ArticleNutrients2022
A Cross-Sectional Reproducibility Study of a Standard Camera Sensor Using Artificial Intelligence to Assess Food Items: The FoodIntech Project.
Article in Nutrients, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it, 17 citations in OpenAlex.
- Technology to Automatically Record Eating Behavior in Real Life: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Applying Image-Based Food-Recognition Systems on Dietary Assessment: A Systematic Review.Advances in nutrition (Bethesda, Md.) · 2022Pooled it
- Navigating next-gen nutrition care using artificial intelligence-assisted dietary assessment tools-a scoping review of potential applications.Frontiers in nutrition · 2025Review
- Detailed assessment of night shift work aspects and potential mediators of its health effects: the contribution of field studies.Frontiers in public health · 2025Article
- LeFood-set: Baseline performance of predicting level of leftovers food dataset in a hospital using MT learning.PloS one · 2025Article
- Artificial Intelligence Applications to Measure Food and Nutrient Intakes: Scoping Review.Journal of medical Internet research · 2024Article
- Article
- Historical Text Line Segmentation Using Deep Learning Algorithms: Mask-RCNN against U-Net Networks.Journal of imaging · 2024Article
- Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
Having a system to measure food consumption is important to establish whether individual nutritional needs are being met in order to act quickly and to minimize the risk of undernutrition. Here, we tested a smartphone-based food consumption assessment system named FoodIntech. FoodIntech, which is based on AI using deep neural networks (DNN), automatically recognizes food items and dishes and calculates food leftovers using an image-based approach, i.e., it does not require human intervention to assess food consumption. This method uses one-input and one-output images by means of the detection and synchronization of a QRcode located on the meal tray. The DNN are then used to process the images and implement food detection, segmentation and recognition. Overall, 22,544 situations analyzed from 149 dishes were used to test the reliability of this method. The reliability of the AI results, based on the central intra-class correlation coefficient values, appeared to be excellent for 39% of the dishes (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.