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相关概念视频

Teeth01:15

Teeth

The formation of teeth, also known as odontogenesis, is a complex process that begins in utero, around the sixth week of embryonic development. There are three stages to this process: the bud stage, the cap stage, and the bell stage.
In the bud stage, the tooth germ (an aggregation of cells) starts to form in the developing jawbone. During the cap stage, the tooth germ differentiates into enamel organ, dental papilla, and dental sac, which will later develop into the tooth's enamel, dentin and...
Tooth Anatomy01:21

Tooth Anatomy

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 grinding food.

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相关实验视频

Updated: Jul 19, 2026

Real-Time Dynamic Navigation System for the Precise Quad-Zygomatic Implant Placement in a Patient with a Severely Atrophic Maxilla
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Published on: October 18, 2021

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人工智能的表现在上狗的冲击:一个系统的审查.

Hassan Ahmed Assiri1,2, Budur Khalid Alsaanah3, Bushra Alshahrani3

  • 1Department of Diagnostic Sciences and Oral Biology and Periodontology, College of Dentistry, King Khalid University, Abha, Saudi Arabia. halmuawad@kku.edu.sa.

European journal of medical research
|January 16, 2026
PubMed
概括

人工智能 (AI) 在诊断受影响的上犬 (MCI) 和预测爆发结果方面表现出高准确性. 人工智能工具可以显著帮助临床医生在MCI诊断和治疗规划.

关键词:
人工智能的人工智能是人工智能.这里是的.上狗的上狗.牙冲击是一个牙冲击.

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Author Spotlight: 3D Movement Assessment of Maxillary Posterior Teeth in Clear Aligner Treatment
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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:

  • 大牙损伤 (MCI) 是一种常见的牙异常.
  • 准确的诊断和预测对于有效的治疗计划至关重要.

研究的目的:

  • 系统地审查有关人工智能 (AI) 在诊断和预测上犬损伤 (MCI) 的实用性和应用的文献.

主要方法:

  • 在四个数据库 (PubMed,Scopus,Web of Science,Google Scholar) 中进行了系统的搜索.
  • 包括专注于AI应用在MCI诊断和预测方面的研究.
  • 诊断准确性研究使用QUADAS-2进行评估,使用PROBASST进行预测性研究.

主要成果:

  • 七项研究符合纳入标准,偏差风险低.
  • 人工智能显示出高的诊断准确性 (高达98.3%) 和高效的细分 (迪斯系数0.99).
  • 人工智能在3D重建和根吸收分类方面显示了中等至高的预测性能.

结论:

  • 人工智能在定位受影响的上犬和预测喷发结果方面表现出高准确度.
  • 人工智能具有显著的潜力,可以支持临床医生在MCI诊断,治疗计划和管理方面.
  • 需要使用标准化方法和更大的样本进行进一步的研究,以确认临床适用性.