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Tooth Anatomy01:21

Tooth Anatomy

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The human tooth enables us to eat a variety of foods, speak clearly, and even aid in shaping our faces. Teeth are composed of various elements that work together. Here's a detailed look at the anatomy of a human tooth.
The Crown, Neck, and Root
The visible part of the tooth is referred to as the crown. It's covered by enamel, the hardest substance in the human body. The crown is uniquely shaped for each type of tooth, allowing for different functions such as cutting, tearing, or...
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Updated: May 5, 2026

A Finite Element Approach for Locating the Center of Resistance of Maxillary Teeth
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A Finite Element Approach for Locating the Center of Resistance of Maxillary Teeth

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交叉中心模型 适应性牙分割

Ruizhe Chen1, Jianfei Yang2, Huimin Xiong3

  • 1Stomatology Hospital Affliated to Zhejiang University of Medicine, Zhejiang University, Hangzhou, 310016, China; ZJU-Angelalign R&D Center for Intelligence Healthcare, ZJU-UIUC Institute, Zhejiang University, Haining, 314400, China; Zhejiang Key Laboratory of Medical Imaging Artificial Intelligence, Zhejiang University, Hangzhou, 310058, China.

Medical image analysis
|January 8, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了跨中心模型适应性牙细分 (CMAT) 的新框架,以解决不同诊所的正统牙科AI模型的性能下降. 通过CMAT,可以在不共享敏感患者数据或不需要新的注释的情况下进行模型调整.

关键词:
交叉中心的中心.没有源代码的域名适应.牙细分是指牙的细分.

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相关实验视频

Last Updated: May 5, 2026

A Finite Element Approach for Locating the Center of Resistance of Maxillary Teeth
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科学领域:

  • 数字牙科数字牙科
  • 医学图像分析 医学图像分析
  • 医疗保健中的人工智能

背景情况:

  • 从口腔内扫描 (IOS) 进行的自动3D牙细分对于计算机辅助的正牙治疗至关重要.
  • 在医疗中心部署人工智能模型面临着由于数据分布转移和数据隐私问题的挑战,阻碍了模型再培训或微调.

研究的目的:

  • 提出一个新的框架,跨中心模型适应性牙细分 (CMAT),以适应预先训练的牙细分模型以适应新的中心,而无需共享数据或额外的注释.
  • 为了解决跨中心场景中数据分布转移造成的性能恶化.

主要方法:

  • CMAT将源中心模型调整为目标中心,使用牙级原型对齐模块,渐进型伪标记传输模块和牙前规范化信息最大化模块.
  • 该框架是为无源数据,无多源数据和测试时间的跨中心适应场景设计的.

主要成果:

  • 在两个数据集的三个交叉中心场景中,CMAT始终优于现有的基线方法.
  • 广泛的废除研究和统计分析证实了拟议方法的有效性.

结论:

  • 在数字牙科中,CMAT为模型适应性牙细分提供了一种有效且保护隐私的解决方案.
  • 该框架显示了在临床环境中与各种数据分布在现实世界中部署的巨大潜力.