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Correlations02:20

Correlations

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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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Correlation and Causation01:27

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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
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Correlation01:09

Correlation

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In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
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Machines01:19

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
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Machines: Problem Solving II01:30

Machines: Problem Solving II

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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Machines: Problem Solving I01:22

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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
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眼表面特征与颈动脉斑块的相关性分析：一种多模态机器学习框架

Shichen Zhang1, Dinghan Hu1, Le Luo2

  • 1Machine Learning and I-health International Cooperation Base of Zhejiang Province, Hangzhou Dianzi University, 310018, China; School of Automation, Hangzhou Dianzi University, Zhejiang, 310018, China.

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まとめ

眼表面成像提供了一种非侵入性的颈动脉斑块检测方法，颈动脉斑块是心血管疾病的关键指标。本研究发现眼部图像特征与斑块存在之间存在很强的关联性，有助于早期疾病筛查。

キーワード:
颈动脉斑块评估相关性分析机器学习眼表面图像

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科学分野:

  • 眼科学与心血管健康
  • 医学影像与诊断
  • 生物医学工程

背景:

  • 颈动脉斑块的诊断对于识别心血管和脑血管疾病至关重要。
  • 目前的诊断方法，如颈动脉超声检查，耗时、具有辐射性、昂贵，并且限制了疾病进展的追踪。
  • 需要易于获取的非侵入性方法来筛查和监测颈动脉斑块。

研究 の 目的:

  • 研究颈动脉斑块与眼表面图像特征之间的关联性。
  • 开发一种使用眼部成像进行颈动脉斑块非侵入性筛查的方法。
  • 探索眼表面图像分析在心血管健康评估中的潜力。

主な方法:

  • 对眼表面图像进行多维度特征分析，包括纹理、频域和颜色特征。
  • 特征选择、置信度评估和分布特性研究，以建立稳健的关联性。
  • 机器学习分类器和亚组验证（年龄、性别）以评估特征的稳健性和预测性能。

主要な成果:

  • 在8875名个体队列中实现了高预测精度。
  • 电子健康记录（EHR）特征与颈动脉斑块的关联性最强（男性ORs：4.35，女性ORs：2.92）。
  • 眼表面图像特征（EHR、LBP、GLGCM、GLCM）、年龄和男性性别与颈动脉斑块密切相关。

結論:

  • 眼表面图像分析为颈动脉斑块筛查提供了一种实用且非侵入性的方法。
  • 已识别的特征关联和预测性能支持临床应用，特别是大规模人群筛查。
  • 该方法有潜力补充现有的心血管风险评估诊断工具。