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Stress-induced Antibiotic Susceptibility Testing on a Chip

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Published on: January 8, 2014

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.

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Immunofluorescence Analysis of Stress Granule Formation After Bacterial Challenge of Mammalian Cells

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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.