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Teeth01:15

Teeth

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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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端到端与人为定义的特征提取:比较深度学习方法使用下第三的年龄分类.

Witsarut Upalananda1, Arnon Charuakkra2, Sitthichok Chaichulee3

  • 1Department of Oral Diagnostic Sciences, Faculty of Dentistry, Prince of Songkla University, Songkhla, Thailand.

The Journal of forensic odonto-stomatology
|December 26, 2025
PubMed
概括

使用下第三的法医年龄估计显示,深度学习可以提高准确性. 人类定义的特征提取方法平衡了特异性和可解释性,以获得可靠的年龄分类.

关键词:
深度学习是一种深度学习.牙年龄估计 牙年龄估计法医牙科学 法医牙科学的第三个牙.

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科学领域:

  • 法医牙科 法医牙科
  • 放射学 放射学是一门学科.
  • 人工智能的人工智能

背景情况:

  • 准确的年龄分类对于法律和法医目的至关重要.
  • 第3是年龄估计的关键指标,特别是在18岁以下或18岁以上的个体.
  • 评估不同的年龄分类方法对于改善法医科学至关重要.

研究的目的:

  • 为了比较传统基于人体的方法,端到端的深度学习和人类定义的特征提取的疗效,使用泰国下第三的放射图来进行年龄分类.
  • 确定在法医背景下对年龄估计的最佳方法.

主要方法:

  • 分析了来自14-23岁的个体的3,407张下第三杆放射图的数据集.
  • 对比了三种方法:修改的德米尔吉安分类,端到端卷积神经网络 (CNN) 年龄预测,以及基于CNN的牙发育阶段估计年龄分类.
  • 性能指标包括灵敏度,特异性和贝叶斯测试后概率.

主要成果:

  • 传统方法具有高特异性 (0.99),但敏感性低 (0.45).
  • 端到端的深度学习模型显示了提高的灵敏度 (0.65-0.74),具有良好的特异性 (0.91-0.95).
  • 人类定义的特征提取方法实现了高特异性 (0.95-0.97) 和可解释性,灵敏度在0.51-0.56.

结论:

  • 虽然传统的方法提供了高的特异性,但他们缺乏对年龄分类的敏感性.
  • 深度学习方法,特别是人类定义的特征提取,为法医年龄估计提供了一个平衡和可解释的解决方案.
  • 人类定义的特征提取方法显示了临床应用在年龄确定中的重大潜力.