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相关概念视频

Teeth01:15

Teeth

424
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...
424

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相关实验视频

Updated: Jul 4, 2025

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
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基于OPG的牙年龄估计,使用深度学习技术的数据技术探索.

Barkın Büyükçakır1, Jeroen Bertels1, Peter Claes1

  • 1ESAT, Center for Processing Speech and Images, KU Leuven, Leuven, Belgium.

Journal of forensic sciences
|January 31, 2024
PubMed
概括

优化卷积神经网络的超参数,比如批量大小,显著改善了自动牙科年龄估计从骨科透视图 (OPGs). 较大的批量大小通常会提高使用深度学习的法医年龄评估的准确性.

关键词:
有机天然气 (OPG) 是一种天然气.卷积神经网络是一种卷积神经网络.牙年龄的估计.超参数优化超参数优化

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科学领域:

  • 法医科学 法医科学 法医科学
  • 计算机科学 计算机科学
  • 放射学 放射学是一门学科.

背景情况:

  • 手动的牙科年龄估计面临着诸如无聊和观察者之间的变化等挑战.
  • 用于年龄估计的自动深度学习方法与数据稀缺性和培训复杂性作斗争.

研究的目的:

  • 研究卷积神经网络 (CNN) 超参数对牙年龄估计准确性的影响.
  • 评估模型复杂性,批量大小和样本数量对来自骨科肌图谱 (OPG) 的年龄估计的影响.

主要方法:

  • 在3896个OPG上对EfficientNet-B4,DenseNet-201和MobileNet V3模型进行交叉验证.
  • 训练的批量大小从10到160,并使用随机数据子集.
  • 对超参数调整对平均绝对误差 (MAE) 的影响的分析.

主要成果:

  • 在全数据集中,EfficientNet-B4实现了0.562年的最低MAE,批量大小为160.
  • 增加批量大小提高了EfficientNet-B4和DenseNet-201的性能,而MobileNet V3的批量大小达到40的峰值.
  • 使用完整数据集的培训优于缩小样本大小的培训,突出显示了数据量的重要性.

结论:

  • 超参数优化对于有效的基于深度学习的牙年龄估计至关重要.
  • 量身定制的培训方法和足够的数据是实现准确的法医年龄评估的关键.
  • 这项研究推进了自动化的年龄估计,展示了优化CNN的潜力.