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AI-Enabled Imaging for Pathogen Detection Under Stress Conditions: A Systematic Review
MeiLi Papa1, Gillian Kuehnle1, Yoo Jung Erika Oh2
1Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, Michigan, USA.
Comprehensive Reviews in Food Science and Food Safety
|April 23, 2026
Summary
Artificial intelligence (AI) imaging shows promise for pathogen detection, especially under stress. However, inconsistent reporting and lack of standardized benchmarks hinder reproducibility and widespread adoption of these advanced methods.
Area of Science:
- Microbiology
- Computer Science
- Biotechnology
Background:
- Traditional pathogen detection methods struggle with microbial signals in challenging environments.
- Artificial intelligence (AI) offers potential for enhanced microbial signal capture.
Purpose of the Study:
- To systematically review AI-enabled imaging for pathogen detection.
- Evaluate AI's performance, application, and methodology in detecting pathogens, particularly under stress conditions.
Main Methods:
- Systematic literature search across five databases using keywords related to AI, pathogen detection, and imaging.
- Inclusion criteria based on the PICOS framework, focusing on AI-enhanced microscopy.
- Data extraction following PRISMA guidelines, capturing biological, imaging, AI, and performance data.
Main Results:
- 28 studies met inclusion criteria, covering over 40 pathogens (e.g., Salmonella, E. coli).
- Only three studies examined stress or inactivated pathogen states.
- Limited reporting of comparator methods and inconsistencies in protocols complicated benchmarking and meta-analysis.
Conclusions:
- AI-enabled imaging presents a comprehensive approach to pathogen detection from biological and computational viewpoints.
- Standardized benchmarks and reporting practices are crucial for reproducible and transferable AI-based pathogen detection.
