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Related Concept Videos

Teeth01:15

Teeth

336
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...
336

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Updated: Jun 10, 2025

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
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Feasibility of using two generative AI models for teeth reconstruction.

O Saleh1, B C Spies2, L S Brandenburg3

  • 1Department of Prosthetic Dentistry, Faculty of Medicine, Medical Center -University of Freiburg, Center for Dental Medicine, University of Freiburg, Freiburg, Germany; Prosthodontics Division, Department of Restorative Sciences & Biomaterials, Boston University Henry M. Goldman School of Dental Medicine, Boston, MA, USA.

Journal of Dentistry
|October 18, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence models, transformer-based (TM) and diffusion-based (DM), show promise for reconstructing missing teeth. Both models achieved similar accuracy, with potential to improve dental restoration processes.

Keywords:
Artificial intelligenceDiffusion modelGenerative AIImplant planningTransformers

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Area of Science:

  • Dental Technology
  • Artificial Intelligence in Medicine
  • Biomedical Engineering

Background:

  • Reconstructing missing teeth is crucial for oral function and aesthetics.
  • Current methods for dental restoration can be time-consuming and costly.
  • Advancements in artificial intelligence offer new possibilities for improving dental procedures.

Purpose of the Study:

  • To investigate the feasibility of using transformer-based (TM) and diffusion-based (DM) AI models for reconstructing single and multiple missing teeth.
  • To assess and compare the accuracy of TM and DM models in dental reconstruction.
  • To evaluate the potential of AI in streamlining dental restoration processes.

Main Methods:

  • A dataset of 129 digitized dental models was used to train TM and DM models.
  • Artificial missing teeth were generated to test reconstruction accuracy.
  • Root Mean Square (RMS) and mean absolute error (MAE) were employed to evaluate performance against ground truth data.
  • Paired t-tests were conducted to compare the accuracy of the two AI models.

Main Results:

  • Both TM and DM models demonstrated comparable accuracy in reconstructing single and multiple missing teeth.
  • The highest reconstruction disparity was observed when reconstructing all teeth, excluding specific incisors.
  • TM showed highest precision for tooth 34 reconstruction (RMS: 0.21), while DM excelled in reconstructing tooth 21 (RMS: 0.19).
  • No statistically significant difference in accuracy was found between the TM and DM models.

Conclusions:

  • AI-based TM and DM models show potential for accurate missing tooth reconstruction.
  • Reconstruction accuracy is superior for single missing teeth compared to multiple edentulous spaces.
  • Further refinement with larger datasets and inclusion of antagonistic teeth is recommended for enhanced clinical application and precision in implant planning.