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Related Concept Videos

Teeth01:15

Teeth

417
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...
417
Tooth Anatomy01:21

Tooth Anatomy

470
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...
470

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Related Experiment Video

Updated: Jul 2, 2025

Systematic Assessment of Mammalian Skull Specimens for Dental and Temporomandibular Joint Pathology
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Intelligently Quantifying the Entire Irregular Dental Structure.

H Liu1, J Duan2, P Zeng1

  • 1Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University and Guangdong Research Center for Dental and Cranial Rehabilitation and Material Engineering, Guangzhou, Guangdong, China.

Journal of Dental Research
|February 19, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an AI tool for precise quantitative analysis of irregular dental structures like palatal alveolar bone. The tool enhances clinical efficiency and personalized treatment planning by providing comprehensive structural insights.

Keywords:
artificial intelligencecomputer-assisted numerical analysiscone-beam computed tomographydeep learningdental equipmentoral medicine

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Area of Science:

  • Oral medicine
  • Medical imaging
  • Artificial intelligence

Background:

  • Quantitative analysis of irregular oral anatomical structures is vital but challenging for clinicians.
  • Current methods often rely on limited representative indicators, hindering comprehensive analysis.
  • Deep learning semantic segmentation shows promise but faces hurdles with unclear boundaries and landmark acquisition.

Purpose of the Study:

  • To develop an AI-powered measurement tool for the entire quantitative analysis of irregular dental structures.
  • To address segmentation challenges and establish a clinical needs-compatible measurement coordinate system.
  • To utilize lightweight networks for broader applicability and reduced computational demands.

Main Methods:

  • Proposed an AI measurement tool utilizing the lightweight LU-Net model for semantic segmentation.
  • Incorporated a compensation module to overcome segmentation difficulties caused by unclear boundaries.
  • Performed additional enamel segmentation to establish a measurement coordinate system for quantitative analysis.

Main Results:

  • Achieved high segmentation performance with Dice coefficients (0.934, 0.949) and IoU (0.888, 0.907) for palatal alveolar bone and enamel.
  • Demonstrated no statistically significant difference from ground truth measurements, with low error metrics.
  • Showed satisfactory agreement with manual measurements via Bland-Altman plots and intraclass correlation coefficients.

Conclusions:

  • The novel intelligent approach enables swift and comprehensive quantitative analysis of irregular image structures.
  • The tool facilitates personalized treatment planning, enhances clinical efficiency, and improves treatment success rates.
  • This AI tool represents a significant advancement in the quantitative assessment of dental anatomy.