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相关概念视频

Tooth Anatomy01:21

Tooth Anatomy

400
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...
400
Teeth01:15

Teeth

361
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...
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相关实验视频

Updated: Jun 14, 2025

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

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人类牙裂纹图像分析与多种深度学习方法.

Zheng Li1, Zhongqiang Li1, Ya Zhang1

  • 1Division of Electrical and Computer Engineering, College of Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA.

Annals of biomedical engineering
|September 6, 2024
PubMed
概括
此摘要是机器生成的。

诊断牙裂是一个挑战. 一种新的近红外光成像技术与深度学习模型相结合,显著提高了检测牙裂的准确性,帮助牙科专业人员.

关键词:
裂纹检测 裂纹检测 裂纹检测 裂纹检测深度学习是一种深度学习.人类牙裂 诊断 诊断 人类牙裂 诊断牙科成像 NIRF 牙科成像

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科学领域:

  • 牙科成像 牙科成像 牙科成像
  • 人工智能的人工智能
  • 生物医学工程 生物医学工程

背景情况:

  • 牙裂很常见,难以检测,往往需要及时治疗以防止牙脱落.
  • 像X射线这样的传统成像方法在可视化微妙的牙裂方面存在局限性.
  • 印ocyanine绿色 (ICG) 辅助近红外光 (NIRF) 成像提供了更好的可视化由于深光透和ICG光.

研究的目的:

  • 评估ICG辅助的NIRF牙科成像技术用于检测牙裂的有效性.
  • 应用机器学习和深度学习模型来自动分析牙裂纹图像.
  • 评估这种综合方法在协助牙医诊断裂方面的潜力.

主要方法:

  • 使用NIR成像视频获得了593张破裂和601张非破裂牙图像的数据集.
  • 机器学习模型,包括预训练的残余网络和squeezenet1_1,用于图像分类.
  • 对象检测 (单拍多盒探测器 - SSD) 和超分辨率 (SR-Generative Adversarial Network) 模型用于裂纹识别和图像增强.

主要成果:

  • 分类模型实现了高准确度:残留网络为88.2%,squeezenet1_1为94.25%,用于区分裂纹与非裂纹的牙.
  • 固态硬盘模型成功地识别了裂,无论输入图像大小如何.
  • 在SR-Generative Adversarial网络中,破解图像的分辨率得到了改进.

结论:

  • 深度学习模型显著提高了牙裂识别的效率和准确性.
  • 开发的NIR牙科成像系统与深度学习相结合,显示出有很大的潜力,可以帮助牙医诊断牙裂.
  • 这项技术可能会导致更早,更可靠地检测牙骨折.