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相关概念视频

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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...
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相关实验视频

Updated: Jun 13, 2025

Creation of Patient-Specific Silicone Cardiac Models with Applications in Pre-surgical Plans and Hands-on Training
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为医学成像生成合成数据.

Lennart R Koetzier1, Jie Wu1, Domenico Mastrodicasa1

  • 1From the Delft University of Technology, Delft, the Netherlands (L.R.K.); Segmed, 3790 El Camino Real #810, Palo Alto, CA 94306 (J.W., A.L., M.C., W.A.K., J.P., M.J.W.); Department of Radiology, University of Washington, Seattle, Wash (D.M.); Department of Radiology, OncoRad/Tumor Imaging Metrics Core, Seattle, Wash (D.M.); Harvard University, Cambridge, Mass (J.P.); Department of Radiology, Stanford University School of Medicine, Palo Alto, Calif (A.S.C.); Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, Calif (A.S.C.); Department of Biomedical Informatics, Harvard Medical School, Boston, Mass (P.R.); Microsoft, Redmond, Wash (M.P.L.); and Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, Calif (M.P.L.).

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|September 10, 2024
PubMed
概括
此摘要是机器生成的。

由人工智能 (AI) 生成的合成数据可以增强医学成像数据集,解决稀缺性和隐私问题. 然而,确保数据的真实性,伦理使用和监管合规性对于医疗保健中的AI至关重要.

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 数据科学数据科学数据科学

背景情况:

  • 医疗成像的AI模型需要大量,多样化的数据集,由于隐私,伦理和基础设施障碍,这些数据很难获得.
  • 由人工智能生成的合成医学成像数据提供了一个解决方案,可以增强和匿名真实数据,从而实现新的应用.
  • 尽管有好处,合成数据存在技术和伦理方面的挑战,包括确保现实性,多样性和不可识别性.

研究的目的:

  • 提供关于医疗成像中合成数据的当前知识的概述.
  • 突出合成医学成像数据的生成和应用中的关键挑战.
  • 在这个快速发展的领域指导未来的研究和开发.

主要方法:

  • 本综述综合了现有的关于合成数据生成和在医学成像中的应用的文献.
  • 它分析了技术挑战,包括现实主义,多样性和计算成本.
  • 它检查了与合成医学图像数据相关的伦理考虑和监管差距.

主要成果:

  • 合成数据可以通过增加数据集大小和多样性,同时保持患者隐私来增强医学成像AI.
  • 应用包括模式翻译,对比度增强和放射科医生培训.
  • 关键的挑战包括确保数据质量,评估模型性能和解决高计算需求.

结论:

  • 合成数据在医疗成像中对人工智能的发展具有重大潜力,但需要谨慎管理.
  • 更新的法规和监管机构,临床医生和AI开发人员之间的协作努力是必不可少的.
  • 需要继续进行研究,以完善合成医学成像数据的伦理和有效使用的最佳实践.