Image-based food monitoring and dietary management for patients living with diabetes: a scoping review of calorie counting applications
Bibliographic record
Abstract
Accurate dietary intake estimation is crucial for managing weight-related chronic diseases, such as diabetes, where precise measurement of food volume and caloric content is essential. Traditional calorie counting methods are often error-prone and may not meet the specific needs of individuals with diabetes. Recent advancements in computer science offer promising solutions through automated systems that estimate calorie intake from food images using deep learning techniques. These systems provide personalized dietary recommendations, helping individuals with diabetes make informed choices. As smartphones and wearable devices become more accessible, the utilization of electronic apps for dietary monitoring is increasing, highlighting the need for more research into safe, secure, and evidence-based IoT solutions. However, challenges such as standardization, validation across diverse populations, and data privacy concerns need to be addressed. This review focuses on the role of computer science in dietary intake estimation, specifically food segmentation, classification, and volume estimation for calorie calculation. By synthesizing existing literature, this review provides insights into current methods, key challenges, and potential future directions. The review also explores advancements in technology that can improve the accuracy of dietary assessments, contributing to personalized disease management and the prevention of weight-related chronic conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".