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Related Concept Videos

Assessment of the Gastrointestinal System II: Health Perception Pattern01:29

Assessment of the Gastrointestinal System II: Health Perception Pattern

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Assessing the gastrointestinal (GI) system is a complex process that begins with collecting subjective data. This data, collected through patient interviews, provides crucial insights into the patient's health history, perception patterns, and lifestyle habits, all contributing significantly to GI health.
Health Perception Patterns
Health perception patterns offer valuable insights into a patient's lifestyle habits and how they may impact their GI health. These patterns include:
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Regulation of Food Intake01:30

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Short-term regulation of food intake primarily involves neural signals from the gastrointestinal (GI) tract, blood nutrient levels, and GI tract hormones. Communication between the gut and brain via vagal nerve fibers plays a significant role in evaluating the contents of the gut. Clinical studies have shown that protein ingestion produces a more prolonged response in these nerve fibers compared to an equivalent amount of glucose. Additionally, the activation of stretch receptors caused by GI...
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Related Experiment Video

Updated: Sep 11, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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Automatic Image Recognition Meal Reporting Among Young Adults: Randomized Controlled Trial.

Prasan Kumar Sahoo1,2, Sherry Yueh-Hsia Chiu3,4, Yu-Sheng Lin5

  • 1Department of Computer Science and Information Engineering, College of Engineering, Chang Gung University, Taoyuan, Taiwan.

JMIR Mhealth and Uhealth
|August 14, 2025
PubMed
Summary

A new AI-powered app for dietary intake assessment significantly improved food recognition accuracy and reporting efficiency compared to voice-based methods. This technology shows promise for enhancing usability in real-world meal scenarios.

Keywords:
Taiwanaccuracyartificial intelligenceautomatic food image recognitionefficacyimage recognitionmHealthnutritionrandomized controlled trialrecognitionspeech recognitionusability evaluationuser interactionuser perceptionvision technology

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Area of Science:

  • Artificial Intelligence in Nutrition
  • Dietary Assessment Technology
  • Human-Computer Interaction

Background:

  • Artificial intelligence (AI) offers potential for evaluating daily dietary intake.
  • Empirical studies are needed for AI in realistic meal scenarios.
  • This study developed an automated food recognition app to improve meal reporting usability.

Purpose of the Study:

  • To compare the performance of an automatic image-based reporting (AIR) app against a voice input reporting (VIR) app.
  • To assess accuracy, efficiency, and user perception of two distinct food intake reporting methods.
  • To evaluate AI-powered food recognition technology in authentic dining conditions.

Main Methods:

  • A 2-group randomized comparative study involving 42 young adults (aged 20-25).
  • Participants were assigned to either the AIR group (image + optional voice) or VIR group (voice-supplemented image).
  • Performance was evaluated based on reporting accuracy, time efficiency, and user perception using a standardized menu.

Main Results:

  • The AIR group achieved significantly higher dish identification accuracy (86%) than the VIR group (68%) (P<.001).
  • The AIR group demonstrated significantly greater time efficiency in food reporting (P<.001).
  • Both apps exhibited high usability and learnability, with no significant difference in user perception scores (P=.20).

Conclusions:

  • The AI-powered AIR app significantly outperformed the VIR app in accuracy and efficiency for meal reporting.
  • AI vision technology integrated into mobile apps shows promise for dietary assessment, despite needing further enhancement.
  • Results support the efficacy of automatic image recognition for user interaction and ease of use in dietary reporting.