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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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Related Experiment Video

Updated: Jun 27, 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

A data-driven approach for the partial reconstruction of individual human molar teeth using generative deep learning.

Alexander Broll^1,2, Martin Rosentritt¹, Thomas Schlegl²

¹Department of Prosthetic Dentistry, University Hospital Regensburg, Regensburg, Germany.

Frontiers in Artificial Intelligence

|May 1, 2024

Summary

This study introduces a data-driven approach using StyleGAN-2 to reconstruct dental occlusal surfaces, improving restoration fabrication. The Generative Adversarial Network (GAN) method offers accurate and preferred outcomes for dental inlays.

Keywords:

StyleGAN dental prosthesis design digital dentistry inlay restoration machine learning

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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

Area of Science:

Biomedical Engineering
Computer Science
Materials Science

Background:

Dental caries necessitate frequent restorations, but fabrication is complex and time-consuming.
Current methods for fixed dental prostheses (FDPs) often require costly adaptations.
A data-driven approach can optimize occlusal surface reconstruction for better dental restorations.

Purpose of the Study:

To develop and evaluate a data-driven method for partial reconstruction of occlusal surfaces.
To leverage Generative Adversarial Networks (GANs), specifically StyleGAN-2, for dental restoration.
To assess the accuracy and clinical preference of GAN-generated inlays.

Main Methods:

Utilized a dataset of 92 3D mesh files of dental crown restorations.
Employed StyleGAN-2 for generating occlusal surfaces via a 2D projection method.
Applied Bayesian Image Reconstruction for inlay type reconstruction and evaluated using Root Mean Square Error (RMSE).

Main Results:

The reconstruction process achieved satisfactory visual and quantitative results with RMSE between 0.02 mm and 0.18 mm.
Dentists preferred GAN-based restorations over CAD fabricated inlays for 3 out of 4 inlay geometries.
The method demonstrated accuracy and efficiency in reconstructing various inlay types.

Conclusions:

The StyleGAN architecture effectively reconstructs various inlay geometries.
The independence of reconstruction from GAN training allows application to arbitrary designs without retraining.
This data-driven approach shows promise for improving the efficiency and quality of dental restorations.