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

Journal: BMC bioinformatics
PMID: 39506632

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.

Authors

  • Bowen Yan
    Research Department, Daping Hosipital, Army Medical University, Chongqing, 400042, China.
  • Lin Zeng
    Department of Health Management, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
  • Yanyi Lu
    Research Department, Daping Hosipital, Army Medical University, Chongqing, 400042, China.
  • Min Li
    Hubei Provincial Institute for Food Supervision and Test, Hubei Provincial Engineering and Technology Research Center for Food Quality and Safety Test, Wuhan 430075, China.
  • Weiping Lu
    Laboratory Department, Daping Hosipital, Army Medical University, Chongqing, 400042, China.
  • Bangfu Zhou
    Research Department, Daping Hosipital, Army Medical University, Chongqing, 400042, China.
  • Qinghua He
    Research Department, Daping Hosipital, Army Medical University, Chongqing, 400042, China. heqinghua@tmmu.edu.cn.

Keywords

External Resources

Popular Topics
Recent Journals