ArticleScientific reports2022
Accurate and fast identification of minimally prepared bacteria phenotypes using Raman spectroscopy assisted by machine learning.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 41 citations in OpenAlex.
- Portable Raman spectroscopy combined with machine learning for rapid and label-free recognition of five pathogenic bacteria.Frontiers in microbiology · 2026Article
- Hyper-spectral imaging with up-converted mid-infrared single-photons.Scientific reports · 2025Article
- Nano-Infrared Detection and Identification of Bacteria at the Single-Cell Level.Analytical chemistry · 2025Article
- Leveraging the microbiome to combat antibiotic resistant gynecological infections.npj antimicrobials and resistance · 2025Review
- Non-Invasive Real-Time Monitoring of Bacterial Activity by Non-Contact Impedance Spectroscopy for Off-the-Shelf Labware.Sensors (Basel, Switzerland) · 2025Article
- Ribosome phenotypes for rapid classification of antibiotic-susceptible and resistant strains of Escherichia coli.Communications biology · 2025Article
- Application of Raman spectroscopy and machine learning forApplied and environmental microbiology · 2024Article
- Rapid, antibiotic incubation-free determination of tuberculosis drug resistance using machine learning and Raman spectroscopy.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Article
- Optics miniaturization strategy for demanding Raman spectroscopy applications.Nature communications · 2024Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The worldwide increase of antimicrobial resistance (AMR) is a serious threat to human health. To avert the spread of AMR, fast reliable diagnostics tools that facilitate optimal antibiotic stewardship are an unmet need. In this regard, Raman spectroscopy promises rapid label- and culture-free identification and antimicrobial susceptibility testing (AST) in a single step. However, even though many Raman-based bacteria-identification and AST studies have demonstrated impressive results, some shortcomings must be addressed. To bridge the gap between proof-of-concept studies and clinical application, we have developed machine learning techniques in combination with a novel data-augmentation algorithm, for fast identification of minimally prepared bacteria phenotypes and the distinctions of methicillin-resistant (MR) from methicillin-susceptible (MS) bacteria. For this we have implemented a spectral transformer model for hyper-spectral Raman images of bacteria. We show that our model outperforms the standard convolutional neural network models on a multitude of classification problems, both in terms of accuracy and in terms of training time. We attain more than 96% classification accuracy on a dataset consisting of 15 different classes and 95.6% classification accuracy for six MR-MS bacteria species. More importantly, our results are obtained using only fast and easy-to-produce training and test data.
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