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相关概念视频

Quantifying Heat02:46

Quantifying Heat

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Thermal Energy Microscopically, thermal energy is the kinetic energy associated with the random motion of atoms and molecules. Temperature is a quantitative measure of “hot” or “cold”, which depends on the amount of thermal energy. When the atoms and molecules in an object are moving or vibrating quickly, they have a higher average kinetic energy (KE) (or higher thermal energy), and the object is perceived as “hot”, or it is described as being at a higher temperature. When the...
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Blood and Nerve Supply to the Bones01:29

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Bones are dynamic organs that require a rich supply of oxygen and nutrients. Around 5% to 10% of the cardiac output supplies blood to the bones. A typical long bone has three main sources: the nutrient artery, the metaphyseal and epiphyseal arteries, and the periosteal arteries.
Nutrient Artery
The nutrient artery is the main blood vessel that enters the diaphysis via the nutrient foramen. While most long bones have only one nutrient foramen, large bones, such as the femur, may have two. This...
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Pain serves as a critical warning signal that alerts the body to potential or actual harm. When mechanical pressure on the skin is intense, such as from a sharp pinch, the sensation transitions from touch to pain. Similarly, extreme temperatures, like a hot pot handle, convert the sensation of heat into pain. Pain can also result from overstimulation of other senses, such as blinding light, loud noise, or the intense heat from habañero peppers. This ability to sense pain is essential for...
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Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli
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在BioVid热痛数据库上的机器学习方法用于疼痛强度估计.

Melpo Pittara1, Andreas Anastasiou2,3, Konstantinos Andreou2,3

  • 1Computer Science, and Artificial Intelligence, Bernoulli Institute of Mathematics, University of Groningen, Groningen, Netherlands. melpopittara@gmail.com.

Scientific reports
|November 1, 2025
PubMed
概括

这项研究使用机器学习对面部表情进行了客观的疼痛评估. 卷积神经网络 (CNN) 在检测疼痛强度方面显示出最高的准确性,为非沟通患者提供了潜力.

关键词:
深度学习是一种深度学习.图像处理 图像处理机器学习是机器学习.疼痛评估 疼痛评估 疼痛评估变压器 变压器 变压器

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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
  • 疼痛管理研究 疼痛管理研究

背景情况:

  • 传统的疼痛量表 (NRS,VAS,VRS) 对某些患者群体来说是主观的,不可靠的.
  • 客观的疼痛评估对于准确的治疗和改善患者生活质量至关重要.
  • 机器学习为开发客观疼痛测量工具提供了一个有希望的途径.

研究的目的:

  • 通过机器学习技术引入和评估客观的疼痛测量模型.
  • 为了比较卷积神经网络 (CNN),VGG16,卷积视觉变换器 (CvT) 和MobileViT在从面部表情分类疼痛强度方面的表现.
  • 评估自动化疼痛评估在临床应用中的潜力,特别是对于非沟通患者.

主要方法:

  • 利用了来自BioVid热痛数据库的面部图像,捕捉了峰值疼痛和没有疼痛的时刻.
  • 训练和评估了四种机器学习模型:CNN,VGG16,CvT和MobileViT.
  • 基于对疼痛强度分类准确性的模型性能进行比较.

主要成果:

  • 在分类疼痛强度方面,CNN模型实现了最高的平均准确率 (0.71).
  • CvT模型表现出强的性能,平均准确率为0.69.
  • VGG16 (0.56) 和MobileViT50 (0.60) 的准确性明显较低,这表明从面部图像分类疼痛存在挑战.

结论:

  • 机器学习模型,特别是CNN,显示出客观和一致的疼痛评估的巨大潜力.
  • 自动化面部表情分析可以帮助评估非语言或认知障碍患者的疼痛.
  • 未来的研究应该集中在多式联运数据集成上,以提高疼痛检测系统的稳定性.