ArticleNutrients2019
An End-to-End Image-Based Automatic Food Energy Estimation Technique Based on Learned Energy Distribution Images: Protocol and Methodology.
Article in Nutrients, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Digital management of diabetes global research trends: a bibliometric study.Frontiers in medicine · 2025Pooled it
- AI-based digital image dietary assessment methods compared to humans and ground truth: a systematic review.Annals of medicine · 2023Pooled it
- Accuracy of energy and nutrient intake estimation versus observed intake using 4 technology-assisted dietary assessment methods: a randomized crossover feeding study.The American journal of clinical nutrition · 2024Trial
- User Preferences for an Image-Assisted Dietary Recall: Qualitative Study Comparing 3 Dietary Assessment Methods.JMIR human factors · 2025Article
- Long-Tailed Continual Learning For Visual Food Recognition.IEEE transactions on multimedia · 2025Article
- Image-Based Dietary Energy and Macronutrients Estimation with ChatGPT-5: Cross-Source Evaluation Across Escalating Context Scenarios.Nutrients · 2025Article
- Nutritional assessment system integrating semantic segmentation and point cloud modeling techniques.Scientific reports · 2025Article
- IGSMNet: Ingredient-Guided Semantic Modeling Network for Food Nutrition Estimation.Foods (Basel, Switzerland) · 2025Article
- Improving Diet Quality of People Living With Obesity by Building Effective Dietetic Service Delivery Using Technology in a Primary Health Care Setting: Protocol for a Randomized Controlled Trial.JMIR research protocols · 2025Article
- The Internet of Things in the Nutritional Management of Patients with Chronic Neurological Cognitive Impairment: A Scoping Review.Healthcare (Basel, Switzerland) · 2024Article
- Artificial Intelligence Applications to Measure Food and Nutrient Intakes: Scoping Review.Journal of medical Internet research · 2024Article
- Advancements in Using AI for Dietary Assessment Based on Food Images: Scoping Review.Journal of medical Internet research · 2024Article
- Automated Artificial Intelligence-Based Thai Food Dietary Assessment System: Development and Validation.Current developments in nutrition · 2024Article
- Surveying Nutrient Assessment with Photographs of Meals (SNAPMe): A Benchmark Dataset of Food Photos for Dietary Assessment.Nutrients · 2023Article
- Artificial intelligence in diabetes management: Advancements, opportunities, and challenges.Cell reports. Medicine · 2023Review
- An Automated Image-Based Dietary Assessment System for Mediterranean Foods.IEEE open journal of engineering in medicine and biology · 2023Article
- Health and sustainability co-benefits of eating behaviors: Towards a science of dietary eco-wellness.Preventive medicine reports · 2022Review
- Multi-Device Nutrition Control.Sensors (Basel, Switzerland) · 2022Article
- An Active Image-Based Mobile Food Record Is Feasible for Capturing Eating Occasions among Infants Ages 3-12 Months Old in Hawai'i.Nutrients · 2022Article
- A Cross-Sectional Reproducibility Study of a Standard Camera Sensor Using Artificial Intelligence to Assess Food Items: The FoodIntech Project.Nutrients · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Obtaining accurate food portion estimation automatically is challenging since the processes of food preparation and consumption impose large variations on food shapes and appearances. The aim of this paper was to estimate the food energy numeric value from eating occasion images captured using the mobile food record. To model the characteristics of food energy distribution in an eating scene, a new concept of "food energy distribution" was introduced. The mapping of a food image to its energy distribution was learned using Generative Adversarial Network (GAN) architecture. Food energy was estimated from the image based on the energy distribution image predicted by GAN. The proposed method was validated on a set of food images collected from a 7-day dietary study among 45 community-dwelling men and women between 21-65 years. The ground truth food energy was obtained from pre-weighed foods provided to the participants. The predicted food energy values using our end-to-end energy estimation system was compared to the ground truth food energy values. The average error in the estimated energy was 209 kcal per eating occasion. These results show promise for improving accuracy of image-based dietary assessment.
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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.