Year: 2025
Type: article
Abstract: Introduction. Food allergies represent a leading cause of adverse reactions and hospital admissions among children, with significant impact on quality of life and public health. The rapid and accurate detection of allergens in meals is therefore crucial for safety. Materials and methods. We developed an AI-based prototype that combines YOLOv8n, a state-of-the-art object detection model trained on the Allergen30 dataset, with Gemini 2.0 Flash, an advanced generative model, to provide multimodal allergen analysis. All images were preprocessed and split into training (70%), validation (15%), and test (15%) sets, with careful class balancing. Results. The system achieved high class-specific performance in detecting allergenic foods from real meal images, with mAP50 >90% and detailed contextual analysis via Gemini 2.0 Flash. Discussion and conclusions. AI-assisted allergen analysis from meal images is feasible and shows promise, but does not replace ingredient disclosure or clinical precautions. Further development and real-world validation are warranted
Institution: University of Campania "Luigi Vanvitelli"
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7215399019