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相关概念视频

X-ray Imaging01:24

X-ray Imaging

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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 23, 2025

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
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分段式X射线图像数据用于诊断使用深度学习的牙周围牙疾病.

Nisrean Thalji1, Emran Aljarrah2, Mohammad H Almomani3

  • 1Department of Robotics and Artificial Intelligence, Jadara University, Irbid, Jordan.

Data in brief
|June 17, 2024
PubMed
概括

这项研究引入了细分牙科X射线的新数据集,用于识别健康与患病牙. 本资源有助于开发人工智能工具,以准确检测牙科病理.

关键词:
深度学习是一种深度学习.牙科周边医疗器械诊断,检测,发现牙腐烂是指牙的腐烂.周围的病理学有关X射线数据的数据.

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科学领域:

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

背景情况:

  • 正确区分正常和异常的牙周X射线对于诊断牙病理至关重要.
  • 牙科X射线为牙和周围组织的生理和病理状态提供了必要的见解.
  • 现有的诊断方法可以通过利用医学成像数据的自动化系统来增强.

研究的目的:

  • 呈现一个细分的数据集的牙周牙周X射线图像.
  • 将图像分类为健康和患病的患者群体.
  • 为开发自动化牙科病理检测系统奠定基础.

主要方法:

  • 收集了来自约旦北部一家医院的患者的929张高质量的牙周周X射线图像.
  • 采用先进的图像细分技术进行数据处理.
  • 根据图像分析,将数据集分为健康和患病的牙科患者组.

主要成果:

  • 创建了一个标记的数据集,包含929张牙周周X射线图像.
  • 数据集包括各种牙科疾病病例,骨损失和周周异常.
  • 分段数据有助于开发人工智能模型,以区分正常和异常的牙状况.

结论:

  • 开发的数据集是培训牙科人工智能模型的宝贵资源.
  • 这项倡议支持为牙病理,如牙和脉疾病创建自动诊断工具.
  • 深度学习人工智能的进步显示了改善牙科诊断和检测准确性的重大前景.