ReviewNutrients2020
Current Developments in Digital Quantitative Volume Estimation for the Optimisation of Dietary Assessment.
Review in Nutrients, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 citations in OpenAlex.
- Technology to Automatically Record Eating Behavior in Real Life: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Global burden of disease changes related to high red meat diets and breast cancer from 1990 to 2021 and its prediction up to 2030.Frontiers in nutrition · 2025Article
- Validity of a Web-Based 24-Hour Dietary Recall of Energy and Nutrient Intakes in Japanese Adults.Nutrients · 2024Article
- Advancements in Using AI for Dietary Assessment Based on Food Images: Scoping Review.Journal of medical Internet research · 2024Article
- Image-Based Volume Estimation for Food in a Bowl.Journal of food engineering · 2024Article
- Automated Artificial Intelligence-Based Thai Food Dietary Assessment System: Development and Validation.Current developments in nutrition · 2024Article
- mid-DeepLabv3+: A Novel Approach for Image Semantic Segmentation Applied to African Food Dietary Assessments.Sensors (Basel, Switzerland) · 2023Article
- Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis.Foods (Basel, Switzerland) · 2023Review
- Review
- Digital Behavior Change Interventions for the Prevention and Management of Type 2 Diabetes: Systematic Market Analysis.Journal of medical Internet research · 2022Review
- Early Antibiotic Exposure Is Not Detrimental to Therapeutic Effect from Immunotherapy in Hepatocellular Carcinoma.Liver cancer · 2021Article
- Potential of existing online 24-h dietary recall tools for national dietary surveys.Public health nutrition · 2021Article
- A Dietary Assessment Training Course Path: The Italian IV SCAI Study on Children Food Consumption.Frontiers in public health · 2021Article
- Feasibility Study of an Automated Carbohydrate Estimation System Using Thai Food Images in Comparison With Estimation by Dietitians.Frontiers in nutrition · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Obesity is a global health problem with wide-reaching economic and social implications. Nutrition surveillance systems are essential to understanding and addressing poor dietary practices. However, diets are incredibly diverse across populations and an accurate diagnosis of individualized nutritional issues is challenging. Current tools used in dietary assessment are cumbersome for users, and are only able to provide approximations of dietary information. Given the need for technological innovation, this paper reviews various novel digital methods for food volume estimation and explores the potential for adopting such technology in the Southeast Asian context. We discuss the current approaches to dietary assessment, as well as the potential opportunities that digital health can offer to the field. Recent advances in optics, computer vision and deep learning show promise in advancing the field of quantitative dietary assessment. The ease of access to the internet and the availability of smartphones with integrated cameras have expanded the toolsets available, and there is potential for automated food volume estimation to be developed and integrated as part of a digital dietary assessment tool. Such a tool may enable public health institutions to be able to gather an effective nutritional insight and combat the rising rates of obesity in the region.
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What OpenQuestion holds
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.