Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Classification of Bones01:18

Classification of Bones

14.3K
The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...
14.3K
Functional Classification of Joints01:09

Functional Classification of Joints

8.2K
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...
8.2K
Structural Classification of Joints01:20

Structural Classification of Joints

8.1K
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...
8.1K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Mechanistic insights into AMPK-SIRT3 positive feedback loop-mediated chondrocyte mitochondrial quality control in osteoarthritis pathogenesis.

Pharmacological research·2021
Same author

Cloning and Expression of Four Aquaporin Homologs from the Chinese Black Sleeper (Bostrychus sinensis): The Effects of Salinity Acclimation.

Biochemical genetics·2021
Same author

YTHDF1 Regulates Pulmonary Hypertension through Translational Control of MAGED1.

American journal of respiratory and critical care medicine·2021
Same author

[Progress of change in bone mineral density after knee arthroplasty].

Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery·2021
Same author

Amphiphilic PEGylated Lanthanide-Doped Upconversion Nanoparticles for Significantly Passive Accumulation in the Peritoneal Metastatic Carcinomatosis Models Following Intraperitoneal Administration.

ACS biomaterials science & engineering·2021
Same author

In vitro and 48 weeks in vivo performances of 3D printed porous Fe-30Mn biodegradable scaffolds.

Acta biomaterialia·2020

相关实验视频

Updated: May 8, 2026

Standardized Histomorphometric Evaluation of Osteoarthritis in a Surgical Mouse Model
07:32

Standardized Histomorphometric Evaluation of Osteoarthritis in a Surgical Mouse Model

Published on: May 6, 2020

12.0K

基于X射线图像的自动膝关节骨关节炎严重程度分级,使用层次分类方法.

Jian Pan1, Yuangang Wu2, Zhenchao Tang3,4,5

  • 1School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China.

Arthritis research & therapy
|November 19, 2024
PubMed
概括

一种新的层次分类方法有效地使用人工智能评估膝关节骨关节炎 (KOA) 的严重程度. 这种方法分析X射线图像来分类Kellgren-Lawrence (KL) 等级,为KOA评估提供了一个可行的工具.

关键词:
膝关节骨关节炎是一种关节炎.机器学习 机器学习这就是U-Net.图像X射线图像X射线图像X射线图像X射线图像

更多相关视频

Software-Assisted Quantitative Measurement of Osteoarthritic Subchondral Bone Thickness
08:52

Software-Assisted Quantitative Measurement of Osteoarthritic Subchondral Bone Thickness

Published on: March 18, 2022

2.9K
Author Spotlight: Investigating the Mechanism of Action of Acupotomy in Treating Knee Osteoarthritis
08:40

Author Spotlight: Investigating the Mechanism of Action of Acupotomy in Treating Knee Osteoarthritis

Published on: October 20, 2023

1.1K

相关实验视频

Last Updated: May 8, 2026

Standardized Histomorphometric Evaluation of Osteoarthritis in a Surgical Mouse Model
07:32

Standardized Histomorphometric Evaluation of Osteoarthritis in a Surgical Mouse Model

Published on: May 6, 2020

12.0K
Software-Assisted Quantitative Measurement of Osteoarthritic Subchondral Bone Thickness
08:52

Software-Assisted Quantitative Measurement of Osteoarthritic Subchondral Bone Thickness

Published on: March 18, 2022

2.9K
Author Spotlight: Investigating the Mechanism of Action of Acupotomy in Treating Knee Osteoarthritis
08:40

Author Spotlight: Investigating the Mechanism of Action of Acupotomy in Treating Knee Osteoarthritis

Published on: October 20, 2023

1.1K

科学领域:

  • 医学成像分析 医学成像分析
  • 医疗保健中的人工智能
  • 骨关节炎研究 骨关节炎研究

背景情况:

  • 膝关节骨关节炎 (KOA) 诊断和严重程度评估对于患者管理至关重要.
  • 目前的KOA严重程度分级方法可能是主观的,耗时的.
  • 需要对KOA严重性的自动评估,以提高效率和一致性.

研究的目的:

  • 开发和验证用于自动化KOA严重性评估的等级分类方法.
  • 利用深度学习模型从膝盖X射线图像中提取特征.
  • 评估开发的方法在分类凯尔格伦-劳伦斯 (KL) 等级的性能.

主要方法:

  • 对4074名患者进行了一项回顾性研究.
  • 为KL分级开发了一种具有四个子任务的等级分类方法.
  • 使用U-Net模型对关节空间和骨质细胞进行细分.
  • 为KL分级模型提取和结合了几何和放射性特征.

主要成果:

  • 对于关节空间 (DSC:0.860.88) 和骨质细胞 (DSC:0.64),U-Net模型实现了高细分精度.
  • 组合模型在KL分级方面表现出卓越的性能,具体分类的准确率高达98.50%.
  • 对测试队列的等级分类方法的整体准确性为65.98%.

结论:

  • 开发的层次分类方法为自动化KOA严重性评估提供了一种可行的方法.
  • 这种人工智能驱动的方法显示了提高KOA评级客观性和效率的潜力.
  • 进一步验证和完善该方法可以提高其临床适用性.