ArticleBMC bioinformatics2024
Rapid bacterial identification through volatile organic compound analysis and deep learning.
Article in BMC bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
The trial behind it
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
3 citing papers in PubMed.
- Classification of bacterial biological warfare agent simulants through 2D Py-GC/MS data coupled with deep learning.Scientific reports · 2026Article
- Untargeted GC-IMS Metabolomics of Wound Headspace for Bacterial Infection Biomarker Discovery.Metabolites · 2026Article
- Identification of anaerobic bacterial strains by pyrolysis-gas chromatography-ion mobility spectrometry.Frontiers in bioengineering and biotechnology · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundThe increasing antimicrobial resistance caused by the improper use of antibiotics poses a significant challenge to humanity. Rapid and accurate identification of microbial species in clinical settings is crucial for precise medication and reducing the development of antimicrobial resistance. This study aimed to explore a method for automatic identification of bacteria using Volatile Organic Compounds (VOCs) analysis and deep learning algorithms.
resultsAlexNet, where augmentation is applied, produces the best results. The average accuracy rate for single bacterial culture classification reached 99.24% using cross-validation, and the accuracy rates for identifying the three bacteria in randomly mixed cultures were SA:98.6%, EC:98.58% and PA:98.99%, respectively.
conclusionThis work provides a new approach to quickly identify bacterial microorganisms. Using this method can automatically identify bacteria in GC-IMS detection results, helping clinical doctors quickly detect bacterial species, accurately prescribe medication, thereby controlling epidemics, and minimizing the negative impact of bacterial resistance on society.
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Registered trials
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