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

X-ray Imaging01:24

X-ray Imaging

German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with X-rays, and by 1900, X-ray was widely...

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

The rise of medical autonomous care, a paradigmatic turning point for military and civilian delivery of health care.

Journal of critical care·2026
Same author

Feasibility of Technology-Assisted Lifestyle Self-Monitoring in Older Adults With Type 2 Diabetes: Mixed Methods Pilot Study.

JMIR formative research·2026
Same author

Deep Learning-Based Tracking of Neurovascular Features Toward Semi-Automated Ultrasound-Guided Peripheral Nerve Blocks by Non-Specialists.

Bioengineering (Basel, Switzerland)·2026
Same author

Addressing Racial Disparities in a Hispanic Population Through Living Donor Liver Transplantation-A Comparison of 2 Eras.

Clinical transplantation·2026
Same author

From battlefield to community: Simulation-based education for walking blood bank whole blood transfusion.

Transfusion·2026
Same author

Initial Calcium Derangements in Major Trauma and Outcomes.

JAMA network open·2026

相关实验视频

Updated: Jun 12, 2026

Measuring the Complete-arch Distortion of an Optical Dental Impression
06:51

Measuring the Complete-arch Distortion of an Optical Dental Impression

Published on: May 30, 2019

8.0K

人工智能扩散模型使用有限的数据集生成现实的合成牙科放射图.

Brian Kirkwood1, Byeong Yeob Choi2, James Bynum3

  • 1Organ Support and Automation Technologies, U.S. Army Institute of Surgical Research, 3698 Chambers Pass, Bldg 3611, Ft. Sam Houston, San Antonio, TX 78234, USA.

Journal of imaging
|October 28, 2025
PubMed
概括

生成型人工智能可以创建现实的合成牙科放射图来训练牙科人工智能系统. 专家投入和技术改进显著提高了这些人工智能生成图像的质量和临床现实性.

关键词:
人工智能的人工智能是人工智能.处理数据的数据处理.深度学习是一种深度学习.牙科放射学 牙科放射学扩散模型的扩散模型.在循环中的人类.图像生成 图像生成审判的判断判断的判断.综合数据 综合数据

更多相关视频

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

2.2K
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

1.3K

相关实验视频

Last Updated: Jun 12, 2026

Measuring the Complete-arch Distortion of an Optical Dental Impression
06:51

Measuring the Complete-arch Distortion of an Optical Dental Impression

Published on: May 30, 2019

8.0K
Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

2.2K
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

1.3K

科学领域:

  • 人工智能的人工智能
  • 医疗成像医学成像
  • 牙科 牙科是指牙科的专业.

背景情况:

  • 牙科放射图的有限可用性阻碍了牙科AI系统的开发.
  • 生成型人工智能通过创建合成牙科放射图 (SDR) 来提供解决方案.
  • 评估人工智能产生的图像需要专家和客观的评估.

研究的目的:

  • 使用生成性AI开发临床现实的SDR.
  • 评估由专家提供的数据策划和模型改进对SDR质量的影响.
  • 通过主观的专家审查和客观的定量指标来验证SDR现实主义.

主要方法:

  • 一个逐步的方法被用来处理10,000张牙科X线图.
  • 牙医查和选择精制人工智能模型的训练数据集.
  • 三种人工智能模型产生了SDR,这些SDR通过专家审查和定量指标 (FID,KID) 进行评估.

主要成果:

  • 专家知情的策划改善了SDR的现实性.
  • 人工智能模型架构的完善进一步提高了SDR质量.
  • 客观指标 (FID,KID) 证实了由于专家投入和技术改进,图像保真度的改善.

结论:

  • 以专家为基础的数据策划和特定领域的评估对于高准确度SDR生成至关重要.
  • 精细的AI模型架构为创建现实的SDR提供了坚实的基础.
  • 主观和客观评估的融合为开发的SDR生成方法构建了对SDR生成方法的信心.