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

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...

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

CMRxRecon2024: A Multimodality, Multiview k-Space Dataset Boosting Universal Machine Learning for Accelerated Cardiac MRI.

Radiology. Artificial intelligence·2025
Same author

Variabilities of two Drechslerella dactyloides isolates in Korea and high predacity against Bursaphelenchus xylophilus.

Current microbiology·2010
Same author

Molecularly tuned peptide assemblies at the liquid-solid interface studied by scanning tunneling microscopy.

Physical chemistry chemical physics : PCCP·2010
Same author

[Resistance mutation patterns of hepatitis B virus in patients with suboptimal response to adefovir dipivoxil therapy after lamivudine resistance].

Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology·2010
Same author

[Application of videomediastinoscopy in positive PET finding for mediastinal lymph node of lung cancer].

Zhongguo fei ai za zhi = Chinese journal of lung cancer·2010
Same author

Preliminary effect of proximal femoral nail antirotation on emergency treatment of senile patients with intertrochanteric fracture.

Chinese journal of traumatology = Zhonghua chuang shang za zhi·2010

相关实验视频

Updated: Jun 16, 2026

Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease
04:44

Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease

Published on: June 16, 2020

19.9K

根据可解释的特征和多任务学习的基础上,诊断出神经炎.

Lei Liu1, Haoyu Zhang2, Weifeng Zhang2

  • 1Medical College, Shantou University, Shantou, Guangdong, 515041, People's Republic of China.

Physics in medicine and biology
|January 18, 2024
PubMed
概括

一个新的放射学和深度学习算法准确地从CT扫描中诊断出神炎. 这种方法可视化了分级特征,提高了早期结性脊髓炎检测的解释性和诊断准确性.

关键词:
金融金融公司 (FFT)多任务学习是多任务学习.无线电学 (radiomics) 是一种无线电学.这种神经性炎是神经性炎.

更多相关视频

Quantitative [18F]-Naf-PET-MRI Analysis for the Evaluation of Dynamic Bone Turnover in a Patient with Facetogenic Low Back Pain
06:31

Quantitative [18F]-Naf-PET-MRI Analysis for the Evaluation of Dynamic Bone Turnover in a Patient with Facetogenic Low Back Pain

Published on: August 8, 2019

7.3K
Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery
09:38

Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery

Published on: April 14, 2016

12.7K

相关实验视频

Last Updated: Jun 16, 2026

Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease
04:44

Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease

Published on: June 16, 2020

19.9K
Quantitative [18F]-Naf-PET-MRI Analysis for the Evaluation of Dynamic Bone Turnover in a Patient with Facetogenic Low Back Pain
06:31

Quantitative [18F]-Naf-PET-MRI Analysis for the Evaluation of Dynamic Bone Turnover in a Patient with Facetogenic Low Back Pain

Published on: August 8, 2019

7.3K
Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery
09:38

Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery

Published on: April 14, 2016

12.7K

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 放射学 放射学是一门学科.

背景情况:

  • 圣炎是结性脊柱炎 (AS) 的早期指标.
  • 精确的成像诊断神炎对于早期AS检测至关重要.
  • 当前的深度学习模型需要大量的标记数据,缺乏功能可视化.

研究的目的:

  • 提出一种放射学和深度学习算法,用于在CT扫描上诊断神经炎.
  • 为了实现对分级特征的可视化,以提高临床解释性.
  • 为了提高精确度和减少神炎诊断中观察者之间的变异性.

主要方法:

  • 使用U-net和统计方法对关节 (SIJ) 3DCT图像进行细分.
  • 采集放射学特征与空间和频率域特征相结合.
  • 应用多任务学习与五类标签用于诊断.

主要成果:

  • 在私人数据集上实现了87.3%的准确率.
  • 与基线相比,准确度有9.8%的改善.
  • 结果与合格的医疗专业人员的评估一致.

结论:

  • 建议的放射学和深度学习方法提供了一个可解释和可移植的解决方案,用于自动神经炎的诊断.
  • 功能可视化增强了临床理解,有助于诊断和治疗规划.
  • 该算法显示了改善结性脊柱炎早期检测的巨大潜力.