Search research articles

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 Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

相关概念视频

Assessment of the Abdomen II: Percussion

Assessment of the Abdomen II: Percussion

Percussion is a fundamental technique used to assess the liver, spleen, and abdominal organs by tapping the abdomen and interpreting the resulting sounds. This method helps identify fluid, distention, and masses through variations in sound, such as the high-pitched tympany of air-filled areas and the dullness of solid masses. Understanding how to percuss these organs provides valuable information for healthcare professionals in diagnosing conditions early.
Percussion
Percussion is an essential...

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

Assessing Treatment Response in Soft Tissue Sarcoma Using Dynamic Contrast-Enhanced MRI: A Systematic Review.

Korean journal of radiology·2026

Same author

Genome-wide CG hypomethylation of the <i>Arabidopsis</i> ecotype Cvi linked to structural variation and RNAi at the <i>VIM4</i>-<i>VIM2</i> locus.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

Prevalence and Relative Proportions of MS, NMOSD, and MOGAD in the Republic of Korea.

Neurology·2026

Same author

Red/NIR-Emissive, Cadmium-Free Quantum Dots: Synthesis, Luminescence Mechanisms, and Applications.

Sensors (Basel, Switzerland)·2026

Same author

Decrease in Nucleated Particles and Cloud Condensation Nuclei Observed across a Range of Environments.

Environmental science & technology·2026

Same author

Deep Learning-based Bone Mineral Density Prediction Using Pediatric Chest Radiographs: A Multicenter Feasibility Study.

Radiology·2026

Search research articles

相关实验视频

Updated: Jun 19, 2025

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

Published on: July 21, 2023

基于深度学习的完全自动的瑞塞阶段评估模型,使用腹部放射图.

Jae-Yeon Hwang^1,2,3, Yisak Kim^1,4,5, Jisun Hwang⁶

¹Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.

Pediatric radiology

|July 24, 2024

概括

一个新的深度学习模型自动从腹部X射线图中确定瑞塞阶段,在分类骨成熟度方面达到高精度. 这种人工智能工具有助于评估青少年的脊柱发育.

关键词:

在腹部,腹部.孩子孩子孩子孩子孩子孩子深度学习是一种深度学习.伊利乌姆 (Ilium) 是一个古老的城市.放射学放射学放射学放射学里塞尔阶段是里塞尔阶段.

更多相关视频

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

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

Published on: August 16, 2020

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

相关实验视频

Last Updated: Jun 19, 2025

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

Published on: July 21, 2023

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

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

Published on: August 16, 2020

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

科学领域:

放射学放射学是一门学科.
人工智能的人工智能
医疗成像医学成像

背景情况:

人工智能 (AI) 在医学图像分类方面表现出专家级别的表现.
准确评估骨成熟度对于骨科治疗计划至关重要.

研究的目的:

开发一种全自动的深度学习方法,用于使用腹部X射线图来确定Risser阶段.
评估模型在骨成熟度分类方面的表现.

主要方法:

一个多中心数据集由1681张腹部X射线图 (年龄9-18岁) 被追溯收集.
一个骨盆骨细分模型 (DeepLabv3+与EfficientNet-B0) 提取了阴茎顶部补丁.
一个ConvNeXt-B模型被训练为Risser阶段分类,通过精度,AUROC和平均绝对误差来评估性能.

主要成果:

自动Risser阶段评估模型实现了0.87 (内部) 和0.75 (外部) 的准确性.
平均绝对误差为0.13 (内部) 和0.26 (外部).
接收器运行特征曲线 (AUROC) 下的面积达到0.99 (内部) 和0.95 (外部).

结论:

成功开发了一种基于深度学习的,完全自动的分段和分类模型,用于Risser阶段评估.
该模型在从腹部X射线图分类骨成熟度方面表现出很高的性能.
这种人工智能方法为客观的Risser分期提供了一个有前途的工具.