Research Department, Daping Hosipital, Army Medical University, Chongqing, 400042, China.
Laboratory Department, Daping Hosipital, Army Medical University, Chongqing, 400042, China.
BMC Bioinformatics. 2024 Nov 6;25(1):347. doi: 10.1186/s12859-024-05967-4.
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.
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.
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.
抗生素的不当使用导致的抗菌药物耐药性不断增加，给人类带来了巨大挑战。在临床环境中快速准确地鉴定微生物种类对于精确用药和减少抗菌药物耐药性的发展至关重要。本研究旨在探索一种使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。
应用扩充的 AlexNet 产生了最佳结果。通过交叉验证，对单个细菌培养物分类的平均准确率达到 99.24%，随机混合培养物中三种细菌的识别准确率分别为 SA：98.6%、EC：98.58%和 PA：98.99%。
这项工作为快速识别细菌微生物提供了一种新方法。使用该方法可以自动识别 GC-IMS 检测结果中的细菌，帮助临床医生快速检测细菌种类，准确用药，从而控制疫情，最大限度地减少细菌耐药性对社会的负面影响。
