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Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method

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Artificial Intelligence Applications to Measure Food and Nutrient Intakes: Scoping Review.

Jiakun Zheng¹, Junjie Wang², Jing Shen³

¹School of Economics and Management, Shanghai University of Sport, Shanghai, China.

Journal of Medical Internet Research

|November 28, 2024

Summary

This summary is machine-generated.

Artificial intelligence (AI) offers advanced methods for assessing food and nutrient intake, overcoming limitations of traditional methods. AI tools show promise in improving accuracy and real-time monitoring for nutrition research and disease management.

Keywords:

AI AI-based artificial intelligence computer vision deep learning diet dietary assessments disease management food food intake machine learning measurement mobile phone natural language processing neural networks nutrient systematic literature

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Area of Science:

Nutrition Science
Artificial Intelligence
Health Informatics

Background:

Accurate food and nutrient intake measurement is vital for nutrition research, surveillance, and disease management.
Traditional methods (e.g., recalls, diaries) suffer from recall error and social desirability bias.
Artificial intelligence (AI) presents an opportunity for automated, objective, and scalable dietary assessment.

Purpose of the Study:

To conduct a scoping review of AI applications in food and nutrient intake assessment.
To synthesize evidence on the efficacy, accuracy, and challenges of AI-driven dietary assessment.
To identify current advantages and areas for improvement in AI dietary assessment tools.

Main Methods:

Adherence to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines.
Comprehensive literature search across PubMed, Web of Science, Cochrane Library, and EBSCO up to June 30, 2023.
Inclusion of studies utilizing modern AI approaches for human dietary intake assessment.

Main Results:

25 studies (2010-2023) used AI with diverse inputs: food images, wearable sensor data (sound, motion), and text.
AI models (deep learning, machine learning) achieved high accuracy in food detection (74-99.85%) and nutrient estimation (10-15% error).
AI systems provide real-time monitoring, enhancing precision and reducing recall bias compared to self-report methods.

Conclusions:

AI significantly improves accuracy, reduces labor, and enables real-time dietary monitoring.
Challenges include adapting to diverse foods, ensuring fairness, and addressing data privacy.
AI holds transformative potential for individual and population-level dietary assessment, supporting precision nutrition and chronic disease management.