ArticleACS omega2022
Highly Accurate Identification of Bacteria's Antibiotic Resistance Based on Raman Spectroscopy and U-Net Deep Learning Algorithms.
Article in ACS omega, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Nanotechnology-Driven Strategies to Combat Antimicrobial Resistance in the One Health Era.Journal of clinical practice and research · 2026Review
- Review
- Engineering Useful Microbial Species for Pharmaceutical Applications.Microorganisms · 2025Review
- 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
- Raman spectroscopic deep learning with signal aggregated representations for enhanced cell phenotype and signature identification.PNAS nexus · 2024Article
- Enhancing Open-World Bacterial Raman Spectra Identification by Feature Regularization for Improved Resilience against Unknown Classes.Chemical & biomedical imaging · 2024Article
- Unveiling the efficacy of a bulk Raman spectra-based model in predicting single cell Raman spectra of microorganisms.Heliyon · 2024Article
- Deep learning and single-cell phenotyping for rapid antimicrobial susceptibility detection in Escherichia coli.Communications biology · 2023Article
- Raman ConvMSANet: A High-Accuracy Neural Network for Raman Spectroscopy Blood and Semen Identification.ACS omega · 2023Article
- Machine Learning Methods for Small Data Challenges in Molecular Science.Chemical reviews · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bacterial pathogens especially antibiotic-resistant ones are a public health concern worldwide. To oppose the morbidity and mortality associated with them, it is critical to select an appropriate antibiotic by performing a rapid bacterial diagnosis. Using a combination of Raman spectroscopy and deep learning algorithms to identify bacteria is a rapid and reliable method. Nevertheless, due to the loss of information during training a model, some deep learning algorithms suffer from low accuracy. Herein, we modify the U-Net architecture to fit our purpose of classifying the one-dimensional Raman spectra. The proposed U-Net model provides highly accurate identification of the 30 isolates of bacteria and yeast, empiric treatment groups, and antimicrobial resistance, thanks to its capability to concatenate and copy important features from the encoder layers to the decoder layers, thereby decreasing the data loss. The accuracies of the model for the 30-isolate level, empiric treatment level, and antimicrobial resistance level tasks are 86.3, 97.84, and 95%, respectively. The proposed deep learning model has a high potential for not only bacterial identification but also for other diagnostic purposes in the biomedical field.
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Registered trials
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