Rapid bacterial identification through volatile organic compound analysis and deep learning.

IF 2.9 3区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

BMC Bioinformatics Pub Date : 2024-11-06 DOI:10.1186/s12859-024-05967-4

Bowen Yan, Lin Zeng, Yanyi Lu, Min Li, Weiping Lu, Bangfu Zhou, Qinghua He

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引用次数: 0

Abstract

Background: The 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.

Results: AlexNet, 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.

Conclusion: This 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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通过挥发性有机化合物分析和深度学习快速识别细菌。

背景：抗生素的不当使用导致抗菌药耐药性不断增加，给人类带来了巨大挑战。在临床环境中快速准确地识别微生物种类对于精确用药和减少抗菌药耐药性的产生至关重要。本研究旨在探索一种利用挥发性有机化合物（VOCs）分析和深度学习算法自动识别细菌的方法：结果：采用增强算法的 AlexNet 效果最好。通过交叉验证，单一细菌培养物分类的平均准确率达到 99.24%，随机混合培养物中识别三种细菌的准确率分别为 SA:98.6%、EC:98.58% 和 PA:98.99%：这项工作提供了一种快速识别细菌微生物的新方法。结论：这项研究提供了一种快速识别细菌微生物的新方法，利用这种方法可以自动识别 GC-IMS 检测结果中的细菌，帮助临床医生快速检测细菌种类，准确开具处方，从而控制流行病，将细菌耐药性对社会的负面影响降到最低。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

BMC Bioinformatics 生物-生化研究方法

CiteScore

5.70

自引率

3.30%

发文量

506

审稿时长

4.3 months

期刊介绍： BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology. BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.