ArticleMicrobiology spectrum2022
Identification of Bacterial Pathogens at Genus and Species Levels through Combination of Raman Spectrometry and Deep-Learning Algorithms.
Article in Microbiology spectrum, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 60 citations in OpenAlex.
- Recent advances in surface enhanced Raman spectroscopy for bacterial pathogen identifications.Journal of advanced research · 2023Pooled it
- Research progress of AI assisted nano-SERS technology in rapid identification of multi species oral pathogenic biofilms.Mikrochimica acta · 2026Review
- Early Sepsis Diagnosis as a Global Imperative: The Role of Raman Spectroscopy.Journal of clinical medicine · 2026Review
- Identification of geographical origins of Ophiocordyceps sinensis (Berk.) via machine learning-assisted surface-enhanced Raman spectroscopy.Analytical and bioanalytical chemistry · 2026Article
- Domain-Level Classification of Archaea and Bacteria Using AI-Assisted Single-Cell Raman Spectroscopy.ACS omega · 2026Article
- BactoRamanBioNet: A Multimodal Neural Network for Bacterial Species Identification Using Raman Spectroscopy and Biological Knowledge.Sensors (Basel, Switzerland) · 2026Article
- Development of surface enhanced Raman spectra coupled with machine learning analysis for differentiation of closely related species within Enterobacter cloacae complex.BMC microbiology · 2026Article
- SERS for Infectious Disease Diagnostics: An Advanced Platform for Pathogen Detection and Antimicrobial Resistance Analysis.International journal of nanomedicine · 2026Review
- High-Wavenumber Raman Spectroscopy for the Identification and Biochemical Characterization of Microbial Species.Analytical chemistry · 2025Article
- Rapid discrimination and ratio quantification of mixed antibiotics in aqueous solution through integrative analysis of SERS spectra via CNN combined with NN-EN model.Journal of advanced research · 2025Article
- Recent Advances in Surface-Enhanced Raman Scattering for Pathogenic Bacteria Detection: A Review.Sensors (Basel, Switzerland) · 2025Review
- Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids.mSystems · 2025Article
- Pseudo-Siamese network combined with label-free Raman spectroscopy for the quantification of mixed trace amounts of antibiotics in human milk: A feasibility study.Food chemistry: X · 2024Article
- Article
- Rapid discrimination of four Salmonella enterica serovars: A performance comparison between benchtop and handheld Raman spectrometers.Journal of cellular and molecular medicine · 2024Article
- Unveiling the efficacy of a bulk Raman spectra-based model in predicting single cell Raman spectra of microorganisms.Heliyon · 2024Article
- Machine Learning for COVID-19 Determination Using Surface-Enhanced Raman Spectroscopy.Biomedicines · 2024Article
- Rapid discrimination between wild and cultivatedCurrent research in food science · 2024Article
- Rapid identification of lactic acid bacteria at species/subspecies level via ensemble learning of Ramanomes.Frontiers in microbiology · 2024Article
- Rapid identification of bloodstream infection pathogens and drug resistance using Raman spectroscopy enhanced by convolutional neural networks.Frontiers in microbiology · 2024Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid and accurate identification of the causing agents during bacterial infections would greatly improve pathogen transmission, prevention, patient care, and medical treatments in clinical settings. Although many conventional and molecular methods have been proven to be efficient and reliable, some of them suffer technical biases and limitations that require the development and application of novel and advanced techniques. Recently, due to its cost affordability, noninvasiveness, and label-free feature, Raman spectroscopy (RS) is emerging as a potential technique for fast bacterial detection. However, the method is still hampered by many technical issues, such as low signal intensity, poor reproducibility, and standard data set insufficiency, among others. Thus, it should be cautiously claimed that Raman spectroscopy could provide practical applications in real-world settings. In order to evaluate the implementation potentials of Raman spectroscopy in the identification of bacterial pathogens, we investigated 30 bacterial species belonging to 9 different bacterial genera that were isolated from clinical samples via surfaced enhanced Raman spectroscopy (SERS). A total of 17,149 SERS spectra were harvested from a Raman spectrometer and were further analyzed via machine learning approaches, which showed that a convolutional neural network (CNN) deep learning algorithm achieved the highest prediction accuracy for recognizing pathogenic bacteria at both the genus and species levels. In summary, the SERS technique holds a promising potential for fast bacterial pathogen identification in clinical laboratories with the integration of machine learning algorithms, which might be further developed and sharpened for the direct identification and prediction of bacterial pathogens from clinical samples.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.