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Updated: Aug 19, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Challenges of diet planning for children using artificial intelligence.

Changhun Lee¹, Soohyeok Kim¹, Jayun Kim²

¹Department of Industrial Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea.

Nutrition Research and Practice

|December 5, 2022

Summary

Artificial intelligence (AI) can assist in planning children's diets, with reinforcement learning (RL) showing promise for nutritional adequacy. Human-designed diets were preferred for composition when food names were known, highlighting AI's potential and need for refinement.

Keywords:

Children artificial intelligence diet planning dieticians

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Area of Science:

Pediatric Nutrition
Artificial Intelligence in Healthcare
Dietary Planning Systems

Background:

Diet planning for childcare centers is complex due to nutritional and developmental knowledge requirements.
Artificial intelligence (AI) offers potential solutions for optimizing diet design in complex scenarios.
This study evaluates AI-generated diets for children aged 3-5 years.

Purpose of the Study:

To develop and evaluate AI-driven solutions for children's diet planning.
To compare the nutritional adequacy and composition of AI-generated diets against human-designed diets.
To explore the utility of AI in addressing the complexities of optimal diet design for young children.

Main Methods:

Developed two AI solutions using a generative adversarial network (GAN) and a reinforcement learning (RL) framework.
Trained AI models to generate daily diet plans for children aged 3-5 years.
Experts evaluated human- and AI-generated diets based on nutritional adequacy and diet composition.

Main Results:

Reinforcement learning (RL)-generated diets received higher expert ratings for nutritional adequacy when only nutrient information was provided (P < 0.001).
Human-designed diets were rated more favorably for diet composition when food names (composition information) were available to experts.
AI demonstrates potential in nutritional adequacy, while human expertise remains crucial for compositional aspects.

Conclusions:

This is the first study demonstrating AI development and evaluation for pediatric diet planning.
AI-assisted diet planning for children is feasible, emphasizing the importance of composition compliance.
Further interdisciplinary collaboration is essential to enhance AI solutions for children's dietary well-being.