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Like all living organisms, plants require organic and inorganic nutrients to survive, reproduce, grow and maintain homeostasis. To identify nutrients that are essential for plant functioning, researchers have leveraged a technique called hydroponics. In hydroponic culture systems, plants are grown—without soil—in water-based solutions containing nutrients. At least 17 nutrients have been identified as essential elements required by plants. Plants acquire these elements from the...
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Assessing the gastrointestinal (GI) system is a complex process that begins with collecting subjective data. This data, collected through patient interviews, provides crucial insights into the patient's health history, perception patterns, and lifestyle habits, all contributing significantly to GI health.
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Updated: Jun 6, 2025

Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method
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测量食物和营养素摄入量的人工智能应用:范围审查

Jiakun Zheng1, Junjie Wang2, Jing Shen3

  • 1School of Economics and Management, Shanghai University of Sport, Shanghai, China.

Journal of medical Internet research
|November 28, 2024
PubMed
概括

人工智能 (AI) 为评估食物和营养摄入量提供了先进的方法,克服了传统方法的局限性. 人工智能工具在改善营养研究和疾病管理的准确性和实时监测方面表现有前途.

关键词:
在这里,我们可以看到AIAIAI.基于人工智能的AI.人工智能的人工智能是人工智能.计算机视觉 计算机视觉深度学习是一种深度学习.饮食 饮食 饮食 饮食饮食评估 饮食评估疾病管理 疾病管理食物 食物 食物 食物食物摄入量 食物摄入量机器学习是机器学习.测量过程中的测量.手机电话 手机电话手机电话自然语言处理自然语言处理.神经网络的神经网络的神经网络营养成分营养成分的营养.系统性的文学作品.

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Last Updated: Jun 6, 2025

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科学领域:

  • 营养科学 营养科学
  • 人工智能的人工智能
  • 医疗信息学 医疗信息学

背景情况:

  • 准确的食物和营养摄入量测量对于营养研究,监测和疾病管理至关重要.
  • 传统的方法 (例如,回忆,日记) 遭受回忆错误和社会可取性偏见.
  • 人工智能 (AI) 为自动化,客观和可扩展的饮食评估提供了机会.

研究的目的:

  • 在食品和营养素摄入量评估中对人工智能应用进行范围审查.
  • 综合关于人工智能驱动的饮食评估的有效性,准确性和挑战的证据.
  • 确定人工智能饮食评估工具当前的优势和改进领域.

主要方法:

  • 遵守PRISMA-ScR (系统性审查的首选报告项目和范围审查的元分析扩展) 准则.
  • 在PubMed,科学网络,科克兰图书馆和EBSCO进行全面的文献搜索,截至2023年6月30日.
  • 包括使用现代人工智能方法进行人类饮食摄入量评估的研究.

主要成果:

  • 25项研究 (2010-2023) 使用人工智能进行了各种输入:食物图像,可穿戴传感器数据 (声音,运动) 和文本.
  • 人工智能模型 (深度学习,机器学习) 在食品检测 (74-99.85%) 和营养估计 (10-15%的误差) 中取得了高精度.
  • 人工智能系统提供实时监控,提高精度和减少与自我报告方法相比召回偏差.

结论:

  • 人工智能显著提高了准确性,减少了劳动力,并使实时饮食监测成为可能.
  • 挑战包括适应多样化的食物,确保公平,解决数据隐私问题.
  • 人工智能对个人和人口层面的饮食评估具有变革性的潜力,支持精准营养和慢性疾病管理.