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Related Concept Videos

Tooth Anatomy01:21

Tooth Anatomy

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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.
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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Improving interproximal surface reconstruction via artificial intelligence-based tooth segmentation with crown-root

Amir Salloum1, Nabil Ghosn2, Alain Tauk3

  • 1Imaging Unit, Craniofacial Research Laboratory, Faculty of Dental Medicine, Saint-Joseph University of Beirut, Beirut, Lebanon.

American Journal of Orthodontics and Dentofacial Orthopedics : Official Publication of the American Association of Orthodontists, Its Constituent Societies, and the American Board of Orthodontics
|April 22, 2026
PubMed
Summary
This summary is machine-generated.

Integrating cone-beam computed tomography (CBCT) with artificial intelligence (AI) improves intraoral scanner (IOS) tooth segmentation accuracy. Manual refinement of the interproximal surface further enhances the precision of AI-segmented dental models.

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Area of Science:

  • Dentistry
  • Digital Imaging
  • Biomedical Engineering

Background:

  • Accurate tooth segmentation is crucial for digital dental diagnosis and treatment planning.
  • Intraoral scanners (IOS) have limitations in capturing interproximal tooth surfaces, impacting segmentation accuracy.
  • Artificial intelligence (AI) and cone-beam computed tomography (CBCT) offer potential solutions to enhance dental imaging and segmentation.

Purpose of the Study:

  • To evaluate the 3D accuracy of interproximal tooth surfaces segmented using different methods.
  • To compare the accuracy of segmentation using IOS alone, AI-based IOS-CBCT fusion, and manual refinement of fused models.

Main Methods:

  • Fourteen extracted maxillary teeth were scanned using IOS and arranged in a full-arch model.
  • AI-based segmentation and fusion of IOS and CBCT data were performed on the Relu platform.
  • 3D deviation analyses were conducted on interproximal, outer, and total crown surfaces of segmented models.

Main Results:

  • Intraoral scanner (IOS) models tended to overestimate the interproximal surface compared to fused models.
  • Manually adjusted models showed significantly improved interproximal surface accuracy.
  • The total surface accuracy of fused models was significantly better than IOS-only models.

Conclusions:

  • Cone-beam computed tomography (CBCT) integration significantly improves the overall accuracy of AI-segmented dental models.
  • Localized manual adjustments at the interproximal surface further enhance the trueness of segmented dental models.