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Related Experiment Video

Updated: Jun 30, 2026

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images
05:49

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Published on: February 23, 2024

Accuracy and Functional Performance of Artificial Intelligence-Based Automated Crown Design Systems: A Systematic

Ahmed A Holiel1,2, Mounir M Al Nakouzi2, Carlos Enrique Cuevas-Suárez3,4

  • 1Department of Conservative Dentistry, Faculty of Dentistry, Alexandria University, Alexandria, Egypt.

Biomedical Engineering and Computational Biology
|June 29, 2026
PubMed
Summary

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This summary is machine-generated.

Artificial intelligence (AI) in dentistry offers precise crown design, improving workflow efficiency and accuracy. While promising, further clinical validation is needed to confirm superiority over traditional methods.

Area of Science:

  • Digital Dentistry and Restorative Treatments
  • Artificial Intelligence in Healthcare Applications

Background:

  • AI-driven automated crown design is revolutionizing digital restorative dentistry.
  • Systems utilize machine learning (ML), deep learning (DL), generative adversarial networks (GANs), and diffusion models for precise crown fabrication.

Purpose of the Study:

  • To systematically review and meta-analyze AI-assisted crown design systems.
  • To compare AI systems against computer-aided design (CAD) and technician-driven workflows for accuracy and efficiency.

Main Methods:

  • Comprehensive literature search across major databases (PubMed, Scopus, Web of Science, Embase, Cochrane Library) up to March 2026.
  • Inclusion of in vitro, in silico, and clinical studies evaluating AI vs. conventional crown design.
  • Primary outcomes: morphological accuracy (RMS deviation), occlusal integration, internal fit; Secondary outcomes: marginal adaptation, design time.
Keywords:
CAD/CAMartificial intelligencecrown designdeep learningdental morphologydigital dentistrygenerative adversarial networksocclusal analysis

Related Experiment Videos

Last Updated: Jun 30, 2026

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images
05:49

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Published on: February 23, 2024

Main Results:

  • AI systems demonstrated clinically acceptable morphological accuracy, internal fit, and occlusal contact reproduction (RMS deviation: SMD = -0.15).
  • Significant workflow efficiency gains: 25-50% reduction in design time and enhanced precision in marginal gaps (p < 0.001).
  • DL and GAN platforms produced crowns within acceptable deviation ranges (<100-200 μm); human-AI collaboration improved complex case outcomes.

Conclusions:

  • AI-assisted crown design shows potential for reproducible, accurate, and efficient restorations, reducing operator dependency.
  • Current evidence is largely from in vitro/computational studies, lacking extensive clinical validation and long-term follow-up.
  • Further prospective trials are necessary to confirm long-term clinical efficacy and superiority over conventional methods.