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A Food Intake Estimation System Using an Artificial Intelligence–Based Model for Estimating Leftover Hospital Liquid Food in Clinical Environments : Development and Validation Study
https://tokushima-u.repo.nii.ac.jp/records/2012394
https://tokushima-u.repo.nii.ac.jp/records/2012394b39609bb-0b2a-4334-9898-0dfe73f90d1a
| 名前 / ファイル | ライセンス | アクション |
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jmirfr_8_e55218.pdf (1.6 MB)
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| Item type | 文献 / Documents(1) | |||||||||||||||||||||||||||||||
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| 公開日 | 2025-01-31 | |||||||||||||||||||||||||||||||
| アクセス権 | ||||||||||||||||||||||||||||||||
| アクセス権 | open access | |||||||||||||||||||||||||||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||||||||||||||||||||||||||
| 資源タイプ | ||||||||||||||||||||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||||||||||||||||||||
| 資源タイプ | journal article | |||||||||||||||||||||||||||||||
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| 関連識別子 | https://doi.org/10.2196/55218 | |||||||||||||||||||||||||||||||
| 関連名称 | 10.2196/55218 | |||||||||||||||||||||||||||||||
| 出版タイプ | ||||||||||||||||||||||||||||||||
| 出版タイプ | VoR | |||||||||||||||||||||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||||||||||||||||||||
| タイトル | ||||||||||||||||||||||||||||||||
| タイトル | A Food Intake Estimation System Using an Artificial Intelligence–Based Model for Estimating Leftover Hospital Liquid Food in Clinical Environments : Development and Validation Study | |||||||||||||||||||||||||||||||
| 著者 |
田木, 真和
× 田木, 真和× 濵田, 康弘
WEKO
631
× 単, 暁× 尾崎, 和美
WEKO
1705
× Kubota, Masanori
× Amano, Sosuke
× 阪上, 浩
WEKO
365
× Suzuki, Yoshiko
× Konishi, Takeshi
× 廣瀬, 隼 |
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| 抄録 | ||||||||||||||||||||||||||||||||
| 内容記述 | Background: Medical staff often conduct assessments, such as food intake and nutrient sufficiency ratios, to accurately evaluate patients’ food consumption. However, visual estimations to measure food intake are difficult to perform with numerous patients. Hence, the clinical environment requires a simple and accurate method to measure dietary intake. Objective: This study aims to develop a food intake estimation system through an artificial intelligence (AI) model to estimate leftover food. The accuracy of the AI’s estimation was compared with that of visual estimation for liquid foods served to hospitalized patients. Methods: The estimations were evaluated by a dietitian who looked at the food photo (image visual estimation) and visual measurement evaluation was carried out by a nurse who looked directly at the food (direct visual estimation) based on actual measurements. In total, 300 dishes of liquid food (100 dishes of thin rice gruel, 100 of vegetable soup, 31 of fermented milk, and 18, 12, 13, and 26 of peach, grape, orange, and mixed juices, respectively) were used. The root-mean-square error (RMSE) and coefficient of determination (R2) were used as metrics to determine the accuracy of the evaluation process. Corresponding t tests and Spearman rank correlation coefficients were used to verify the accuracy of the measurements by each estimation method with the weighing method. Results: The RMSE obtained by the AI estimation approach was 8.12 for energy. This tended to be smaller and larger than that obtained by the image visual estimation approach (8.49) and direct visual estimation approach (4.34), respectively. In addition, the R2 value for the AI estimation tended to be larger and smaller than the image and direct visual estimations, respectively. There was no difference between the AI estimation (mean 71.7, SD 23.9 kcal, P=.82) and actual values with the weighing method. However, the mean nutrient intake from the image visual estimation (mean 75.5, SD 23.2 kcal, P<.001) and direct visual estimation (mean 73.1, SD 26.4 kcal, P=.007) were significantly different from the actual values. Spearman rank correlation coefficients were high for energy (ρ=0.89-0.97), protein (ρ=0.94-0.97), fat (ρ=0.91-0.94), and carbohydrate (ρ=0.89-0.97). Conclusions: The measurement from the food intake estimation system by an AI-based model to estimate leftover liquid food intake in patients showed a high correlation with the actual values with the weighing method. Furthermore, it also showed a higher accuracy than the image visual estimation. The errors of the AI estimation method were within the acceptable range of the weighing method, which indicated that the AI-based food intake estimation system could be applied in clinical environments. However, its lower accuracy than that of direct visual estimation was still an issue. |
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| キーワード | ||||||||||||||||||||||||||||||||
| 主題 | artificial intelligence | |||||||||||||||||||||||||||||||
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| 主題 | machine learning | |||||||||||||||||||||||||||||||
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| 主題 | system development | |||||||||||||||||||||||||||||||
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| 主題 | food intake | |||||||||||||||||||||||||||||||
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| 主題 | dietary intake | |||||||||||||||||||||||||||||||
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| 主題 | dietary assessment | |||||||||||||||||||||||||||||||
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| 主題 | food consumption | |||||||||||||||||||||||||||||||
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| 主題 | image visual estimation | |||||||||||||||||||||||||||||||
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| 主題 | AI estimation | |||||||||||||||||||||||||||||||
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| 主題 | direct visual estimation | |||||||||||||||||||||||||||||||
| 書誌情報 |
en : JMIR Formative Research 巻 8, p. e55218, 発行日 2024-11-05 |
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| 収録物識別子タイプ | EISSN | |||||||||||||||||||||||||||||||
| 収録物識別子 | 2561326X | |||||||||||||||||||||||||||||||
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| 出版者 | JMIR Publications | |||||||||||||||||||||||||||||||
| 権利情報 | ||||||||||||||||||||||||||||||||
| 権利情報 | 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. | |||||||||||||||||||||||||||||||
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| 識別子 | 415814 | |||||||||||||||||||||||||||||||
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| 言語 | eng | |||||||||||||||||||||||||||||||
