ArticleScientific reports2021
Drug-resistant Staphylococcus aureus bacteria detection by combining surface-enhanced Raman spectroscopy (SERS) and deep learning techniques.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed.
- An underlying mechanism of bovine mastitis: PGEThe veterinary quarterly · 2026Article
- Species-level identification of Nocardia spp. from clinical samples via intelligent analysis of Raman spectroscopic fingerprints.World journal of microbiology & biotechnology · 2026Article
- Nanotechnology-Driven Strategies to Combat Antimicrobial Resistance in the One Health Era.Journal of clinical practice and research · 2026Review
- Enzymatic DNA Reaction Networks for Orchestrating Stimuli-Dependent Temporal Molecular Pulse.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Meat-Borne Bacterial Pathogen Detection: Conventional, Molecular and Emerging AI-Based Strategies.Diagnostics (Basel, Switzerland) · 2026Review
- Rapid Identification of Trace Pharmacodynamic Substances in Traditional Chinese Medicine via SERS and Deep Learning.Biosensors · 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
- Molecular mechanisms of antibiotic resistance in Gram-positive and Gram-negative bacteria - a narrative review.Frontiers in cellular and infection microbiology · 2026Review
- Explainable AI-SERS approach for highly accurate discrimination ofCurrent research in microbial sciences · 2026Article
- Molecular insights into antimicrobial resistance in human bacterial pathogens: mechanisms, resistance genes, and translational diagnostic applications.Frontiers in microbiology · 2026Review
- Silver integrated hybrids and nanocomposites for next-generation biomedicine: Beyond antimicrobial coatings toward smart sense-response-heal platforms.Materials today. Bio · 2025Review
- Integration of Artificial Intelligence in Biosensors for Enhanced Detection of Foodborne Pathogens.Biosensors · 2025Review
- Rapid Detection of Perfluorooctanesulfonic Acid Using Surface-Enhanced Raman Spectroscopy and Deep Learning.ACS omega · 2025Article
- Explainable Deep Learning Framework for SERS Bioquantification.ACS sensors · 2025Article
- Utilization of probiotics against virulence factors of Staphylococcus aureus in bovine mastitis.Antonie van Leeuwenhoek · 2025Review
- A Pilot Study on Single-Cell Raman Spectroscopy Combined with Machine Learning for Phenotypic Characterization ofMicroorganisms · 2025Article
- Analytical techniques and molecular platforms for detection and surveillance of antimicrobial resistance: advancements of the past decade.3 Biotech · 2025Review
- Emerging trends in SERS-based veterinary drug detection: multifunctional substrates and intelligent data approaches.NPJ science of food · 2025Review
- Detecting antibiotic resistance: classical, molecular, advanced bioengineering, and AI-enhanced approaches.Frontiers in microbiology · 2025Review
- Machine Learning in FTIR Spectrum for the Identification of Antibiotic Resistance: A Demonstration with Different Species of Microorganisms.Antibiotics (Basel, Switzerland) · 2024Article
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Authors and funding
7 authors.
Funding
Abstract
Over the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door-antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and accurately is crucial in preventing antibiotic resistance development, bacterial identification techniques include some challenging processes. To address this challenge, we proposed a deep neural network (DNN) that can discriminate antibiotic-resistant bacteria using surface-enhanced Raman spectroscopy (SERS). Stacked autoencoder (SAE)-based DNN was used for the rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) bacteria using a label-free SERS technique. The performance of the DNN was compared with traditional classifiers. Since the SERS technique provides high signal-to-noise ratio (SNR) data, some subtle differences were found between MRSA and MSSA in relative band intensities. SAE-based DNN can learn features from raw data and classify them with an accuracy of 97.66%. Moreover, the model discriminates bacteria with an area under curve (AUC) of 0.99. Compared to traditional classifiers, SAE-based DNN was found superior in accuracy and AUC values. The obtained results are also supported by statistical analysis. These results demonstrate that deep learning has great potential to characterize and detect antibiotic-resistant bacteria by using SERS spectral data.
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