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

Osteoclasts in Bone Remodeling01:31

Osteoclasts in Bone Remodeling

2.9K
Osteoclasts are cells responsible for bone resorption and remodeling. They originate from hematopoietic progenitor cells present in the bone marrow. Numerous progenitor cells fuse to form multinucleated cells, each with 10-20 nuclei. A single osteoclast has a diameter of 150 to 200 µM. These cells have ruffled borders that break down the underlying bone tissue and release minerals such as calcium into the blood in bone resorption. Osteoclasts cling to bones with their ruffled edges during...
2.9K

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

Development of a deep learning based framework for classification of Indian venomous snakes integrated with explainable artificial intelligence for primary and emergency care providers.

PLoS neglected tropical diseases·2026
Same author

AlzStack: Forecasting early-onset Alzheimer's with an explainable AI system using multiple data balancing techniques.

Global epidemiology·2026
Same author

An explainable artificial intelligence framework for ischemic heart disease prediction using enhanced squirrel search feature selection.

Scientific reports·2026
Same author

Interpretable machine learning and deep neural networks for ICU admission prediction in paediatric respiratory patients.

Scientific reports·2026
Same author

Diagnostic Accuracy of Artificial Intelligence Models for Differentiation of Squamous Cell Carcinoma and Adenocarcinoma of Lung-A Systematic Review.

Diagnostics (Basel, Switzerland)·2026
Same author

MM-GradCAM: an improved multimodal GradCAM method with 1D and 2D ECG data for detection of cardiac arrhythmia.

Scientific reports·2026

相关实验视频

Updated: Jul 7, 2025

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

6.8K

使用机器学习和可解释的人工智能进行骨质疏松风险预测的决策支持系统.

Varada Vivek Khanna1, Krishnaraj Chadaga2, Niranjana Sampathila1

  • 1Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, India.

Heliyon
|December 25, 2023
PubMed
概括

机器学习模型可以使用患者数据以89%的准确度预测骨质疏松症风险. 该系统旨在帮助医生早期诊断和自动查这种常见的骨疾病.

关键词:
集体学习 - 学习.可解释的机器学习特性选择技术 特性选择技术机器学习 机器学习骨质疏松症是一种骨质疏松症.

更多相关视频

Semiautomated Longitudinal Microcomputed Tomography-based Quantitative Structural Analysis of a Nude Rat Osteoporosis-related Vertebral Fracture Model
07:12

Semiautomated Longitudinal Microcomputed Tomography-based Quantitative Structural Analysis of a Nude Rat Osteoporosis-related Vertebral Fracture Model

Published on: September 28, 2017

8.2K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

相关实验视频

Last Updated: Jul 7, 2025

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

6.8K
Semiautomated Longitudinal Microcomputed Tomography-based Quantitative Structural Analysis of a Nude Rat Osteoporosis-related Vertebral Fracture Model
07:12

Semiautomated Longitudinal Microcomputed Tomography-based Quantitative Structural Analysis of a Nude Rat Osteoporosis-related Vertebral Fracture Model

Published on: September 28, 2017

8.2K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

科学领域:

  • 医疗信息学 医疗信息学
  • 医疗保健中的人工智能
  • 骨代谢研究研究 骨代谢研究

背景情况:

  • 骨质疏松症是一种代谢性骨疾病,其特点是骨矿物质密度和质量下降,导致骨脆弱.
  • 这种情况往往是无症状的,直到发生骨折才被诊断出来,这凸显了早期检测的必要性.
  • 机器学习 (ML) 为分析健康数据提供了强大的工具,以预测疾病风险并改善患者的治疗结果.

研究的目的:

  • 开发和评估用于预测骨质疏松风险的异质机器学习框架.
  • 确定关键的患者参数,以准确评估骨质疏松症风险.
  • 创建一个自动查系统,以支持临床决策.

主要方法:

  • 利用了1493名患者的开源数据集,包括骨密度,血液和物理测试结果.
  • 应用了13种不同的特征选择技术来识别突出的预测参数.
  • 开发了一个多级合体学习堆,优化了Forward Feature Selection,用于风险预测.

主要成果:

  • 性能最好的ML管道在预测骨质疏松风险方面达到89%的准确性.
  • 使用可解释的人工智能工具 (SHAP,LIME,ELI5,Qlattice) 来确保模型预测的透明度和可解释性.
  • 该研究成功确定了有助于骨质疏松风险评估的关键特征.

结论:

  • 开发的ML框架为骨质疏松风险预测提供了一个整体的方法.
  • 该系统提供了自动查的潜力,协助医生及时诊断.
  • 整合ML和可解释AI可以显著提高骨质疏松症的管理.