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

Sutures of the Skull01:22

Sutures of the Skull

6.9K
The human skull is composed of several bones that come together to protect the brain and support the structures of the face. The junctions where these bones meet are called sutures.
Sutures are immobile joints between adjacent bones of the skull. The narrow gap between the bones is filled with dense, fibrous connective tissue that unites the bones. The long sutures located between the skull bones are not straight but instead follow irregular, tightly twisting paths. These twisting lines tightly...
6.9K

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

Effects of Extraction Methods on the Structural Characteristics and Bioactivities of Houttuynia cordata Polysaccharides.

Journal of food science·2026
Same author

Synthesis, structural characterization, antioxidant activity, and α-glycosidase inhibitory effects of carboxymethylated Houttuynia cordata polysaccharide.

International journal of biological macromolecules·2026
Same author

Comprehensive analysis of real-world data on liraglutide treatment in patients with obesity: a multicenter national study.

European journal of medical research·2025
Same author

Development and characterization of BSA-pullulan gel matrices incorporating orange peel flavonoids: Structural interactions and antimicrobial and antioxidant functionalities.

Food chemistry·2025
Same author

Goal-oriented balance rehabilitation system for balance disorder.

Medical engineering & physics·2025
Same author

Predicting thyroid cancer recurrence using supervised CatBoost: A SHAP-based explainable AI approach.

Medicine·2025

相关实验视频

Updated: Jul 18, 2025

A Training and Testing System for Performing Vascular Reconstruction In Vitro
09:52

A Training and Testing System for Performing Vascular Reconstruction In Vitro

Published on: October 26, 2019

8.0K

基于深度学习的接培训系统.

Mohammed Mansour1, Eda Nur Cumak1, Mustafa Kutlu1

  • 1Department of Mechatronics Engineering, Sakarya University of Applied Sciences, Sakarya, Turkey.

Surgery open science
|August 21, 2023
PubMed
概括
此摘要是机器生成的。

深度学习 (DL) 模型可以准确地分类手术 success. 该Xception模型实现了95%的准确性,提供了一个数字工具来改善学员评估和减少错误.

关键词:
分类 分类 分类 分类.深度学习是一种深度学习.专业培训 专业培训是什么

更多相关视频

Step By Step: Microsurgical training method combining two nonliving animal models
05:25

Step By Step: Microsurgical training method combining two nonliving animal models

Published on: May 9, 2015

15.3K
Novel Triple-Loop Technique for Suturing TFCC Injuries without Transosseous Tunnel
08:27

Novel Triple-Loop Technique for Suturing TFCC Injuries without Transosseous Tunnel

Published on: May 23, 2025

139

相关实验视频

Last Updated: Jul 18, 2025

A Training and Testing System for Performing Vascular Reconstruction In Vitro
09:52

A Training and Testing System for Performing Vascular Reconstruction In Vitro

Published on: October 26, 2019

8.0K
Step By Step: Microsurgical training method combining two nonliving animal models
05:25

Step By Step: Microsurgical training method combining two nonliving animal models

Published on: May 9, 2015

15.3K
Novel Triple-Loop Technique for Suturing TFCC Injuries without Transosseous Tunnel
08:27

Novel Triple-Loop Technique for Suturing TFCC Injuries without Transosseous Tunnel

Published on: May 23, 2025

139

科学领域:

  • 医疗教育 技术 技术 医学教育
  • 在外科手术中使用人工智能
  • 外科手术技能的评估.

背景情况:

  • 手术是医学和牙科学生的核心技能.
  • 目前的技巧评估是主观的,缺乏具体的技术反.
  • 技术进步使得能够客观地衡量外科技能.

研究的目的:

  • 评估深度学习 (DL) 技术在评估手术接成功方面的有效性.
  • 开发一个人工智能驱动的工具,用于客观的技巧分类.

主要方法:

  • 六个卷积神经网络 (CNN) 模型 (VGG16,VGG19,Xception,Inception,MobileNet,DensNet) 在一个图像数据集上进行了训练.
  • 模型使用精度,回忆和F1分数进行评估,用于分类成功与不成功的.

主要成果:

  • Xception模型的准确性最高,达到95%.
  • 其他模型的准确性各不相同:移动网络 (91%),DensNet (90%),Inception (84%),VGG16 (73%) 和VGG19 (61%).
  • 开发了一个图形用户界面,用于实时图像评估.

结论:

  • 深度学习模型可以客观而准确地评估外科接技能.
  • 人工智能驱动的评估可以最大限度地减少缺乏经验的错误,并提高医生的效率.
  • 数字化接技能评估为医疗培训提供了一个可扩展的解决方案.