Development and Evaluation of an AI-Enabled Nutrient Intake Monitoring App for Obesity and Diabetes Prevention in Young People: A Comparative Study with Live Scanning and Photo-Uploading Methods

Obesity and diabetes are prevalent health issues worldwide, especially among young people. To address this, an app was proposed to help users monitor their daily nutrient intake and prevent obesity and diabetes [1]. The app uses AI scanning to analyze the nutrient level of food and suggests a suitable daily nutrient intake for the user based on their age and gender. Data storage allows users to track their meal history and create a personalized diet plan [2]. The app was compared to similar systems, and it was found that live scanning is more intuitive and convenient than photo uploading. Additionally, the proposed app was tested in two experiments and was found to be effective in identifying food items and received generally positive feedback from users, but further improvements are necessary to enhance accuracy and user experience. In the first experiment, the accuracy of the AI model for predicting food items was tested using a combination of existing and customized datasets [3]. A total of 227 food items were tested, including bananas, watermelons, peaches, tomatoes, pineapples, rice, fries, hamburgers, eggs, noodles, and other items. The results showed an overall accuracy rate of 82% for all food items tested, with pineapple having the highest accuracy at 100% and peaches having the lowest accuracy at 60%. In the second experiment, 15 participants tested the application's features and provided feedback through a survey. The results showed that the application was successful in its implementation of features and received generally positive feedback, with an average functionality rating of 8.13 and an average convenience rating of 7.67.