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Efficient complete denture metal base design via a dental feature-driven segmentation network.

Cheng Li1, Yaming Jin2, Yunhan Du2

  • 1Center of Digital Dentistry, Faculty of Prosthodontics, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, No.22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, PR China.

Computers in Biology and Medicine
|May 3, 2024
PubMed
Summary

A new AI framework, CDMB-SegNet, automates the design of complete denture metal bases (CDMB), improving accuracy and personalization. This significantly reduces design time, offering a more efficient and clinically applicable solution for edentulous patients.

Keywords:
Complete dentureComputer-aided designDental featureMetal baseSegmentation network

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Area of Science:

  • Biomedical Engineering
  • Computer Science
  • Dental Materials

Background:

  • Automated design of complete denture metal bases (CDMB) is challenging due to manual complexities, low personalization, and accuracy issues.
  • Successful CDMB restoration relies on precise design of major connectors and retentive mesh.

Purpose of the Study:

  • To develop an automated, personalized digital design framework for CDMB using a segmentation network.
  • To address limitations in manual CDMB design, enhancing accuracy and patient-specific fit.

Main Methods:

  • Developed CDMB-SegNet, a computer-aided framework incorporating an upright-orientation adjustment module (UO-AM) and a boundary-optimization design module (BO-DM).
  • Utilized a dental feature-driven segmentation network with a light-weight backbone for efficient 3D model processing.
  • UO-AM identifies spatial attitude; BO-DM refines designs for smoother, personalized outcomes.

Main Results:

  • CDMB-SegNet demonstrated superior performance compared to state-of-the-art methods on a large clinical dataset.
  • Achieved high Accuracy (98.54%/97.73%) and IoU (87.42%/70.42%) for major connectors/retentive mesh.
  • Significantly reduced design time to approximately 4 minutes, a tenfold improvement.

Conclusions:

  • CDMB-SegNet is the first AI network for automatic CDMB design using oral big data and dental features.
  • The AI-designed CDMB offers enhanced clinical applicability by coupling with patient-specific dental anatomy.
  • This automated approach provides more personalized and accurate solutions for complete denture restorations.