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

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

Radiologist Detection of Deficiencies in LLM-Generated Patient Communications About Radiology Reports: A Multi-Reader Study.

Journal of imaging informatics in medicine·2026

Same author

Patient-Facing Radiology Communication with LLMs: Calibration Deficit and the Metadata Paradox.

Healthcare (Basel, Switzerland)·2026

Same author

Strategies to Increase Patients' Adherence to Digital Therapeutics for Musculoskeletal Diseases: A Narrative Review.

Yonsei medical journal·2026

Same author

Evaluation of a newly developed hybrid brace for scoliosis: Usability tests and subject-specific finite element analysis.

Technology and health care : official journal of the European Society for Engineering and Medicine·2025

Same author

Predictive Factors for Postoperative Outcomes of Cervical Spondylotic Myelopathy in Individuals With Cerebral Palsy.

Global spine journal·2025

Same author

A deep-learning algorithm using real-time collected intraoperative vital sign signals for predicting acute kidney injury after major non-cardiac surgeries: A modelling study.

PLoS medicine·2025

Search research articles

相关实验视频

Updated: Sep 14, 2025

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

Published on: July 2, 2021

基于深度学习的地标检测模型用于多种脚形分类:双中心研究

Su Ji Lee¹, Hangyul Yoon², Seongsu Bae²

¹Department and Research Institute of Rehabilitation Medicine, Yonsei University College of Medicine, Seoul, Korea.

Yonsei medical journal

|July 25, 2025

概括

此摘要是机器生成的。

一个新的热图中的热图 (HIH) 模型自动化了从放射图中诊断脚形,比手工方法提高了准确性和效率. 这种人工智能工具为临床使用提供了可靠的解决方案.

关键词:

人工智能的人工智能是人工智能.深度学习是一种深度学习.诊断成像诊断成像的使用.足部形足部形足部形

更多相关视频

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Published on: September 8, 2023

High-Resolution Three-Dimensional Imaging of the Footpad Vasculature in a Murine Hindlimb Gangrene Model

High-Resolution Three-Dimensional Imaging of the Footpad Vasculature in a Murine Hindlimb Gangrene Model

Published on: March 16, 2022

相关实验视频

Last Updated: Sep 14, 2025

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

Published on: July 2, 2021

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Published on: September 8, 2023

High-Resolution Three-Dimensional Imaging of the Footpad Vasculature in a Murine Hindlimb Gangrene Model

High-Resolution Three-Dimensional Imaging of the Footpad Vasculature in a Murine Hindlimb Gangrene Model

Published on: March 16, 2022

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
骨科诊断器骨科诊断器骨科诊断器

背景情况:

手动诊断从X射线照片的脚形是劳动密集型,容易变化.
需要自动诊断工具来提高效率和一致性.

研究的目的:

引入一种基于热图中的热图 (HIH) 的新型模型,用于自动诊断脚部形.
为了评估HIH模型的性能,使用承重脚的X线图.

主要方法:

一个双中心的回顾性研究,涉及第一中心的1561张 (培训) 和374张 (验证) 图像,以及第二中心的527张用于外部验证的图像.
在前后 (AP) 和侧侧X射线图中使用预测地标之间的角度来诊断五脚形.
与基线模型的比较,FlatNet.

主要成果:

与FlatNet相比,HIH模型实现了更高的精度 (85.1%与78.9%相比),灵敏度 (84.1%与78.9%) 和特异性 (85.9%与79.0%相比).
HIH显示出较低的错误率,更高的检测率,更快的处理速度和更少的参数.
强大的性能在内部和外部验证套件中得到证实.

结论:

热图中的热图 (HIH) 模型在多种足部形的自动诊断中显示出高效率.
这种人工智能驱动的方法对各种基于里程碑的医学成像任务具有前景.