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Teeth

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...

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Updated: Jun 26, 2025

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

机器学习辅助的5部分牙细分方法用于基于CBCT的成年人牙年龄估计.

R Merdietio Boedi¹, S Shepherd², F Oscandar³

¹Department of Dentistry, Faculty of Medicine, Universitas Diponegoro, Semarang, Indonesia.

The Journal of forensic odonto-stomatology

|May 14, 2024

概括

这项研究使用圆束计算机断层扫描 (CBCT) 和机器学习来估计成人牙科年龄. 最好的模型使用了大侧切口,达到4.86年的平均误差.

关键词:

通过牙来确定年龄圆束计算机断层扫描计算机断层扫描法医牙科法医牙科监督机器学习的监督机器学习

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Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

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Published on: February 23, 2024

A Finite Element Approach for Locating the Center of Resistance of Maxillary Teeth

A Finite Element Approach for Locating the Center of Resistance of Maxillary Teeth

Published on: April 8, 2020

相关实验视频

Last Updated: Jun 26, 2025

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

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Published on: August 5, 2021

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

A Finite Element Approach for Locating the Center of Resistance of Maxillary Teeth

A Finite Element Approach for Locating the Center of Resistance of Maxillary Teeth

Published on: April 8, 2020

科学领域:

法医牙科法医牙科
放射学放射学是一门学科.
生物识别信息生物识别信息

背景情况:

在成年人中使用形光束计算机断层扫描 (CBCT) 的体积数据进行牙科年龄估计 (DAE) 是一个不断发展的领域.
五部分牙分割 (SG) 方法提高了DAE的准确性.
对于DAE,研究了监督机器学习模型.

研究的目的:

用CBCT数据评估成人DAE监督机器学习模型的有效性.
将支持向量回归 (SVR) 和回归树模型与多重线性回归进行比较.
评估体积牙测量和性别作为时间年龄预测指标的有用性.

主要方法:

分析了99名患者 (20-59.99岁) 的CBCT扫描.
八十颗牙 (大,侧切口,中央切口) 被细分.
乳-牙体积比率,肉-牙体积比率,牙体积比率和性别被用作独立变量.

主要成果:

在预测变量中没有检测到多线性.
使用大侧切口的多项式内核的支向量回归 (SVR) 产生了最佳性能 (R2 = 0.73).
最佳模型的平均平均误差为4.86年,根平均平方误差为6.05年,尽管细分复杂且耗时.

结论:

机器学习,特别是具有多项式内核的SVR,对使用CBCT体积数据的成人DAE显示出希望.
大侧切口器是这个人群中时间年龄的有效预测器.
需要进一步改进,以优化细分技术并减少临床应用的劳动时间.