Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning

Chi-Sing Ho (), Neal Jean, Catherine A. Hogan, Lena Blackmon, Stefanie S. Jeffrey, Mark Holodniy, Niaz Banaei, Amr A. E. Saleh (), Stefano Ermon () and Jennifer Dionne ()
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Chi-Sing Ho: Stanford University
Neal Jean: Stanford University
Catherine A. Hogan: Stanford University School of Medicine
Lena Blackmon: Stanford University
Stefanie S. Jeffrey: Stanford University School of Medicine
Mark Holodniy: Stanford University School of Medicine
Niaz Banaei: Stanford University School of Medicine
Amr A. E. Saleh: Stanford University
Stefano Ermon: Stanford University
Jennifer Dionne: Stanford University

Nature Communications, 2019, vol. 10, issue 1, 1-8

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41467-019-12898-9

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