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Related Concept Videos

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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Tooth Anatomy01:21

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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...
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Updated: Jun 10, 2025

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
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Dental Composite Performance Prediction Using Artificial Intelligence.

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
Summary
This summary is machine-generated.

Artificial intelligence (AI) and machine learning (ML) models can predict dental composite performance. Different ML models accurately forecast specific outcomes like flexural modulus and shrinkage, aiding new material development.

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Area of Science:

  • Materials Science
  • Biomaterials Engineering
  • Data Science

Background:

  • Dental composites require enhanced performance and longevity.
  • Accelerating the market translation of novel dental materials is crucial.
  • Predictive modeling can optimize composite development.

Purpose of the Study:

  • To explore artificial intelligence (AI), specifically machine learning (ML), for predicting dental composite performance outcomes (POs) from composite attributes (CAs).
  • To evaluate various ML models for their efficacy in predicting key performance metrics of dental composites.

Main Methods:

  • An extensive dataset of 233 dental composite samples was compiled from over 200 publications.
  • Seventeen composite attributes (CAs) and seven performance outcomes (POs) were analyzed.
  • Nine ML models were assessed for classification-based PO prediction, and five for regression analysis.

Main Results:

  • K-Nearest Neighbors (KNN) excelled in predicting flexural modulus (FlexMod).
  • Decision Tree models were optimal for flexural strength (FlexStr) and volumetric shrinkage (ShrinkV).
  • Logistic Regression and Support Vector Machine (SVM) models performed well for shrinkage stress (ShrinkStr).
  • Random Forest showed superior performance for FlexStr and ShrinkV via ROC AUC analysis.
  • Voting Regressor and Decision Tree Regression demonstrated effectiveness in specific regression tasks.
  • Key composite attributes influencing POs were identified, including TEGDMA, BisGMA, UDMA, depth of cure, degree of conversion, and filler loading.

Conclusions:

  • Different ML models exhibit varying strengths in predicting specific dental composite performance outcomes.
  • A comprehensive dataset and a multi-model approach are essential for training robust AI models.
  • AI-driven prediction facilitates the optimization of composite properties and supports the development of advanced dental materials.