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Related Concept Videos

Teeth01:15

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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.
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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.
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German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
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A relation-based framework for effective teeth recognition on dental periapical X-rays.

Kailai Zhang1, Hu Chen2, Peijun Lyu2

  • 1Department of Electronic Engineering, Tsinghua University, Beijing, China.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|December 9, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel relation-based framework for automated teeth recognition in dental X-rays, improving both location and classification accuracy. The method enhances diagnostic support for dentists by overcoming deep learning limitations with limited data.

Keywords:
Convolutional neural networkLabel reconstructionProposal correlation moduleTeeth recognition

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Computer Vision

Background:

  • Dental periapical X-rays are crucial for diagnosis.
  • Automated teeth recognition aids dentists but faces challenges with data scarcity and complex positioning.
  • Deep learning, particularly Convolutional Neural Networks (CNNs), shows promise but requires adaptation for this task.

Purpose of the Study:

  • To develop an automated system for teeth location and classification in dental periapical X-rays.
  • To enhance diagnostic support for dentists using artificial intelligence.
  • To address the limitations of standard CNNs in dental image analysis.

Main Methods:

  • A relation-based framework integrating prior dental knowledge was proposed.
  • A multi-task CNN was employed for teeth classification and location.
  • Specialized techniques included label reconstruction, a proposal correlation module, and a teeth sequence refinement module.

Main Results:

  • The relation-based framework significantly improved teeth classification and location performance.
  • The proposed method outperformed direct applications of established detection architectures.
  • High accuracy in identifying and classifying teeth was achieved.

Conclusions:

  • The developed framework offers a robust solution for automated teeth recognition in dental radiography.
  • This approach provides reliable teeth information, enabling effective automated diagnostic support for dental professionals.
  • The integration of domain knowledge into deep learning models is effective for specialized medical imaging tasks.