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A Cross-Sectional Reproducibility Study of a Standard Camera Sensor Using Artificial Intelligence to Assess Food

Virginie Van Wymelbeke-Delannoy1,2, Charles Juhel3, Hugo Bole3

  • 1Elderly Unit, University Hospital Center Dijon Bourgogne F Mitterrand, F-21000 Dijon, France.

Nutrients
|January 11, 2022
PubMed
Summary
This summary is machine-generated.

FoodIntech, an AI-powered smartphone system, accurately assesses food consumption and leftovers using image analysis. This technology helps ensure nutritional needs are met, reducing undernutrition risks.

Keywords:
artificial intelligencemachine learningmobile phone imagesportion evaluationreliability

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Area of Science:

  • Nutrition Science
  • Artificial Intelligence
  • Computer Vision

Background:

  • Accurate measurement of food consumption is crucial for assessing nutritional needs and preventing undernutrition.
  • Existing methods for food consumption assessment can be labor-intensive and prone to human error.
  • Technological solutions are needed to streamline and improve the accuracy of dietary intake monitoring.

Purpose of the Study:

  • To evaluate the reliability and effectiveness of FoodIntech, a smartphone-based system for automated food consumption assessment.
  • To determine the accuracy of FoodIntech in recognizing food items and quantifying leftovers using artificial intelligence.
  • To explore the potential of image-based analysis for non-invasive dietary monitoring.

Main Methods:

  • FoodIntech utilizes deep neural networks (DNN) for image-based food recognition and leftover calculation.
  • A QR code on the meal tray synchronizes input and output images for analysis.
  • The system performs food detection, segmentation, and recognition through DNN processing of images.

Main Results:

  • The study analyzed 22,544 situations across 149 dishes to assess FoodIntech's reliability.
  • AI results demonstrated excellent reliability for 39% of dishes and good reliability for 19%.
  • The system's accuracy indicates strong potential for automated dietary assessment.

Conclusions:

  • FoodIntech offers an effective method for improving food and dish recognition in consumption assessment.
  • The AI-driven approach minimizes the need for human intervention, enhancing efficiency.
  • The system's capabilities can be expanded to new dishes and foods with sufficient image data.