ReviewJournal of diabetes science and technology2023
Multimedia Data-Based Mobile Applications for Dietary Assessment.
Review in Journal of diabetes science and technology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed.
- Chinese Food Images for Full-cycle Nutrition Analysis Towards Diabetes Management.Scientific data · 2026Article
- Designing a Carbohydrate Counting App for Young Adults With Type 1 Diabetes: Usability Testing Interview Study.Journal of medical Internet research · 2026Article
- A comparative study of vision-language models for food ingredient recognition and nutrient estimation.Current research in food science · 2026Article
- Artificial Intelligence in Nutrition and Dietetics: A Comprehensive Review of Current Research.Healthcare (Basel, Switzerland) · 2025Review
- Food Image Recognition Based on Anti-Noise Learning and Covariance Feature Enhancement.Foods (Basel, Switzerland) · 2025Article
- Bridging the Gap in Carbohydrate Counting With a Mobile App: Needs Assessment Survey.Journal of medical Internet research · 2025Article
- A Lightweight Hybrid Model with Location-Preserving ViT for Efficient Food Recognition.Nutrients · 2024Article
- The Nutritional Content of Meal Images in Free-Living Conditions-Automatic Assessment with goFOODNutrients · 2023Article
- Diabetes Technology Meeting 2022.Journal of diabetes science and technology · 2023Article
- Home-based cooking intervention with a smartphone app to improve eating behaviors in children aged 7-9 years: a feasibility study.Discover social science and health · 2023Article
- A feasibility study to assess Mediterranean Diet adherence using an AI-powered system.Scientific reports · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetes mellitus (DM) and obesity are chronic medical conditions associated with significant morbidity and mortality. Accurate macronutrient and energy estimation could be beneficial in attempts to manage DM and obesity, leading to improved glycemic control and weight reduction, respectively. Existing dietary assessment methods are subject to major errors in measurement, are time consuming, are costly, and do not provide real-time feedback. The increasing adoption of smartphones and artificial intelligence, along with the advances in algorithms and hardware, allowed the development of technologies executed in smartphones that use food/beverage multimedia data as an input, and output information about the nutrient content in almost real time. Scope of this review was to explore the various image-based and video-based systems designed for dietary assessment. We identified 22 different systems and divided these into three categories on the basis of their setting for evaluation: laboratory (12), preclinical (7), and clinical (3). The major findings of the review are that there is still a number of open research questions and technical challenges to be addressed and end users-including health care professionals and patients-need to be involved in the design and development of such innovative solutions. Last, there is a clear need that these systems should be validated under unconstrained real-life conditions and that they should be compared with conventional methods for dietary assessment.
Indexed as
Identifiers
What Socratic 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 Socratic graph.