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Related Concept Videos

Tooth Anatomy01:21

Tooth Anatomy

566
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...
566
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...
507

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Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
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Robust automated teeth identification from dental radiographs using deep learning.

Mingming Xu1, Yujia Wu1, Zineng Xu2

  • 1Beijing Laboratory of Biomedical Materials, Department of Geriatric Dentistry, Peking, University School and Hospital of Stomatology, Beijing 100081, China.

Journal of Dentistry
|July 8, 2023
PubMed
Summary
This summary is machine-generated.

A new deep learning method accurately segments and numbers teeth in panoramic radiographs for all dentition stages. This automated teeth identification achieves expert-level performance, aiding dental diagnostics.

Keywords:
Deep learningDental image interpretationPanoramic radiographTeeth identification

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Area of Science:

  • Artificial Intelligence in Dentistry
  • Medical Imaging Analysis
  • Deep Learning for Dental Radiography

Background:

  • Panoramic radiographs are crucial for dental diagnostics but manual interpretation is time-consuming.
  • Accurate identification and numbering of teeth are fundamental for various dental procedures.
  • Existing automated methods may struggle with diverse dentitions and complex cases.

Purpose of the Study:

  • To develop and validate a deep learning-based method for automatic teeth segmentation and numbering.
  • To assess the algorithm's performance across primary, mixed, and permanent dentitions.
  • To evaluate the algorithm's robustness in handling real-world dental complexities.

Main Methods:

  • A dataset of 6,046 panoramic radiographs with diverse dentitions and abnormalities was curated.
  • A two-stage deep learning model (U-Net and Hybrid Task Cascade) was employed for teeth identification.
  • The algorithm was trained, validated, and tested on distinct subsets of the annotated dataset.

Main Results:

  • The deep learning algorithm achieved high precision and recall (>97%) for teeth segmentation and numbering.
  • Intersection-over-Union (IoU) between predicted and ground truth segmentations reached 92%.
  • The method demonstrated strong generalization across all dentition stages and complex clinical scenarios.

Conclusions:

  • The developed deep learning algorithm provides automated teeth identification comparable to dental experts.
  • This robust algorithm can assist in clinical interpretation of panoramic radiographs.
  • It holds potential for future automated dental diagnosis and treatment systems.