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Tooth Anatomy

Tooth Anatomy

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: Jan 10, 2026

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

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Published on: August 5, 2021

A fully automated database-driven framework for interproximal tooth morphology reconstruction in orthodontics.

Zhilei Wu¹, Zongsong Han¹, Ning Dai¹

¹College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, P. R. China.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of Engineering in Medicine

|November 20, 2025

Summary

This study introduces an automated framework for reconstructing 3D tooth models, improving accuracy in digital orthodontics. The new method overcomes limitations of manual interventions for precise dental restorations.

Keywords:

Laplacian deformation automated reconstruction digital orthodontics multi-view CNN parametric modeling template retrieval tooth morphology reconstruction

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Guided Endodontics: Three-Dimensional Planning and Template-Aided Preparation of Endodontic Access Cavities

Guided Endodontics: Three-Dimensional Planning and Template-Aided Preparation of Endodontic Access Cavities

Published on: May 24, 2022

Area of Science:

Biomedical Engineering
Computer Science

Background:

Intraoral scans generate 3D tooth models with defects like inter-tooth adhesion due to crown overlap and low scanner resolution.
These defects obscure interproximal tooth morphology and interdental spaces, compromising orthodontic treatment accuracy.
Current reconstruction methods require extensive manual input, hindering clinical efficiency.

Purpose of the Study:

To develop a fully automated, database-driven framework for reconstructing missing tooth morphology.
To enhance the accuracy and clinical efficiency of 3D tooth model generation for digital orthodontics.

Main Methods:

Constructed a parametric tooth database using multi-view convolutional neural networks (MVCNN) for 3D morphology feature encoding.
Implemented a coarse-to-fine localization strategy for automated localization with sub-millimeter accuracy.
Restored missing morphology using iterative Laplacian deformation and reconstructed root anatomy with B-spline modeling.

Main Results:

Achieved a root mean square surface distance of less than 0.096 mm on clinical cases.
Outperformed existing state-of-the-art approaches in accuracy.
Demonstrated efficient and precise fully automated tooth reconstruction.

Conclusions:

The proposed framework offers a clinically viable solution for accurate 3D tooth reconstruction in digital orthodontics.
Automated reconstruction significantly improves efficiency and precision compared to manual methods.
This technology has the potential to advance orthodontic treatment planning and execution.