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Updated: Sep 14, 2025

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
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Accuracy and Time Efficiency of Artificial Intelligence-Driven Tooth Segmentation on CBCT Images: A Validation Study

Panagiotis Ntovas1,2,3, Piyarat Sirirattanagool1, Praewvanit Asavanamuang1

  • 1Department of Prosthodontics, School of Dental Medicine, Tufts University, Boston, Massachusetts, USA.

Clinical Oral Implants Research
|July 19, 2025
PubMed
Summary

Artificial intelligence (AI)-driven tooth segmentation offers comparable accuracy to manual methods in cone-beam computed tomography (CBCT) imaging while significantly improving time efficiency for creating virtual 3D tooth models.

Keywords:
CBCTaccuracyartificial intelligencedeep learningimplant dentistrytooth segmentationvirtual model

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Area of Science:

  • Dental imaging and diagnostics
  • Artificial intelligence in medicine
  • 3D modeling and visualization

Background:

  • Accurate tooth segmentation is crucial for dental diagnostics and treatment planning, particularly in implantology.
  • Manual segmentation of teeth from CBCT scans is time-consuming and requires significant expertise.
  • AI tools integrated into implant planning software offer potential for automating and improving segmentation processes.

Purpose of the Study:

  • To compare the accuracy and time efficiency of manual versus AI-driven tooth segmentation on CBCT images.
  • To evaluate the influence of artifacts, dental arch, tooth type, and region on segmentation accuracy.
  • To assess the reliability of AI-driven tools for generating virtual 3D tooth models.

Main Methods:

  • Fourteen patients underwent CBCT scans, yielding 67 extracted teeth for segmentation.
  • Segmentation was performed manually and using two AI-driven tools.
  • Segmentation time was recorded, and volumetric discrepancies were calculated by comparing segmented models to intraoral scans.

Main Results:

  • AI-driven segmentation showed discrepancies ranging from 0.10 to 0.98 mm (mean RMS 0.27 mm).
  • Manual segmentation yielded lower RMS deviation than AI methods (p < 0.05), with the apical root region showing lowest accuracy.
  • AI methods significantly reduced segmentation time compared to manual methods (p < 0.05).

Conclusions:

  • AI-driven tooth segmentation provides reliable virtual 3D models with accuracy comparable to manual methods.
  • AI significantly enhances time efficiency in tooth segmentation for CBCT imaging.
  • Further AI model development is needed to improve accuracy in cases with restoration artifacts.