Using artificial intelligence and computer vision to detect and monitor unhealthy child-directed food and beverage marketing: a data-driven study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».