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Related Concept Videos

Tooth Anatomy01:21

Tooth Anatomy

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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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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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Related Experiment Video

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Tooth segmentation and dental crowding diagnosis using two-stage dual-dilated graph convolution.

Zongsong Han1, Ning Dai2, Zhilei Wu1

  • 1The College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, People's Republic of China.

International Journal of Computer Assisted Radiology and Surgery
|October 6, 2025
PubMed
Summary

This study introduces a two-stage intelligent workflow using dual-dilated graph convolutional networks for automated tooth segmentation and dental crowding diagnosis from 3D intraoral scans, improving efficiency and accuracy in orthodontic analysis.

Keywords:
3D deep learning3D tooth segmentationComputer-aided diagnosisDental crowdingGraph convolutional networks (GCN)

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Area of Science:

  • Computer-aided dentistry
  • Orthodontic diagnostics
  • Artificial intelligence in healthcare

Background:

  • Conventional orthodontic model analysis is time-consuming and subjective.
  • Automated tooth segmentation and crowding diagnosis are crucial for efficient computer-aided analysis.
  • There is a need for intelligent and efficient approaches in orthodontics.

Purpose of the Study:

  • To propose a two-stage intelligent workflow for tooth segmentation and dental crowding severity diagnosis.
  • To develop an automated system for analyzing 3D intraoral scan models.
  • To overcome the limitations of conventional manual methods in orthodontics.

Main Methods:

  • A two-stage workflow utilizing innovative dual-dilated graph convolutional networks (DDGCNet1 and DDGCNet2).
  • Stage 1: Tooth segmentation using DDGCNet1.
  • Stage 2: Point cloud conversion and processing by DDGCNet2 for arch length discrepancy (ALD) measurement, incorporating a novel dual-dilated EdgeConv module.

Main Results:

  • The proposed network demonstrated outstanding performance in tooth segmentation.
  • Accurate diagnosis of dental crowding severity was achieved.
  • Mean absolute error (MAE) for ALD measurement was 1.553 mm (maxilla) and 1.434 mm (mandible).

Conclusions:

  • The developed system assists orthodontists in diagnosis and treatment planning.
  • It alleviates workload and expedites the creation of reliable orthodontic treatment plans.
  • The study meets the growing demands for computer-aided orthodontic diagnosis.