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Related Concept Videos

Tooth Anatomy01:21

Tooth Anatomy

510
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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Autologous Transplantation Tooth Guide Design Based on Deep Learning.

Lifen Wei1, Shuyang Wu2, Zelun Huang1

  • 1Department of Dental Implantation, Affiliated Stomatology Hospital of Guangzhou Medical University, Guangdong Engineering Research Center of Oral Restoration and Reconstruction, Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangzhou, Guangdong, China.

Journal of Oral and Maxillofacial Surgery : Official Journal of the American Association of Oral and Maxillofacial Surgeons
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Summary

Deep learning significantly reduces time costs for autologous tooth transplantation surgical guide design. This AI approach matches senior surgeon accuracy, improving efficiency in dental procedures.

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Area of Science:

  • Biomedical Engineering
  • Dental Surgery
  • Artificial Intelligence in Medicine

Background:

  • Autologous tooth transplantation requires precise surgical guides, traditionally designed manually from cone-beam computed tomography (CBCT) scans.
  • Manual design is labor-intensive, time-consuming, and prone to human error.
  • Deep learning (DL) offers potential for automated, accurate, and efficient surgical guide design, but its application in this field is unexplored.

Purpose of the Study:

  • To evaluate the feasibility of replacing manual surgical guide design with a DL-enabled pipeline for autologous tooth transplantation.
  • To compare the accuracy and efficiency of DL-based design against traditional manual methods.

Main Methods:

  • A retrospective cross-sectional study utilizing 79 CBCT scans.
  • Extraction and preprocessing of 5,070 region-of-interest images.
  • Comparison of DL-based design pipeline with manual design pipelines (senior and junior dentists) using trueness (RMS) and accuracy (standard deviation), and time cost.

Main Results:

  • The DL-based pipeline achieved the lowest root mean square (RMS) error (0.335 ± 0.066 mm), indicating high trueness.
  • No significant difference in RMS or standard deviation was found between DL and senior dentist manual design.
  • The DL pipeline dramatically reduced design time (0.017 ± 0.001 minutes) compared to manual methods (senior: 19.676 ± 2.386 minutes; junior: 30.613 ± 6.571 minutes).

Conclusions:

  • A DL-based automatic pipeline is feasible for autologous tooth transplantation surgical guide design.
  • The DL approach demonstrates comparable accuracy to senior clinicians while significantly reducing design time.
  • This AI-driven method offers a more efficient alternative to traditional manual surgical guide design in dentistry.