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使用基于人工智能成像的医院患者的摄入量估计:前性试点研究

Jiwon Ryu^1,2, Sejoong Kim^2,3,4, Yejee Lim^1,2

¹Hospital Medicine Center, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea.

JMIR formative research

|February 16, 2024

概括

基于人工智能 (AI) 的成像可以使用食物照片估计住院患者的摄入量. 这种人工智能方法显示出临床意义,可能消除了24小时尿液度测量的需要.

科学领域:

医疗成像医学成像
人工智能的人工智能
营养科学营养科学

背景情况:

对住院患者来说,准确测量摄入量至关重要.
传统的方法可能是繁和不太精确的.
基于人工智能 (AI) 的成像提供了一种新的饮食评估方法.

研究的目的:

评估人工智能驱动的饮食管理系统的适用性,以评估医院用餐中的含量.
为了确定基于人工智能的成像在估计患者摄入量的准确性,与既定方法相比.

主要方法:

一个混合AI模型,结合了You Only Look Once版本4 (YOLOv4) 和ResNet-101,分析了餐前和餐后拍摄的食物图像.
超光谱成像技术用于精确估计食物数量和量.
使用24小时尿 (UNa) 作为评估摄入量的参考标准.

主要成果:

人工智能算法 (AI-Na) 估计平均每天每人摄入量为202.27毫克.
尽管存在一些差异,但在AI-Na和24小时UNa之间发现了显著的相关性.
回归分析确定了影响AI-Na和24小时UNa关系的患者因素,从而产生了预测公式.

结论:

以人工智能为基础的成像证明了对医院患者估计摄入量的临床意义.
这种人工智能方法可以避免需要24小时尿液度测量.
人工智能衍生的摄入量的准确性可能会受到利尿剂使用等因素的影响.

关键词:

在这里,我们可以看到AIAIAI.人工智能图像人工智能图像年龄的年龄年龄的年龄.人工智能的人工智能是人工智能.饮食饮食饮食饮食饮食管理饮食管理电子健康电子健康人工智能食物人工智能食物医院医院医院医院图像转化为文本的方法影像成像技术影像成像技术试点研究试点研究在性爱中,性爱是性爱.聪明的营养智能营养摄入量摄入量尿液尿液尿液验证验证的时间

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