ArticleApplied and environmental microbiology2023
Accelerating the Detection of Bacteria in Food Using Artificial Intelligence and Optical Imaging.
Article in Applied and environmental microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 14 papers, 1 of them a synthesis that pooled it.
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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.
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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.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI-Enabled Imaging for Pathogen Detection Under Stress Conditions: A Systematic Review.Comprehensive reviews in food science and food safety · 2026Pooled it
- Climate change impact on disease emergence and antimicrobial resistance: A perspective on transmission and detection.iScience · 2026Review
- Artificial intelligence in food safety.NPJ science of food · 2026Review
- Meat-Borne Bacterial Pathogen Detection: Conventional, Molecular and Emerging AI-Based Strategies.Diagnostics (Basel, Switzerland) · 2026Review
- Detection and identification of insect processed animal proteins in microscopic images using deep learning.Current research in food science · 2026Article
- Deep learning enabled rapid detection of live bacteria in the presence of food debris.NPJ science of food · 2025Article
- RapidFoods (Basel, Switzerland) · 2025Article
- Colony-YOLO: A Lightweight Micro-Colony Detection Network Based on Improved YOLOv8n.Microorganisms · 2025Article
- Supplier Risk Assessment-A Quantitative Tool for the Identification of Reliable Suppliers to Enhance Food Safety Across the Supply Chain.Foods (Basel, Switzerland) · 2025Article
- IGLOO: Machine Vision System for Determination of Solubilization Index in Phosphate-Solubilizing Bacteria.Microorganisms · 2025Article
- Resilient sustainable current and emerging technologies for foodborne pathogen detection.Sustainable food technology · 2025Review
- Aptamer-functionalized graphene quantum dots combined with artificial intelligence detect bacteria for urinary tract infections.Frontiers in cellular and infection microbiology · 2025Article
- Enhancing AI microscopy for foodborne bacterial classification using adversarial domain adaptation to address optical and biological variability.Frontiers in artificial intelligence · 2025Article
- Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation.Antibiotics (Basel, Switzerland) · 2023Review
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In assessing food microbial safety, the presence of Escherichia coli is a critical indicator of fecal contamination. However, conventional detection methods require the isolation of bacterial macrocolonies for biochemical or genetic characterization, which takes a few days and is labor-intensive. In this study, we show that the real-time object detection and classification algorithm You Only Look Once version 4 (YOLOv4) can accurately identify the presence of E. coli at the microcolony stage after a 3-h cultivation. Integrating with phase-contrast microscopic imaging, YOLOv4 discriminated E. coli from seven other common foodborne bacterial species with an average precision of 94%. This approach also enabled the rapid quantification of E. coli concentrations over 3 orders of magnitude with an
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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.