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

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 long...

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

Mandibular Prognathism in Dolang Sheep: Hi-C Evidence for Localized TAD Remodeling at Craniofacial Loci.

Animals : an open access journal from MDPI·2026
Same author

Construction of a novel prediction model based on albumin-hemoglobin score and serum microRNA-497-5p for prognosis of patients with stage II-III colorectal cancer.

Oncology letters·2025
Same author

Spinal infection caused by <i>Aspergillus terreus</i> in immunocompetent individuals: a case report and literature review.

Frontiers in medicine·2025
Same author

DEAF1 confers resistance to adriamycin-induced apoptosis and pyroptosis in multiple myeloma.

Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy·2025
Same author

Integrated metabolomic and transcriptomic analysis reveals digestive tract adaptations to high altitude in Bayanbulak sheep.

Frontiers in veterinary science·2025
Same author

Effectiveness and safety of orelabrutinib combined with rituximab, temozolomide, methotrexate, and cytarabine in intensive chemotherapy-unfit patients with PCNSL in China.

European journal of medical research·2025

相关实验视频

Updated: Jun 21, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.6K

儿童骨质疏松症诊断中的人工智能:使用手腕X射线评估深度网络分类和模型解释性.

Chelsea E Harris1, Lingling Liu1, Luiz Almeida2

  • 1Division of Physics, Engineering, Mathematics, and Computer Science, Delaware State University, 1200 N. Dupont Hwy., Dover, 19901, DE, USA.

Bone reports
|May 9, 2025
PubMed
概括

机器学习模型在儿科手腕X射线中准确地识别了骨质疏松症,达到95.2%的准确性. 可解释的AI提供了洞察力,支持在临床环境中早期诊断骨质疏松症.

关键词:
深度学习是一种深度学习.可解释的人工智能骨质疏松症预测的预测一些X射线图像.

更多相关视频

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

743
Cortical Bone Assessment Using Ultrasonic Guided Waves: A Reproducibility Study in a Healthy Population
09:02

Cortical Bone Assessment Using Ultrasonic Guided Waves: A Reproducibility Study in a Healthy Population

Published on: January 31, 2025

351

相关实验视频

Last Updated: Jun 21, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.6K
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

743
Cortical Bone Assessment Using Ultrasonic Guided Waves: A Reproducibility Study in a Healthy Population
09:02

Cortical Bone Assessment Using Ultrasonic Guided Waves: A Reproducibility Study in a Healthy Population

Published on: January 31, 2025

351

科学领域:

  • 医学成像分析分析 医学成像分析
  • 医疗保健中的人工智能
  • 骨健康研究 骨健康研究

背景情况:

  • 骨质疏松症是一种骨密度低的疾病,在全球范围内影响着数百万人.
  • 诊断通常依赖于骨矿物质密度 (BMD) 评估.
  • 机器学习 (ML) 显示出对医学图像分析的前景.

研究的目的:

  • 用儿科手腕X射线评估骨质疏松症分类的深度学习网络.
  • 应用可解释AI (XAI) 来解释模型决策.
  • 评估ML在临床骨质疏松症诊断中的潜力.

主要方法:

  • 利用六个深度学习网络 (包括CNN和变压器) 来进行二进制分类 (骨质疏松症与健康).
  • 使用了GRAZPEDWRI-DX儿科手腕X射线数据集.
  • 应用了两种XAI技术来对模型预测进行视觉解释.

主要成果:

  • 深度网络有效地学习了区分骨质疏松和健康骨的特征.
  • 在各种模型中实现了高分类准确率.
  • 使用转移学习的DenseNet201实现了最高的准确率95.2%.
  • XAI为模型的决策过程提供了可解释的见解.

结论:

  • 深度学习模型在儿科手腕X射线中显示出明显的区别骨质疏松症与健康骨的能力.
  • 高精度和可解释的解释的结合支持将ML集成到临床工作流程中.
  • 这种方法有望为早期和更准确的骨质疏松症诊断提供希望.