ReviewHealthcare (Basel, Switzerland)2022
Mobile Computer Vision-Based Applications for Food Recognition and Volume and Calorific Estimation: A Systematic Review.
Review in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis 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
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 46 citations in OpenAlex.
- Empowering Diabetics: Advancements in Smartphone-Based Food Classification, Volume Measurement, and Nutritional Estimation.Sensors (Basel, Switzerland) · 2024Pooled it
- Automatic Image Recognition Meal Reporting Among Young Adults: Randomized Controlled Trial.JMIR mHealth and uHealth · 2025Trial
- Development and Validation of a Mobile Application for Real-Time Chinese Dish Classification and Nutrient Estimation.Journal of food science · 2026Article
- VolE: A point-cloud framework for food 3D reconstruction and volume estimation.Scientific reports · 2026Article
- Data in Personalized Nutrition: Bridging Biomedical, Psycho-behavioral, and Food Environment Approaches for Population-wide Impact.Advances in nutrition (Bethesda, Md.) · 2025Review
- Retrieval augmented generation for large language models in healthcare: A systematic review.PLOS digital health · 2025Article
- AI-driven tools for the prediction of obesity-related vascular diseases: stakeholder perspectives and challenges.Frontiers in public health · 2025Article
- Innovative food supply chain through spatial computing technologies: A review.Comprehensive reviews in food science and food safety · 2024Review
- Dining Bowl Modeling and Optimization for Single-Image-Based Dietary Assessment.Sensors (Basel, Switzerland) · 2024Article
- Image-Based Volume Estimation for Food in a Bowl.Journal of food engineering · 2024Article
- Decoding Heterogeneity in Data-Driven Self-Monitoring Adherence Trajectories in Digital Lifestyle Interventions for Weight Loss: A Qualitative Study.Research square · 2024Article
- Article
- Estimating Amount of Food in a Circular Dining Bowl from a Single Image.MADiMa '23 : proceedings of the 8th International Workshop on Multimedia Assisted Dietary Management · 2023Article
- The Nutritional Content of Meal Images in Free-Living Conditions-Automatic Assessment with goFOODNutrients · 2023Article
- Assessing daily energy intake in adult women: validity of a food-recognition mobile application compared to doubly labelled water.Frontiers in nutrition · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The growing awareness of the influence of "what we eat" on lifestyle and health has led to an increase in the use of embedded food analysis and recognition systems. These solutions aim to effectively monitor daily food consumption, and therefore provide dietary recommendations to enable and support lifestyle changes. Mobile applications, due to their high accessibility, are ideal for real-life food recognition, volume estimation and calorific estimation. In this study, we conducted a systematic review based on articles that proposed mobile computer vision-based solutions for food recognition, volume estimation and calorific estimation. In addition, we assessed the extent to which these applications provide explanations to aid the users to understand the related classification and/or predictions. Our results show that 90.9% of applications do not distinguish between food and non-food. Similarly, only one study that proposed a mobile computer vision-based application for dietary intake attempted to provide explanations of features that contribute towards classification. Mobile computer vision-based applications are attracting a lot of interest in healthcare. They have the potential to assist in the management of chronic illnesses such as diabetes, ensuring that patients eat healthily and reducing complications associated with unhealthy food. However, to improve trust, mobile computer vision-based applications in healthcare should provide explanations of how they derive their classifications or volume and calorific estimations.
Indexed as
Identifiers
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.