Nutrition Research and Practice

  • 제16권6호
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  • Pages.801-812
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  • 2022
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  • 1976-1457(pISSN)
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  • 2005-6168(eISSN)
한국영양학회 (The Korean Nutrition Society)
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Challenges of diet planning for children using artificial intelligence

  • Changhun, Lee (Department of Industrial Engineering, Ulsan National Institute of Science and Technology (UNIST)) ;
  • Soohyeok, Kim (Department of Industrial Engineering, Ulsan National Institute of Science and Technology (UNIST)) ;
  • Jayun, Kim (Kosin Innovative Smart Healthcare Research Center, Kosin University Gospel Hospital) ;
  • Chiehyeon, Lim (Department of Industrial Engineering, Ulsan National Institute of Science and Technology (UNIST)) ;
  • Minyoung, Jung (Kosin Innovative Smart Healthcare Research Center, Kosin University Gospel Hospital)
  • 투고 : 2021.09.16
  • 심사 : 2022.03.23
  • 발행 : 2022.12.01
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초록

BACKGROUND/OBJECTIVES: Diet planning in childcare centers is difficult because of the required knowledge of nutrition and development as well as the high design complexity associated with large numbers of food items. Artificial intelligence (AI) is expected to provide diet-planning solutions via automatic and effective application of professional knowledge, addressing the complexity of optimal diet design. This study presents the results of the evaluation of the utility of AI-generated diets for children and provides related implications. MATERIALS/METHODS: We developed 2 AI solutions for children aged 3-5 yrs using a generative adversarial network (GAN) model and a reinforcement learning (RL) framework. After training these solutions to produce daily diet plans, experts evaluated the human- and AI-generated diets in 2 steps. RESULTS: In the evaluation of adequacy of nutrition, where experts were provided only with nutrient information and no food names, the proportion of strong positive responses to RL-generated diets was higher than that of the human- and GAN-generated diets (P < 0.001). In contrast, in terms of diet composition, the experts' responses to human-designed diets were more positive when experts were provided with food name information (i.e., composition information). CONCLUSIONS: To the best of our knowledge, this is the first study to demonstrate the development and evaluation of AI to support dietary planning for children. This study demonstrates the possibility of developing AI-assisted diet planning methods for children and highlights the importance of composition compliance in diet planning. Further integrative cooperation in the fields of nutrition, engineering, and medicine is needed to improve the suitability of our proposed AI solutions and benefit children's well-being by providing high-quality diet planning in terms of both compositional and nutritional criteria.

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과제정보

We appreciate the constructive feedback and advice from the experts who participated in the 2 surveys of this research. We also thank people in the National Institute of Food & Nutrition Service, Centers for Children's Food Service Management in Gwangju-si (Gyeonggi-do), Haeundae-gu (Busan), and Gijang-gun (Busan), as well as the Ulsan National Institute of Science and Technology (UNIST) Child Education Center.

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