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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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Randomized comparative crossover clinical study of mechanical and optical behavior, and oral health-related quality of life of CAD-CAM milled and 3D-printed complete-arch implant-supported interim restorations.

Journal of prosthodontics : official journal of the American College of Prosthodontists·2026
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Implant scanning workflow combining extraoral photogrammetry and the reverse scanning method for fabricating implant-supported prostheses.

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Impact of number of maxillary fixation landmarks on the registration accuracy of an implant scanning workflow.

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Influence of ambient light color temperature and illuminance on the accuracy of 3-dimensional patient representation using different facial scanning technologies: A clinical study.

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Influence of Implant Angulation on the Accuracy of Complete Arch Digital Scans with Intraoral Photogrammetry.

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Deep learning applications in prosthodontics: A systematic review.

Rata Rokhshad1, Kamyar Khosravi2, Parisa Motie3

  • 1Resident, Department of Pediatric Dentistry, School of Dentistry, Loma Linda University, Loma Linda, Calif.

The Journal of Prosthetic Dentistry
|May 14, 2025
PubMed
Summary
This summary is machine-generated.

Deep learning shows great promise in dental prosthodontics, particularly for prosthesis design and manufacturing. Standardization and validation are crucial for widespread clinical use of these advanced deep learning techniques.

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Area of Science:

  • Dentistry
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Deep learning (DL) applications are emerging in dentistry for diagnosis, treatment planning, and prosthesis fabrication.
  • A comprehensive overview of DL applications specifically within prosthodontics is currently lacking.

Purpose of the Study:

  • To systematically review DL applications in dental prosthodontics for restorations like inlays, onlays, crowns, and fixed dental prostheses (FDPs).
  • To evaluate DL's role in predicting restoration outcomes, optimizing prosthetic design, aiding treatment planning, improving color matching, and automating landmark detection for RPD and CD treatments.

Main Methods:

  • A systematic review of 6 databases (PubMed, EMBASE, Scopus, Web of Science, arXiv, IEEE) and Google Scholar, supplemented by manual searches.
  • Independent evaluation of 31 eligible studies using the JBI Critical Appraisal checklist, with a third examiner resolving discrepancies.
  • Classification of studies based on DL application: restoration identification, outcome prediction, treatment planning, design/manufacturing, shade matching, RPD planning, and facial change analysis.

Main Results:

  • The review included 31 studies, with 10 showing a low risk of bias. Most studies (n=13) focused on prosthesis design and manufacturing, predominantly using generative DL tasks (n=11).
  • Convolutional Neural Networks (CNNs) were the most common model (n=11), followed by Generative Adversarial Networks (GANs) (n=7). Tooth-supported crowns were the most assessed restoration (n=14).
  • Intraoral scanners (IOSs) were the primary data source (n=16). Reported accuracies for restoration identification varied (99.4% for gold, 60% for onlays), with sensitivity from 88.6% to 100% and IoU from 60% to 90%.

Conclusions:

  • Deep learning demonstrates significant potential in prosthodontics, especially in prosthesis design and manufacturing.
  • Standardization of methodologies and rigorous validation are essential for the reliable clinical adoption of DL-driven approaches in prosthodontics.