The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning
Open accessAbstract
Raman optical spectroscopy promises label-free bacterial detection, identification, and antibiotic susceptibility testing in a single step. However, achieving clinically relevant speeds and accuracies remains challenging due to weak Raman signal from bacterial cells and numerous bacterial species and phenotypes. Here we generate an extensive dataset of bacterial Raman spectra and apply deep learning approaches to accurately identify 30 common bacterial pathogens. Even on low signal-to-noise spectra, we achieve average isolate-level accuracies exceeding 82% and antibiotic treatment identification accuracies of 97.0±0.3%. We also show that this approach distinguishes between methicillin-resistant and -susceptible isolates of Staphylococcus aureus (MRSA and MSSA) with 89±0.1% accuracy. We validate our results on clinical isolates from 50 patients. Using just 10 bacterial spectra from each patient isolate, we achieve treatment identification accuracies of 99.7%. Our approach has potential for culture-free pathogen identification and antibiotic susceptibility testing, and could be readily extended for diagnostics on blood, urine, and sputum.
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
Surviving Sepsis Campaign: International Guidelines for Management of Severe Sepsis and Septic Shock
Dellinger, R. P., Levy, M. M., Rhodes, A., Annane, D., Gerlach, H., Opal, S. M., Sevransky, J. E., Sprung, C. L., Douglas, I. S., Jaeschke, R., et al. (2013) · Critical Care Medicine · doi:10.1097/ccm.0b013e31827e83af · Cited by 9,694
The Surviving Sepsis Campaign Guidelines Committee including The Pediatric Subgroup*, Dellinger, R. P., Levy, M. M., Rhodes, A., Annane, D., Gerlach, H., Opal, S. M., Sevransky, J. E., Sprung, C. L., Douglas, I. S., et al. (2013) · Intensive Care Medicine · doi:10.1007/s00134-012-2769-8 · Cited by 7,290
Dellinger, R. P., Levy, M. M., Carlet, J. M., Bion, J., Parker, M. M., Jaeschke, R., Reinhart, K., Angus, D. C., Brun-Buisson, C., Beale, R., et al. (2008) · Intensive Care Medicine · doi:10.1007/s00134-007-0934-2 · Cited by 6,772
(2005) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.200405-644st · Cited by 5,877
Learning a Deep Convolutional Network for Image Super-Resolution
Dong, C., Loy, C. C., He, K., & Tang, X. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10593-2_13 · Cited by 5,494
Deep visual-semantic alignments for generating image descriptions
Karpathy, A., & Fei-Fei, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298932 · Cited by 4,902
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Kobayashi-Kirschvink, K. J., Comiter, C. S., Gaddam, S., Joren, T., Grody, E. I., Ounadjela, J. R., Zhang, K., Ge, B., Kang, J. W., Xavier, R. J., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-02082-2 · Cited by 93
Blood culture-free ultra-rapid antimicrobial susceptibility testing
Kim, T. H., Kang, J., Jang, H., Joo, H., Lee, G. Y., Kim, H., Cho, U., Bang, H., Jang, J., Han, S., et al. (2024) · Nature · doi:10.1038/s41586-024-07725-1 · Cited by 90
Zhang, Y., Chang, K., Ogunlade, B., Herndon, L., Tadesse, L. F., Kirane, A. R., & Dionne, J. A. (2024) · ACS Nano · doi:10.1021/acsnano.4c04282 · Cited by 83
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Metadata
- Authors
- Ho, C.-S., Jean, N., Hogan, C. A., Blackmon, L., Jeffrey, S. S., Holodniy, M., Banaei, N., Saleh, A. A. E., Ermon, S., & Dionne, J.
- Venue
- Nature Communications
- Published
- 2019-10-30
- Volume
- 10
- Issue
- 1
- Pages
- 4927-4927
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2982482221
- Cited by
- 871
- References
- 57
- FWCI
- 32.97
- Type
- journal article
- Language
- en
- Open access
- gold
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- Ho, C.-S., Jean, N., Hogan, C. A., Blackmon, L., Jeffrey, S. S., Holodniy, M., Banaei, N., Saleh, A. A. E., Ermon, S., & Dionne, J. (2019). Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning. Nature Communications. https://doi.org/10.1038/s41467-019-12898-9
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