ReviewFoods (Basel, Switzerland)2025
Application of Convolutional Neural Networks and Recurrent Neural Networks in Food Safety.
Review in Foods (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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
12 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
- Comprehensive overview of AI methodologies in nano-drug delivery Optimization and Design.NPJ precision oncology · 2026Review
- A Review of Machine Learning and AI Applications in Enhancing HACCP Systems for Ice Cream Manufacturing.Foods (Basel, Switzerland) · 2026Review
- Precision Livestock Farming and Biomedical Engineering: Assessing Feed Quality, Animal Health, and Behavior Using Machine Learning for Sensor Data.Sensors (Basel, Switzerland) · 2026Review
- Next-Generation Bionic Sensors for Small Molecule Detection: Integrating Synthetic Biology, Nanomaterials, and Artificial Intelligence.Micromachines · 2026Review
- Detection of DSCP-based traffic prioritization manipulations and their impact on network performance.Scientific reports · 2026Article
- Food characterization in the omics era: current advances, analytical approaches, and future perspectives.Frontiers in nutrition · 2026Review
- From spatial maps to treatment decisions: a roadmap for integrating explainable AI with multi-omics to guide precision immunotherapy.Biotechnology notes (Amsterdam, Netherlands) · 2026Review
- Deep learning in food quality and safety management: A review of architectures, applications, and future directions.Current research in food science · 2026Review
- Integration of Artificial Intelligence in Biosensors for Enhanced Detection of Foodborne Pathogens.Biosensors · 2025Review
- Color Dynamics, Pigments and Antioxidant Capacity in Pouteria sapota Puree During Frozen Storage: A Correlation Study Using CIELAB Color Space and Machine Learning Models.Plant foods for human nutrition (Dordrecht, Netherlands) · 2025Article
- Deep Learning-Enhanced Spectroscopic Technologies for Food Quality Assessment: Convergence and Emerging Frontiers.Foods (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
This review explores the application of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in food safety detection and risk prediction. This paper highlights the advantages of CNNs in image processing and feature recognition, as well as the powerful capabilities of RNNs (especially their variant LSTM) in time series data modeling. This paper also makes a comparative analysis in many aspects: Firstly, the advantages and disadvantages of traditional food safety detection and risk prediction methods are compared with deep learning technologies such as CNNs and RNNs. Secondly, the similarities and differences between CNNs and fully connected neural networks in processing image data are analyzed. Furthermore, the advantages and disadvantages of RNNs and traditional statistical modeling methods in processing time series data are discussed. Finally, the application directions of CNNs in food safety detection and RNNs in food safety risk prediction are compared. This paper also discusses combining these deep learning models with technologies such as the Internet of Things (IoT), blockchain, and federated learning to improve the accuracy and efficiency of food safety detection and risk warning. Finally, this paper mentions the limitations of RNNs and CNNs in the field of food safety, as well as the challenges in the interpretability of the model, and suggests the use of interpretable artificial intelligence (XAI) technology to improve the transparency of the model.
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.