Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Teeth01:15

Teeth

339
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...
339
Tooth Anatomy01:21

Tooth Anatomy

384
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...
384

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Microbial Metabolic Strategies for Environmental Detoxification: From Enzymatic Mechanisms to Synthetic Biology and Omics.

Applied biochemistry and biotechnology·2026
Same author

TWIK-1 plays distinct roles in spinal and peripheral sensory circuits controlling mechanical sensitivity and neuropathic hypersensitivity.

Signal transduction and targeted therapy·2026
Same author

Precision nutrition and chronic disease: Integrating genomics, microbiome, and digital health for personalized dietary interventions.

Clinical nutrition ESPEN·2026
Same author

Vagal dopaminergic afferents link interoception to trigeminal pain modulation.

bioRxiv : the preprint server for biology·2026
Same author

Let-7a and miR-34a Interplay Potent Suppressive Roles in Hepatocellular Carcinoma via Co-Targeting <i>FNDC3B</i>, <i>IGF2</i> and <i>SOX4</i>.

International journal of molecular sciences·2026
Same author

Mechanical thrombectomy and decompressive hemicraniectomy trends for acute ischemic stroke: A nationwide analysis.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association·2026
Same journal

Predicting Chemotherapy Response from Staging Laparoscopy Images.

medRxiv : the preprint server for health sciences·2026
Same journal

Development and External Validation of a Machine Learning Model for 10-Year Ischemic Stroke Risk Prediction in Diverse Populations.

medRxiv : the preprint server for health sciences·2026
Same journal

MCH-Guard: Multimodal Machine Learning Framework for Risk Stratification of Cerebral Microhemorrhage Risk in the Alzheimer's Disease Neuroimaging Initiative.

medRxiv : the preprint server for health sciences·2026
Same journal

Genetic and maternal environmental contributions to estimated fetal weight at 20 weeks gestation compared with birthweight.

medRxiv : the preprint server for health sciences·2026
Same journal

Better immediate declarative memory is associated with forgetting during locomotor adaptation in chronic stroke and in older adults.

medRxiv : the preprint server for health sciences·2026
Same journal

An empirical Bayes framework for burden and dispersion association tests helps prioritize rare variants associated with Alzheimer's disease.

medRxiv : the preprint server for health sciences·2026
查看所有相关文章

相关实验视频

Updated: Jun 10, 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

780

使用人工智能预测牙科复合材料的性能.

Karla Paniagua Rivera1, Kyumin Whang2, Krishna Joshi2

  • 1Department of Electrical and Computer Engineering, the University of Texas at San Antonio, San Antonio, TX, 78249, USA.

medRxiv : the preprint server for health sciences
|October 17, 2024
PubMed
概括
此摘要是机器生成的。

人工智能 (AI) 和机器学习 (ML) 模型可以预测牙复合材料的性能. 不同的ML模型准确地预测了诸如屈曲模量和收缩等特定结果,帮助新材料的开发.

更多相关视频

Quasistatic Mechanical Testing for Computer-Aided Design and Manufacturing Occlusal Veneers Cemented to Milled Dentin Analog Material
07:42

Quasistatic Mechanical Testing for Computer-Aided Design and Manufacturing Occlusal Veneers Cemented to Milled Dentin Analog Material

Published on: December 20, 2024

319
Shrinkage of Dental Composite in Simulated Cavity Measured with Digital Image Correlation
08:45

Shrinkage of Dental Composite in Simulated Cavity Measured with Digital Image Correlation

Published on: July 21, 2014

13.4K

相关实验视频

Last Updated: Jun 10, 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

780
Quasistatic Mechanical Testing for Computer-Aided Design and Manufacturing Occlusal Veneers Cemented to Milled Dentin Analog Material
07:42

Quasistatic Mechanical Testing for Computer-Aided Design and Manufacturing Occlusal Veneers Cemented to Milled Dentin Analog Material

Published on: December 20, 2024

319
Shrinkage of Dental Composite in Simulated Cavity Measured with Digital Image Correlation
08:45

Shrinkage of Dental Composite in Simulated Cavity Measured with Digital Image Correlation

Published on: July 21, 2014

13.4K

科学领域:

  • 材料科学 材料科学 材料科学
  • 生物材料工程 生物材料工程
  • 数据科学数据科学数据科学

背景情况:

  • 牙复合材料需要提高性能和寿命.
  • 加快新型牙科材料的市场转化至关重要.
  • 预测建模可以优化复合开发.

研究的目的:

  • 探索人工智能 (AI),特别是机器学习 (ML),从复合属性 (CA) 预测牙科复合材料性能结果 (PO).
  • 评估各种ML模型的有效性,以预测牙科复合材料的关键性能指标.

主要方法:

  • 从200多个出版物中编制了233个牙科复合样本的广泛数据集.
  • 分析了17个复合属性 (CA) 和7个绩效结果 (PO).
  • 评估了9个ML模型用于基于分类的PO预测,5个用于回归分析.

主要成果:

  • K-最近邻居 (KNN) 在预测曲模量 (FlexMod) 中表现出色.
  • 决策树模型对于屈曲强度 (FlexStr) 和体积收缩 (ShrinkV) 是最优的.
  • 物流回归和支向量机 (SVM) 模型在收缩应力 (ShrinkStr) 中表现良好.
  • 随机森林通过ROC AUC分析显示了FlexStr和ShrinkV的优异性能.
  • 投票回归和决策树回归在特定的回归任务中表现出有效性.
  • 确定了影响POs的关键复合物属性,包括TEGDMA,BisGMA,UDMA,固化深度,转化程度和填充物负载.

结论:

  • 不同的ML模型在预测特定的牙复合材料性能结果方面表现出不同的强度.
  • 一个全面的数据集和多模型方法对于训练强大的AI模型至关重要.
  • 人工智能驱动的预测有助于优化复合材料的性能,并支持先进牙科材料的开发.