Using artificial intelligence and computer vision to detect and monitor unhealthy child-directed food and beverage marketing: a data-driven study
Bibliographic record
Abstract
Background: Childhood obesity and diet-related noncommunicable diseases are urgent global public health challenges, driven in part by children's continued exposure to persuasive marketing of unhealthy foods and beverages. Traditional methods that manually evaluate child-targeted food marketing practices are resource-intensive and inefficient. This study aims to address these limitations by leveraging artificial intelligence (AI), specifically computer vision and deep learning, to identify child-targeted food products and classify marketing elements on food packaging using a comprehensive database labeled with child-directed marketing features. Additionally, this study explores the practical application of AI within a specific food category to examine the relationships between child-directed marketing features, nutrition quality, and price, to estimate the potential impact of proposed Canadian marketing-to-children regulations. Methods: Image classification algorithms were trained on 8283 manually labeled food package images, annotated and validated between 2021 and 2024 using a validated child-appealing packaging (CAP) coding tool. This labeled dataset served as the ground truth for model training and evaluation. Three machine learning algorithms, k-nearest neighbors (kNN), support vector machines (SVM), and convolutional neural networks (CNN) were used to classify food package images targeted at children. In addition, latest image object detection model, YOLOv12 (released February 2025), were fine-tuned to identify specific child-targeted marketing features on food and beverage packaging. Model performance was evaluated using accuracy, precision, recall, F1 score, AUC, and confusion matrix. We applied this AI strategy to breakfast cereals (n = 1765) to assess the proportion of food products displaying child-directed marketing features and that would be restricted under Canadian proposed marketing-to-children regulations, and conducted regression analysis to investigate the relationship between food marketing features and food price. Findings: A total of 22 distinct child-directed marketing techniques featured on food and beverage packages were identified and annotated. The CNN-based image classification model outperformed kNN and SVM, achieving 0.90 accuracy and 0.96 AUC in identifying food marketing targeted at children against manually coded labels. Fine-tuned YOLOv12 object detection model demonstrated varying performance levels across child-targeted marketing features, reflecting the complexity and diversity of marketing strategies on food packaging. By applying this AI strategy, 39.2% of breakfast cereals were found to display child-directed marketing features, of which 89.5% would be considered as unhealthy and restricted under Canadian proposed marketing-to-children regulations. Products with child-directed marketing features showed a negative, but not statistically significant, association with price (coefficient = -0.25, p = 0.072). Interpretation: This study introduces the first AI-driven approach to effectively identify and categorize child-directed marketing on food and beverage packaging. These methods offer a scalable and efficient AI-driven solution for monitoring compliance with marketing-to-children policies and evaluating associations with nutrition quality and price, and can be extended to other data sources such as social media and online video content for broader applications. This study supports evidence-based policy development to protect children from unhealthy food marketing practices and provides critical insights into its potential impacts on children's health outcomes. Funding: This work was funded by the Data Sciences Institute catalyst grants, Health Canada research contract on M2K research, and the CIHR project grants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".