Enhancing Food Intake Tracking in Long-term Care With Automated Food Imaging and Nutrient Intake Tracking (AFINI-T) Technology: Validation and Feasibility Assessment
Bibliographic record
Abstract
Background Half of long-term care (LTC) residents are malnourished, leading to increased hospitalization, mortality, and morbidity, with low quality of life. Current tracking methods are subjective and time-consuming. Objective This paper presented the automated food imaging and nutrient intake tracking technology designed for LTC. Methods A needs assessment was conducted with 21 participating staff across 12 LTC and retirement homes. We created 2 simulated LTC intake data sets comprising modified (664/1039, 63.91% plates) and regular (375/1039, 36.09% plates) texture foods. Overhead red-green-blue-depth images of plated foods were acquired, and foods were segmented using a pretrained food segmentation network. We trained a novel convolutional autoencoder food feature extractor network using an augmented UNIMIB2016 food data set. A meal-specific food classifier was appended to the feature extractor and tested on our simulated LTC food intake data sets. Food intake (percentage) was estimated as the differential volume between classified full portion and leftover plates. Results The needs assessment yielded 13 nutrients of interest, requirement for objectivity and repeatability, and account for real-world environmental constraints. For 12 meal scenarios with up to 15 classes each, the top-1 classification accuracy was 88.9%, with mean intake error of −0.4 (SD 36.7) mL. Nutrient intake estimation by volume was strongly linearly correlated with nutrient estimates from mass (r2=0.92-0.99), with good agreement between methods (σ=−2.7 to −0.01; 0 within each of the limits of agreement). Conclusions The automated food imaging and nutrient intake tracking approach is a deep learning–powered computational nutrient sensing system that appears to be feasible (validated accuracy against gold-standard weighed food method, positive end user engagement) and may provide a novel means for more accurate and objective tracking of LTC residents’ food intake to support and prevent malnutrition tracking strategies.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".