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相关概念视频

Functional Classification of Joints01:09

Functional Classification of Joints

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Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
4.1K
Structural Classification of Joints01:20

Structural Classification of Joints

3.5K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
3.5K

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相关实验视频

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Labeling of Extracellular Vesicles for Monitoring Migration and Uptake in Cartilage Explants
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基于振动关节学的膝盖病变位置通过多标签嵌入学习.

Tongjie Pan, Yangwuyong Zhang, Qiaosen Dong

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
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    概括

    这项研究引入了一种使用振动关节学 (VAG) 信号精确定位膝盖病变的新方法. 该方法有效地解决了数据不平衡,并提取了复杂的损伤相关性,以提高诊断准确度.

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    Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
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    科学领域:

    • 生物医学工程 生物医学工程
    • 医疗成像医学成像
    • 机器学习 机器学习

    背景情况:

    • 振动关节学 (VAG) 是一种非侵入性,无辐射的技术,用于膝盖病理的识别.
    • 目前的研究主要集中在膝盖健康状况上,对VAG进行有限的探索,以精确地定位病变.

    研究的目的:

    • 开发一种有效的方法,使用VAG信号同时定位多个膝盖病变.
    • 克服现有的多标签分类 (MLC) 方法在处理标签不平衡和稀疏相关性,以识别膝盖损伤的局限性.

    主要方法:

    • 提出了一种新的预训练模型,其中包含一个标签自编码器 (PTM-LAE).
    • 使用预训练的焦点损失特征映射模型来解决正负标签不平衡.
    • 采用基于因子化机器的神经网络 (DeepFM) 来提取不同病变之间的稀疏标签相关性.

    主要成果:

    • 与最先进的方法相比,PTM-LAE模型在膝盖损伤局部化方面表现出更高的性能.
    • 有效地缓解了正负标签不平衡的问题,并改进了稀疏标签相关性的提取.
    • 收集的VAG数据的实验验证证证了该模型的有效性.

    结论:

    • 拟议的PTM-LAE方法在利用VAG信号来准确地定位膝盖损伤方面取得了重大进展.
    • 这种方法有可能极大地帮助医生进行诊断和患者监测.
    • 该研究强调了在医学图像分析中解决标签失衡和相关性提取的重要性.