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相关概念视频

Functional Classification of Joints01:09

Functional Classification of Joints

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 immobile...
Classification of Skeletal Muscle Relaxants01:28

Classification of Skeletal Muscle Relaxants

Skeletal muscle relaxants are a group of drugs that can reduce muscle stiffness and induce temporary paralysis to relieve pain. These agents can act centrally to reduce muscle tone or spasms in painful conditions such as multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), or spinal injuries; they are called antispasmodics or spasmolytics.
Peripherally acting skeletal muscle relaxants interfere with the neurotransmission at the neuromuscular end plate to induce paralysis during...
Muscles that Move the Head01:19

Muscles that Move the Head

The muscles that move the head are a dynamic and complex group of structures that work together to facilitate a wide range of head movements, including rotation, flexion, extension, and lateral bending.
The bilateral sternocleidomastoid, or SCM, and the suprahyoid and infrahyoid muscles are significant head flexors. The SCM muscles originate at the sternum and clavicle and attach to the mastoid process of the temporal bone. The SCM contracts bilaterally to bend the head forward, whereas...

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相关实验视频

Updated: May 7, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

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机器学习模型用于使用骨姿势和运动来分类非特异性部疼痛.

Ui-Jae Hwang1, Oh-Yun Kwon2, Jun-Hee Kim1

  • 1Department of Physical Therapy, College of Health Science, Laboratory of KEMA AI Research (KAIR), Yonsei University, Wonju, 26426, Republic of Korea.

Musculoskeletal science & practice
|March 25, 2024
PubMed
概括

机器学习模型准确地对办公室工作者的非特异性部疼痛 (NSNP) 进行分类,使用运动期间的宫动力学. 这种方法在预测NSNP方面优于椎骨姿势评估.

关键词:
宫延伸 宫延伸 宫延伸宫收缩 宫收缩头骨的姿势 头骨的姿势机器学习是机器学习.

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相关实验视频

Last Updated: May 7, 2026

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科学领域:

  • 生物力学和物理治疗.
  • 医疗保健中的机器学习
  • 职业健康 职业健康 职业健康

背景情况:

  • 非特异性部疼痛 (NSNP) 在办公室工作者中很普遍.
  • 椎姿势 (CCP) 和宫动力学是常用的查方法.
  • 对于NSNP分类的机器学习 (ML) 模型的预测性能需要进一步研究.

研究的目的:

  • 为了比较ML模型的预测性能,用于分类具有和没有NSNP的个体.
  • 评估数据集,包括延伸和收缩 (CKdPR) 期间的CCP和宫动力学.

主要方法:

  • 这是一项涉及773名公共服务办公室工作人员 (PSOW) 的探索性,横截面研究.
  • 创建了五个数据集:CCP,延伸期间的宫动力学,收缩期间的宫动力学,CKdPR,以及CCP和CKdPR的组合.
  • 四个ML算法 (随机森林,后勤回归,极端梯度提升,支持矢量机) 被训练并使用AUC,准确性,精度,回忆和F1得分进行评估.

主要成果:

  • 使用CKdPR数据集的随机森林模型实现了最高的AUC (0.892) 和F1得分 (0.832) 来分类NSNP.
  • 使用CCP数据集的随机森林模型显示出最低的性能 (AUC,0.738;F1,0.715).

结论:

  • 与经典的统计方法相比,ML算法在分类NSNP方面表现优越.
  • 在延伸和收缩 (CKdPR) 期间的宫动力学数据集为NSNP预测提供了比CCP数据集更好的ML模型性能.