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著作: [田木 真和]/Tajiri Mari/[濵田 康弘]/[若田 好史]/[単 暁]/[尾崎 和美]/Kubota Masanori/Amano Sosuke/[阪上 浩]/[鈴木 佳子]/[廣瀬 隼]/Accuracy of an Artificial Intelligence-Based Model for Estimating Leftover Liquid Food in Hospitals: Validation Study./[JMIR Formative Research]

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EID
387992
EOID
1051106
Map
0
LastModified
2022年8月7日(日) 20:43:40
Operator
大家 隆弘
Avail
TRUE
Censor
0
Owner
田木 真和
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種別 必須 学術論文(審査論文)
言語 必須 英語
招待 推奨
審査 推奨
カテゴリ 推奨
共著種別 推奨
学究種別 推奨
組織 推奨
著者 必須
  1. 田木 真和([徳島大学.大学院医歯薬学研究部.医学域.医科学部門.内科系.医療情報学])
    役割 任意
    貢献度 任意
    学籍番号 推奨
  2. (英) Tajiri Mari
    役割 任意
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    学籍番号 推奨
  3. 濵田 康弘([徳島大学.大学院医歯薬学研究部.医学域.栄養科学部門.医科栄養学系.疾患治療栄養学])
    役割 任意
    貢献度 任意
    学籍番号 推奨
  4. 若田 好史
    役割 任意
    貢献度 任意
    学籍番号 推奨
  5. 単 暁([徳島大学.病院.中央診療施設等.病院情報センター])
    役割 任意
    貢献度 任意
    学籍番号 推奨
  6. 尾崎 和美([徳島大学.大学院医歯薬学研究部.歯学域.口腔科学部門.口腔保健学系.口腔保健支援学]/[徳島大学.歯学部.口腔保健学科.口腔保健支援学講座])
    役割 任意
    貢献度 任意
    学籍番号 推奨
  7. (英) Kubota Masanori
    役割 任意
    貢献度 任意
    学籍番号 推奨
  8. (英) Amano Sosuke
    役割 任意
    貢献度 任意
    学籍番号 推奨
  9. 阪上 浩([徳島大学.大学院医歯薬学研究部.医学域.栄養科学部門.医科栄養学系.代謝栄養学]/[徳島大学.糖尿病臨床・研究開発センター])
    役割 任意
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    学籍番号 推奨
  10. 鈴木 佳子
    役割 任意
    貢献度 任意
    学籍番号 推奨
  11. 廣瀬 隼([徳島大学.大学院医歯薬学研究部.医学域.医科学部門.内科系.医療情報学])
    役割 任意
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題名 必須

(英) Accuracy of an Artificial Intelligence-Based Model for Estimating Leftover Liquid Food in Hospitals: Validation Study.

副題 任意
要約 任意

(英) 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. The accuracy of estimating leftover liquid food in hospitals using an artificial intelligence (AI)-based model was compared to that of visual estimation. 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 (R) 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. 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 (R) for fermented milk and peach juice tended to be larger for the AI estimation than for the visual estimation, and the R value for the total was equal in terms of accuracy between the AI and visual estimations. The AI estimation approach achieved a smaller mean absolute error and root mean squared error and a larger coefficient of determination (R) 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 (R) 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.

キーワード 推奨
発行所 推奨
誌名 必須 JMIR Formative Research(JMIR Publications)
(eISSN: 2561-326X)
ISSN 任意 2561-326X
ISSN: 2561-326X (eISSN: 2561-326X)
Title: JMIR formative research
Title(ISO): JMIR Form Res
Publisher: JMIR Publications Inc.
 (NLM Catalog  (Scopus  (CrossRef (Scopus information is found. [need login])
必須 6
必須 5
必須 e35991 e35991
都市 任意
年月日 必須 2022年 5月 10日
URL 任意
DOI 任意 10.2196/35991    (→Scopusで検索)
PMID 任意 35536638    (→Scopusで検索)
CRID 任意
WOS 任意
Scopus 任意
評価値 任意
被引用数 任意
指導教員 推奨
備考 任意
  1. (英) Article.ELocationID: 10.2196/35991

  2. (英) Article.PublicationTypeList.PublicationType: Journal Article

  3. (英) KeywordList.Keyword: artificial intelligence

  4. (英) KeywordList.Keyword: convolutional neural network

  5. (英) KeywordList.Keyword: diet

  6. (英) KeywordList.Keyword: dietary intake

  7. (英) KeywordList.Keyword: food consumption

  8. (英) KeywordList.Keyword: food intake

  9. (英) KeywordList.Keyword: hospital

  10. (英) KeywordList.Keyword: liquid food

  11. (英) KeywordList.Keyword: machine learning

  12. (英) KeywordList.Keyword: malnourished

  13. (英) KeywordList.Keyword: malnourishment

  14. (英) KeywordList.Keyword: model

  15. (英) KeywordList.Keyword: neural network

  16. (英) KeywordList.Keyword: nutrition

  17. (英) KeywordList.Keyword: nutrition management

  18. (英) KeywordList.Keyword: patient