SynthesisComprehensive reviews in food science and food safety2026
AI-Enabled Imaging for Pathogen Detection Under Stress Conditions: A Systematic Review.
Synthesis in Comprehensive reviews in food science and food safety, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Artificial Intelligence in Foodborne Pathogen Detection from Sensing to Food Safety Systems: A Systematic Review.Foods (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Advances in pathogen detection that incorporate artificial intelligence (AI) may capture microbial signals under challenging environmental conditions that traditional methods miss. This systematic review evaluates the application, performance, and methodological characteristics of AI-enabled imaging for pathogen detection, including its impact on speed, accuracy, and modeling under stress conditions. Studies were systematically identified from five electronic databases using search terms related to AI, pathogen, detection, and imaging. Inclusion criteria, defined using the Population, Intervention, Comparators, Outcome, Study design (PICOS) framework, focused on microscopy-based pathogen detection enhanced by AI. Data extraction followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and captured biological sample preparation, imaging modalities, AI-enabled data analyses, comparator methods, and performance metrics. Of 2736 citations retrieved, 120 were reviewed in full and 28 studies met the inclusion criteria. These represented more than 40 pathogens, most commonly Salmonella spp. and Escherichia coli. Only three studies explicitly evaluated signals from stress or inactivated states. Comparator methods (e.g., culture-based or molecular assays) were infrequently reported, limiting benchmarking against established workflows. Reporting inconsistencies in laboratory protocols and computational pipeline further complicated reproducibility and precluded meta-analysis. Overall, this review offers a comprehensive overview of current AI-enabled imaging approaches from both biological and computational perspectives and highlights the need for standardized benchmarks and reporting practices to support reproducible, transferable pathogen detection.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.