基于人工智能模型的食物摄入量估算系统,用于估算临床环境中医院剩余流质食物的摄入量:开发与验证研究。

IF 2 Q3 HEALTH CARE SCIENCES & SERVICES
Masato Tagi, Yasuhiro Hamada, Xiao Shan, Kazumi Ozaki, Masanori Kubota, Sosuke Amano, Hiroshi Sakaue, Yoshiko Suzuki, Takeshi Konishi, Jun Hirose
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引用次数: 0

摘要

背景:医务人员经常进行食物摄入量和营养素充足率等评估,以准确评价患者的食物消耗量。然而,目测食物摄入量的方法很难在众多患者中使用。因此,临床环境需要一种简单而准确的方法来测量饮食摄入量:本研究旨在开发一种食物摄入量估算系统,通过人工智能(AI)模型估算剩余食物。人工智能估算的准确性与目测估算的准确性进行了比较:估算由营养师通过观察食物照片进行评估(图像视觉估算),而视觉测量评估则由护士根据实际测量结果直接观察食物进行评估(直接视觉估算)。总共使用了 300 盘流质食物(稀饭 100 盘、蔬菜汤 100 盘、发酵乳 31 盘,以及桃汁、葡萄汁、橙汁和混合果汁分别为 18 盘、12 盘、13 盘和 26 盘)。均方根误差(RMSE)和判定系数(R2)被用来衡量评价过程的准确性。采用相应的 t 检验和斯皮尔曼等级相关系数来验证每种估算方法与称重方法测量结果的准确性:结果:人工智能估算方法得到的能量均方根误差为 8.12。与图像目测估算法(8.49)和直接目测估算法(4.34)相比,其均方误差分别趋于较小和较大。此外,人工智能估算的 R2 值分别比图像估算和直接视觉估算的 R2 值大和小。称重法估算的 AI 值(平均 71.7 千卡,标差 23.9 千卡,P=.82)与实际值之间没有差异。然而,图像直观估算的平均营养摄入量(平均 75.5 千卡,标差 23.2 千卡,P=.82)与称重法的实际值没有差异:通过基于人工智能模型的食物摄入量估算系统估算出的患者剩余流质食物摄入量与称重法得出的实际值具有很高的相关性。此外,其准确性也高于图像视觉估算法。人工智能估算方法的误差在称重法的可接受范围内,这表明基于人工智能的食物摄入量估算系统可应用于临床环境。然而,与直接目测法相比,其准确性较低仍是一个问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Food Intake Estimation System Using an Artificial Intelligence-Based Model for Estimating Leftover Hospital Liquid Food in Clinical Environments: Development and Validation Study.

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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来源期刊
JMIR Formative Research
JMIR Formative Research Medicine-Medicine (miscellaneous)
CiteScore
2.70
自引率
9.10%
发文量
579
审稿时长
12 weeks
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