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ID 117555
Title Alternative
人工知能を用いた病院流動食の残量推定の精度 : 検証研究
Author
Tajiri, Mari Tokushima University
Wakata, Yoshifumi National Hospital Organization Kyushu Medical Center|Tokushima University Tokushima University Educator and Researcher Directory
Kubota, Masanori foo.log
Amano, Sosuke foo.log
Suzuki, Yoshiko Tokushima University
Keywords
artificial intelligence
convolutional neural network
food intake
liquid food
nutrition management
neural network
machine learning
malnourished
malnourishment
model
hospital
patient
nutrition
food consumption
dietary intake
diet
Content Type
Thesis or Dissertation
Description
Background: An 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.
Objective: The accuracy of estimating leftover liquid food in hospitals using an artificial intelligence (AI)–based model was compared to that of visual estimation.
Methods: The 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 (R2) were used as metrics for determining the accuracy of the evaluation process. Welch t test and the confusion matrix were used to examine the difference of mean absolute error between AI and visual estimation.
Results: The 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 (R2) for fermented milk and peach juice tended to be larger for the AI estimation than for the visual estimation, and the R2 value for the total was equal in terms of accuracy between the AI and visual estimations.
Conclusions: The AI estimation approach achieved a smaller mean absolute error and root mean squared error and a larger coefficient of determination (R2) than the visual estimation approach for the side dishes. Additionally, the AI estimation approach achieved a smaller mean absolute error and root mean squared error compared to the visual estimation method, and the coefficient of determination (R2) was similar to that of the visual estimation method for the total. AI estimation measures liquid food intake in hospitals more precisely than visual estimation, but its accuracy in estimating staple food leftovers requires improvement.
Journal Title
JMIR Formative Research
ISSN
2561326X
Publisher
JMIR Publications
Volume
6
Issue
5
Start Page
e35991
Published Date
2022-05-10
Remark
内容要旨・審査要旨・論文本文の公開
本論文は,著者Masato Tagiの学位論文として提出され,学位審査・授与の対象となっている。
Rights
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.
EDB ID
DOI (Published Version)
URL ( Publisher's Version )
FullText File
language
eng
TextVersion
ETD
MEXT report number
甲第3690号
Diploma Number
甲医第1555号
Granted Date
2023-03-23
Degree Name
Doctor of Medical Science
Grantor
Tokushima University
departments
Medical Sciences
University Hospital
Oral Sciences