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

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

Validation of AI-derived whole prostate volumetry versus ellipsoid formula for PSA density-based risk stratification.

European journal of radiology·2026
Same author

A Whole-Body PSMA-PET/CT dataset with manually annotated tumor lesions.

Scientific data·2026
Same author

ESR Innovation in Focus: Future PET radiotracers in oncology.

European radiology·2026
Same author

Impact of ischemia duration on MRI-derived perfusion parameters in a mouse kidney transplant model.

European radiology experimental·2026
Same author

AutoPET Challenge on Fully Automated Lesion Segmentation in Oncologic PET/CT Imaging, Part 2: Domain Generalization.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine·2025
Same author

Digital Transformation and Artificial Intelligence in Radiology: Challenges and Opportunities for Clinical Practice, Research, and the Next Generation.

RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin·2025

相关实验视频

Updated: Jul 14, 2025

Author Spotlight: Integrating High-Resolution Intravital Imaging and MRI to Enhance Stereotactic Body Radiation Therapy Planning
10:25

Author Spotlight: Integrating High-Resolution Intravital Imaging and MRI to Enhance Stereotactic Body Radiation Therapy Planning

Published on: April 12, 2024

1.4K

在瘤杂交成像中使用人工智能

Benedikt Feuerecker1,2, Maurice M Heimer1, Thomas Geyer1

  • 1Department of Radiology, University Hospital, LMU Munich, Munich, Germany.

Nuklearmedizin. Nuclear medicine
|October 6, 2023
PubMed
概括

人工智能 (AI) 通过改善病变检测和表征来增强瘤混合成像. 人工智能应用有望为基于证据的治疗指导提供高效的定量数据,但实施方面仍然存在挑战.

更多相关视频

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

879
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K

相关实验视频

Last Updated: Jul 14, 2025

Author Spotlight: Integrating High-Resolution Intravital Imaging and MRI to Enhance Stereotactic Body Radiation Therapy Planning
10:25

Author Spotlight: Integrating High-Resolution Intravital Imaging and MRI to Enhance Stereotactic Body Radiation Therapy Planning

Published on: April 12, 2024

1.4K
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

879
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K

科学领域:

  • 医疗成像医学成像
  • 在瘤学瘤学.
  • 人工智能的人工智能

背景情况:

  • 人工智能 (AI) 在医学成像中变得越来越重要,特别是在癌症混合成像中,因为大量的数据.
  • 在癌症治疗中,人工智能对于病变检测,表征,分期,治疗监测和复发检测至关重要.
  • 机器学习 (ML) 和深度学习 (DL) 的快速进步正在推动人工智能对成像工作流程和临床决策的影响.

研究的目的:

  • 审查当前的研究和AI在瘤杂交成像中的关键概念.
  • 探索AI在瘤学中的应用,专注于它们的潜在好处和当前限制.
  • 讨论人工智能对医学成像工作流程和临床结果的影响.

主要方法:

  • 这篇叙事综述介绍了瘤杂交成像中的AI和数据科学概念.
  • 它检查了瘤学中的相关AI应用,包括挑战和局限性.
  • 讨论了肺,前列腺和神经内分泌瘤的具体例子.

主要成果:

  • 人工智能应用程序可以有效地处理复杂的混合成像数据,用于自动化病变检测和表征.
  • 人工智能有助于高质量,高效的瘤疾病评估,有助于反应评估.
  • 人工智能有可能产生可重复的,结构化的,定量数据,用于治疗指导.

结论:

  • 人工智能具有显著的潜力,可以提高瘤杂交成像工作流程的质量和效率.
  • 目标是为基于证据的治疗指导生成定量数据,增强临床决策.
  • 必须解决人工智能应用开发,基准测试和临床实施的关键挑战.