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相关概念视频

Teeth01:15

Teeth

417
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...
417

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信息融合用于从落叶牙估计婴儿年龄,使用机器学习.

Práxedes Martínez-Moreno1,2, Andrea Valsecchi3, Sergio Damas2,4

  • 1Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain.

American journal of biological anthropology
|February 24, 2024
PubMed
概括

机器学习 (ML) 准确地使用落叶牙发育来估计婴儿年龄. 与传统方法相比,结合多种牙特征显著提高了准确性.

关键词:
人工智能的人工智能是人工智能.婴儿年龄估计婴儿年龄估计信息融合 信息融合机器学习是机器学习.物理人类学 物理人类学

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科学领域:

  • 法医人类学 法医人类学
  • 儿科牙科 儿科牙科
  • 机器学习 机器学习

背景情况:

  • 牙发育是婴儿年龄估计的可靠标记.
  • 传统的方法很难有效地整合各种牙特征.
  • 机器学习 (ML) 为提高准确性提供了一个潜在的解决方案.

研究的目的:

  • 开发和验证一种ML模型,用于使用落叶牙来估计婴儿年龄.
  • 评估各种牙特征的信息性 (长度,矿物化,膜阶段).
  • 评估结合来自多个牙和特征的信息的好处.

主要方法:

  • 利用114个婴儿骨的数据集 (怀孕5个月至3年).
  • 采用多层感知器 (MLP) 模型,是一种人工神经网络.
  • 通过一个leave-one-out交叉验证协议验证了模型.
  • 实验分析了个人和组合的牙特征.

主要成果:

  • 牙科变量的融合产生了比单个变量 (RMSE = 101天) 更准确的年龄估计 (RMSE = 66天).
  • 与使用单一的牙相比,结合多个牙显著降低了根平均平方误差 (RMSE).
  • ML方法在年龄估计方面表现出卓越的准确性和稳定性.

结论:

  • 基于ML的方法为婴儿年龄估计提供了显著的优势.
  • 整合多个牙特征和牙可以提高准确性和可靠性.
  • 这项研究强调了ML在法医和儿科年龄评估中的潜力.