ArticleNature communications2019
Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 254 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
254 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of AI-based raman spectroscopy in the diagnosis of gastric cancer: a systematic review and meta-analysis.Lasers in medical science · 2026Pooled it
- Plasmonic metasurface microfluidic accelerates redox reactions for rapid phenotypic antibiotic susceptibility testing.Science advances · 2026Article
- Unlocking the power ofmSystems · 2026Article
- Boosting identification of microsporidian spores originating from different hosts: single-cell Raman spectroscopy combined with self-attention mechanism-driven convolutional neural network.Analytical and bioanalytical chemistry · 2026Article
- A Review of SERS-Based Bacterial Detection from Nanomaterials to Integrated Clinical Platforms.Biosensors · 2026Review
- Artificial Intelligence in Foodborne Pathogen Detection from Sensing to Food Safety Systems: A Systematic Review.Foods (Basel, Switzerland) · 2026Review
- PLANCK: super-multiplex optical imaging without labeling.bioRxiv : the preprint server for biology · 2026Article
- Photonic biosensors based on nanoparticle superstructures: from data analysis to artificial intelligence (AI) detection.Fundamental research · 2026Review
- Characterization of theInternational journal of molecular sciences · 2026Article
- Causal Responsibility Based Explainable AI for Vibrational Spectroscopy Applied to Oral FTIR and Oesophageal Raman Diagnostics.Research square · 2026Article
- AI/ML-Assisted SERS Biosensing for Biomolecular Detection: From Direct Spectral Response to Integrated Diagnostic Systems.Biosensors · 2026Review
- Precision culturomics enabled by unlabeled single-cell morphology and Raman spectra.Nature communications · 2026Article
- Advances in Foodborne Pathogen Detection: From Conventional Confirmation to Integrated and Intelligent Platforms.Foods (Basel, Switzerland) · 2026Review
- PlasticAnalytics: A Deep Learning-Powered Spectral Library and Analytical Suite.Environmental science & technology · 2026Article
- One-day rapid sterility test for human-derived biopharmaceuticals.Nature biomedical engineering · 2026Article
- Next-Generation SERS Probes: Engineering Hotspots, Intelligent Molecular Targeting, and AI-Driven Spectral Analysis for Emerging Applications.Nanomaterials (Basel, Switzerland) · 2026Review
- Characterization of theInternational journal of molecular sciences · 2026Article
- Untargeted GC-IMS Metabolomics of Wound Headspace for Bacterial Infection Biomarker Discovery.Metabolites · 2026Article
- Rapid Antifungal Susceptibility Testing by Stimulated Raman Photothermal Imaging of DAnalytical chemistry · 2026Article
- Capillary microfluidics aided single-cell manipulation for optical detection of bacterial pathogens.Biophysical reviews · 2026Review
194 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.