Machine learning-enabled prediction of antimicrobial resistance in foodborne pathogens

ABSTRACT

The World Health Organization (WHO) has identified antimicrobial resistance (AMR) as one of the top three global dangers to public health. One of the most vital factors contributing to the high prevalence of AMR is the misuse/overuse of antibiotics for treatment and/or as a growth promoter in the food industry. AMR can be transmitted to humans via food, the environment, or other channels through horizontal gene transfer. Therefore, efficient methods are urgently needed to determine whether bacteria are resistant to antibiotics. This work provides a review of the advances in machine learning (ML) techniques for predicting and identifying AMR in foodborne pathogens. We also emphasize the groundbreaking potential of whole genome sequencing (WGS) and spectroscopy technologies combined with ML in the context of AMR detection. These offer enormous potential because of their unique characteristics, which can overcome inherent limits in existing detection approaches.

KEYWORDS:

• Foodborne pathogens
• food safety
• antimicrobial resistance
• machine learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under Grant [32272431].