identification of minimally prepared bacteria phenotypes using Raman spectroscopy assisted by
Benjamin Lundquist Thomsen1, Jesper B Christensen1, Olga Rodenko1
1Danish Fundamental Metrology, Kogle Allé 5, 2970, Hørsholm, Denmark.
Scientific Reports
|September 30, 2022
Summary
Antimicrobial resistance (AMR) demands rapid diagnostics. This study uses machine learning and Raman spectroscopy for fast bacteria identification and antibiotic susceptibility testing, achieving over 96% accuracy.
Area of Science:
- Microbiology
- Spectroscopy
- Machine Learning
Background:
- Antimicrobial resistance (AMR) is a growing global health threat requiring rapid diagnostic tools.
- Current diagnostics for AMR and antibiotic stewardship are often slow and culture-dependent.
- Raman spectroscopy offers a potential solution for rapid, label-free bacterial identification and antimicrobial susceptibility testing (AST).
Purpose of the Study:
- To develop advanced machine learning techniques combined with data augmentation for rapid bacterial identification and AST.
- To distinguish between methicillin-resistant (MR) and methicillin-susceptible (MS) bacteria phenotypes.
- To bridge the gap between laboratory proof-of-concept and clinical application of Raman spectroscopy for diagnostics.
Main Methods:
- Implementation of a spectral transformer model for analyzing hyper-spectral Raman images of bacteria.
- Development of a novel data-augmentation algorithm to enhance spectral data.
- Utilizing machine learning for fast identification of minimally prepared bacteria phenotypes and MR-MS distinctions.
Main Results:
- The spectral transformer model demonstrated superior performance compared to standard convolutional neural networks in accuracy and training time.
- Achieved over 96% classification accuracy for a dataset of 15 different bacteria classes.
- Attained 95.6% classification accuracy in distinguishing between six MR-MS bacteria species.
- Results were obtained using rapidly generated training and test datasets.
Conclusions:
- Machine learning, particularly spectral transformer models, significantly enhances Raman spectroscopy for rapid bacterial diagnostics.
- The developed methods show high accuracy in identifying bacteria and determining antibiotic susceptibility, including MR-MS status.
- This approach offers a promising pathway for clinical application, improving antibiotic stewardship and combating AMR.
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