ArticleNutrients2018
A Comparative Study on Carbohydrate Estimation: GoCARB vs. Dietitians.
Article in Nutrients, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
46 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- AI-based digital image dietary assessment methods compared to humans and ground truth: a systematic review.Annals of medicine · 2023Pooled it
- Applying Image-Based Food-Recognition Systems on Dietary Assessment: A Systematic Review.Advances in nutrition (Bethesda, Md.) · 2022Pooled it
- Experiences and Perceived Influence of the Artificial Intelligence-Based Health Education Accurately Linking System (AI-HEALS) on Health Behaviors Among Patients With Type 2 Diabetes: Qualitative Study.Journal of medical Internet research · 2026Trial
- Trial
- Accuracy of ChatGPT, Gemini, Claude and DeepSeek in Carbohydrate Counting.Diabetes, obesity & metabolism · 2026Article
- 5. Facilitating Positive Health Behaviors and Well-being to Improve Health Outcomes: Standards of Care in Diabetes-2026.Diabetes care · 2026Review
- AI powered dietary proportion assessment for improving accuracy and practicality of the balanced meal plate model.Scientific reports · 2025Article
- Artificial intelligence in primary ovarian insufficiency management: opportunities and challenges.Journal of assisted reproduction and genetics · 2025Review
- Article
- Investigation and Assessment of AI's Role in Nutrition-An Updated Narrative Review of the Evidence.Nutrients · 2025Review
- Navigating next-gen nutrition care using artificial intelligence-assisted dietary assessment tools-a scoping review of potential applications.Frontiers in nutrition · 2025Review
- 5. Facilitating Positive Health Behaviors and Well-being to Improve Health Outcomes: Standards of Care in Diabetes-2025.Diabetes care · 2025Review
- AI-enabled wearable cameras for assisting dietary assessment in African populations.NPJ digital medicine · 2024Article
- Advancements in Using AI for Dietary Assessment Based on Food Images: Scoping Review.Journal of medical Internet research · 2024Article
- Article
- Assessing Carbohydrate Counting Accuracy: Current Limitations and Future Directions.Nutrients · 2024Review
- 5. Facilitating Positive Health Behaviors and Well-being to Improve Health Outcomes: Standards of Care in Diabetes-2024.Diabetes care · 2024Review
- Artificial intelligence in diabetes management: Advancements, opportunities, and challenges.Cell reports. Medicine · 2023Review
- The Nutritional Content of Meal Images in Free-Living Conditions-Automatic Assessment with goFOODNutrients · 2023Article
- Multimedia Data-Based Mobile Applications for Dietary Assessment.Journal of diabetes science and technology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
GoCARB is a computer vision-based smartphone system designed for individuals with Type 1 Diabetes to estimate plated meals' carbohydrate (CHO) content. We aimed to compare the accuracy of GoCARB in estimating CHO with the estimations of six experienced dietitians. GoCARB was used to estimate the CHO content of 54 Central European plated meals, with each of them containing three different weighed food items. Ground truth was calculated using the USDA food composition database. Dietitians were asked to visually estimate the CHO content based on meal photographs. GoCARB and dietitians achieved comparable accuracies. The mean absolute error of the dietitians was 14.9 (SD 10.12) g of CHO versus 14.8 (SD 9.73) g of CHO for the GoCARB (
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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.