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Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning

Author

Listed:
  • Chi-Sing Ho

    (Stanford University
    Stanford University)

  • Neal Jean

    (Stanford University
    Stanford University)

  • Catherine A. Hogan

    (Stanford University School of Medicine
    Stanford Health Care)

  • Lena Blackmon

    (Stanford University)

  • Stefanie S. Jeffrey

    (Stanford University School of Medicine)

  • Mark Holodniy

    (Stanford University School of Medicine
    VA Palo Alto Health Care System
    Stanford University School of Medicine)

  • Niaz Banaei

    (Stanford University School of Medicine
    Stanford Health Care
    Stanford University School of Medicine)

  • Amr A. E. Saleh

    (Stanford University
    Cairo University)

  • Stefano Ermon

    (Stanford University)

  • Jennifer Dionne

    (Stanford University)

Abstract

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.

Suggested Citation

  • Chi-Sing Ho & Neal Jean & Catherine A. Hogan & Lena Blackmon & Stefanie S. Jeffrey & Mark Holodniy & Niaz Banaei & Amr A. E. Saleh & Stefano Ermon & Jennifer Dionne, 2019. "Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning," Nature Communications, Nature, vol. 10(1), pages 1-8, December.
  • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-12898-9
    DOI: 10.1038/s41467-019-12898-9
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    Cited by:

    1. Hao He & Maofeng Cao & Yun Gao & Peng Zheng & Sen Yan & Jin-Hui Zhong & Lei Wang & Dayong Jin & Bin Ren, 2024. "Noise learning of instruments for high-contrast, high-resolution and fast hyperspectral microscopy and nanoscopy," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    2. Oleksii Ilchenko & Yurii Pilhun & Andrii Kutsyk & Denys Slobodianiuk & Yaman Goksel & Elodie Dumont & Lukas Vaut & Chiara Mazzoni & Lidia Morelli & Sofus Boisen & Konstantinos Stergiou & Yaroslav Auli, 2024. "Optics miniaturization strategy for demanding Raman spectroscopy applications," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    3. Alexandre Girard & Anneli Cooper & Samuel Mabbott & Barbara Bradley & Steven Asiala & Lauren Jamieson & Caroline Clucas & Paul Capewell & Francesco Marchesi & Matthew P Gibbins & Franziska Hentzschel , 2021. "Raman spectroscopic analysis of skin as a diagnostic tool for Human African Trypanosomiasis," PLOS Pathogens, Public Library of Science, vol. 17(11), pages 1-28, November.
    4. Alberto Signoroni & Alessandro Ferrari & Stefano Lombardi & Mattia Savardi & Stefania Fontana & Karissa Culbreath, 2023. "Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    5. Liping Huang & Hongwei Sun & Liangbin Sun & Keqing Shi & Yuzhe Chen & Xueqian Ren & Yuancai Ge & Danfeng Jiang & Xiaohu Liu & Wolfgang Knoll & Qingwen Zhang & Yi Wang, 2023. "Rapid, label-free histopathological diagnosis of liver cancer based on Raman spectroscopy and deep learning," Nature Communications, Nature, vol. 14(1), pages 1-14, December.

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