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Related Concept Videos

Tooth Anatomy01:21

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 grinding food.
Teeth01:15

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 and...

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Related Experiment Video

Updated: May 21, 2026

Measuring Maxillary Posterior Tooth Movement: A Model Assessment using Palatal and Dental Superimposition
07:32

Measuring Maxillary Posterior Tooth Movement: A Model Assessment using Palatal and Dental Superimposition

Published on: February 23, 2024

Deep learning-based tooth axis estimation from 3D tooth crowns using quaternion representation and multi-loss

Geunhye Kim1, Sena Lee1, Junghun Han1

  • 1Department of Precision Medicine, Yonsei University Wonju College of Medicine, Wonju, 26426, Republic of Korea.

Scientific Reports
|May 19, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning framework to estimate tooth axes using 3D crown scans, improving digital dentistry workflows. The method offers a faster, more objective alternative to traditional radiographic analysis for tooth orientation.

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Last Updated: May 21, 2026

Measuring Maxillary Posterior Tooth Movement: A Model Assessment using Palatal and Dental Superimposition
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Published on: February 23, 2024

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09:10

Precision of In Vivo Quantitative Tooth Wear Measurement Using Intra-Oral Scans

Published on: July 12, 2022

Area of Science:

  • Digital Dentistry
  • Computer-Aided Surgery
  • Machine Learning

Background:

  • Accurate tooth axis estimation is crucial for digital dental workflows like orthodontics and prosthetics.
  • Current methods rely on subjective and time-consuming manual analysis of crown morphology and radiographs.
  • Cone-beam computed tomography (CBCT) provides anatomical tooth axes but has limitations including radiation exposure and cost.

Purpose of the Study:

  • To develop and validate a deep learning framework for automated tooth axis estimation using only 3D tooth crown point cloud data.
  • To explore the feasibility of approximating clinically relevant tooth axes from crown geometry alone, as an alternative to root-based methods.
  • To provide a supportive geometric reference for crown-based digital dentistry workflows.

Main Methods:

  • A deep learning framework utilizing geometric information from 3D tooth crown point clouds.
  • Preprocessing steps include centering, absolute orientation alignment, and rotation-based data augmentation.
  • A quaternion-based rotation regression model estimates the tooth axis, optimized with a composite loss function for consistency.
  • Medial-distal orientation alignment is applied during inference for directional consistency.

Main Results:

  • The proposed method achieved an average angular error of 3.23° (standard deviation: 2.06°).
  • Consistent Chamfer distance measures were observed, indicating geometric accuracy.
  • Qualitative analysis showed consistent directional patterns of predicted tooth axes at the full-arch level.

Conclusions:

  • Crown surface geometry, when combined with alignment-based preprocessing, can effectively approximate crown-based tooth axes.
  • The deep learning framework offers a viable, automated approach for tooth axis estimation in settings with limited information.
  • This method serves as a supportive geometric reference, enhancing crown-based digital dentistry workflows without replacing anatomical tooth axes.