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Related Experiment Video

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Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method
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goFOODTM: An Artificial Intelligence System for Dietary Assessment.

Ya Lu1, Thomai Stathopoulou1, Maria F Vasiloglou1

  • 1ARTORG Center for Biomedical Engineering Research, University of Bern, 3008 Bern, Switzerland.

Sensors (Basel, Switzerland)
|August 6, 2020
PubMed
Summary

A new artificial intelligence (AI) system, goFOOD™, estimates meal nutrition from smartphone images. This dietary assessment tool accurately determines calories and macronutrients, aiding healthier eating habits.

Keywords:
caloriecarbohydratecomputer visionfatnutrient estimationproteinsmartphone

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Area of Science:

  • Computer Science
  • Nutrition Science
  • Biomedical Engineering

Background:

  • Accurate nutritional information is crucial for promoting healthier diets and improving clinical outcomes.
  • Traditional dietary assessment methods can be time-consuming and prone to inaccuracies.

Purpose of the Study:

  • To introduce goFOOD™, an AI-powered system for estimating meal calorie and macronutrient content using smartphone images.
  • To evaluate the performance of goFOOD™ compared to experienced dietitians.

Main Methods:

  • goFOOD™ utilizes deep neural networks for food detection, segmentation, and recognition from two meal images or a short video.
  • A 3D reconstruction algorithm estimates food volume, combined with a nutrient database for calculations.
  • The system supports 319 fine-grained food categories.

Main Results:

  • goFOOD™ demonstrated superior performance compared to experienced dietitians on a non-standardized meal database.
  • The system's accuracy was comparable to dietitians on a fast-food meal database.
  • Validation was performed on two diverse multimedia databases.

Conclusions:

  • goFOOD™ offers a simple, efficient, and accurate solution for end-users to perform dietary assessments.
  • AI-driven image analysis presents a promising approach for automated nutritional estimation.
  • This technology has the potential to support personalized nutrition and public health initiatives.