ArticleJMIR formative research2022
Accuracy of an Artificial Intelligence-Based Model for Estimating Leftover Liquid Food in Hospitals: Validation Study.
Article in JMIR formative research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 3 of them syntheses that pooled it.
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Who cites it
7 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Validity and accuracy of artificial intelligence-based dietary intake assessment methods: a systematic review.The British journal of nutrition · 2025Pooled it
- Artificial Intelligence in Malnutrition: A Systematic Literature Review.Advances in nutrition (Bethesda, Md.) · 2024Pooled it
- AI-based digital image dietary assessment methods compared to humans and ground truth: a systematic review.Annals of medicine · 2023Pooled it
- Applying a Robust Deep Learning Model to Assess the Remaining Amount of Foods With High Glycemic Index in Taiwanese Hospital Diabetic Meals: A Focus on Rice and Congee, With Milk as a Low-Glycemic Reference.Current developments in nutrition · 2025Article
- LeFood-set: Baseline performance of predicting level of leftovers food dataset in a hospital using MT learning.PloS one · 2025Article
- A Food Intake Estimation System Using an Artificial Intelligence-Based Model for Estimating Leftover Hospital Liquid Food in Clinical Environments: Development and Validation Study.JMIR formative research · 2024Article
- Health to Eat: A Smart Plate with Food Recognition, Classification, and Weight Measurement for Type-2 Diabetic Mellitus Patients' Nutrition Control.Sensors (Basel, Switzerland) · 2023Article
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11 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundAn accurate evaluation of the nutritional status of malnourished hospitalized patients at a higher risk of complications, such as frailty or disability, is crucial. Visual methods of estimating food intake are popular for evaluating the nutritional status in clinical environments. However, from the perspective of accurate measurement, such methods are unreliable.
objectiveThe accuracy of estimating leftover liquid food in hospitals using an artificial intelligence (AI)-based model was compared to that of visual estimation.
methodsThe accuracy of the AI-based model (AI estimation) was compared to that of the visual estimation method for thin rice gruel as staple food and fermented milk and peach juice as side dishes. A total of 576 images of liquid food (432 images of thin rice gruel, 72 of fermented milk, and 72 of peach juice) were used. The mean absolute error, root mean squared error, and coefficient of determination (R
resultsThe mean absolute errors obtained through the AI estimation approach were 0.63 for fermented milk, 0.25 for peach juice, and 0.85 for the total. These were significantly smaller than those obtained using the visual estimation approach, which were 1.40 (P<.001) for fermented milk, 0.90 (P<.001) for peach juice, and 1.03 (P=.009) for the total. By contrast, the mean absolute error for thin rice gruel obtained using the AI estimation method (0.99) did not differ significantly from that obtained using visual estimation (0.99). The confusion matrix for thin rice gruel showed variation in the distribution of errors, indicating that the errors in the AI estimation were biased toward the case of many leftovers. The mean squared error for all liquid foods tended to be smaller for the AI estimation than for the visual estimation. Additionally, the coefficient of determination (R
conclusionsThe AI estimation approach achieved a smaller mean absolute error and root mean squared error and a larger coefficient of determination (R
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