使用基于人工智能的模型来估计在临床环境中剩余的医院液体食物的食物摄入估计系统:开发和验证研究
Masato Tagi1, Yasuhiro Hamada2, Xiao Shan3
1Medical Informatics, Institute of Biomedical Sciences, Tokushima University Graduate School, Tokushima, Japan.
JMIR formative research
|November 5, 2024
概括
一个人工智能 (AI) 模型准确地估计了患者的液体食物摄入量,与实际摄入量有很好的相关性,并且优于基于图像的视觉估计. 这种人工智能系统对临床使用有希望,尽管直接视觉估计仍然更准确.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 营养科学 营养科学
背景情况:
- 准确评估患者的食物摄入量至关重要,但在临床环境中使用传统的视觉估计方法具有挑战性.
- 衡量饮食摄入量的现有方法往往不准确且劳动密集,需要更简单,更准确的替代方法.
研究的目的:
- 开发和验证基于人工智能 (AI) 的系统,用于估计住院患者的剩余液体食物摄入量.
- 将人工智能食物摄入量估计系统的准确性与视觉估计方法 (基于图像和直接) 和称重方法进行比较.
主要方法:
- 开发了一个AI模型,从图像中估计剩余的液体食物的能量含量.
- 人工智能的估计与饮食师的图像视觉估计和护士的直接视觉估计进行了比较,使用300个液体食品样本.
- 使用根-平方平均误差 (RMSE) 和确定系数 (R2),与斯皮尔曼等级相关性和对权重方法进行t测试进行验证.
主要成果:
- 人工智能估计方法显示,RMSE比图像视觉估计 (8.49 kcal) 小 (8.12 kcal),但比直接视觉估计 (4.34 kcal) 大.
- 人工智能估计显示,与实际值 (P=.82) 没有显著差异,与图像 (P<.001) 和直接视觉估计 (P=.007) 不同.
- 在AI估计值与能量,蛋白质,脂肪和碳水化合物的实际值之间观察到高相关性 (ρ=0.89-0.97).
结论:
- 基于人工智能的食物摄入量估计系统准确地估计了液体食物消耗,与称重方法高度相关,超过了图像视觉估计的准确性.
- 人工智能系统的错误在可以接受的范围内,这表明它在临床环境中对改善饮食摄入量测量的潜在适用性.
- 虽然有希望,但人工智能系统的准确性目前低于直接视觉估计,这表明需要进一步改进的领域.
相关概念视频
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In the one-compartment open model for intravenous (IV) bolus administration, clearance is estimated by dividing the elimination rate by the plasma drug concentration. This equation leverages the elimination rate constant and the apparent...
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