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Related Concept Videos

Bone Remodeling01:40

Bone Remodeling

Bone remodeling is a continuous and balanced process of bone resorption by osteoclasts and bone formation by osteoblasts. In adults, it helps maintain bone mass and calcium homeostasis. While mechanical stress can stimulate turnover as part of the normal maintenance and reparative process, several hormones also regulate bone remodeling.
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Treatment for a fracture is based on the type of break, the bone affected, and the patient's age.
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Related Experiment Video

Updated: Jun 19, 2026

Designing CAD/CAM Surgical Guides for Maxillary Reconstruction Using an In-house Approach
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An Automated Framework for Mandibular Reconstruction: Evaluation and Clinical Application.

Chenyao Li1, Yan Guo2, Rong Yang3

  • 1Department of Oral Surgery, Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, and Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai, China.

Head & Neck
|March 6, 2026
PubMed
Summary

This study introduces an automated framework for mandibular reconstruction, improving accuracy and efficiency. The AI-driven approach significantly reduces planning time and enhances bone contact compared to manual methods.

Keywords:
automated surgical planningcomputer‐assisted surgeryfibula flapmandibular reconstructionmaxillofacial surgery

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Area of Science:

  • Medical imaging and artificial intelligence
  • 3D deep learning for surgical planning
  • Statistical shape modeling in craniofacial surgery

Background:

  • Mandibular reconstruction faces challenges with automation and objectivity.
  • Current manual planning methods are time-consuming and subjective.
  • Need for enhanced efficiency and accuracy in reconstructive surgery.

Purpose of the Study:

  • To develop an automated framework for mandibular reconstruction.
  • To improve objectivity and efficiency in surgical planning.
  • To evaluate the performance of the automated method against manual planning.

Main Methods:

  • A novel approach combining statistical shape modeling and 3D deep learning.
  • Training on 200 CT scans and validation in 80 clinical cases.
  • Comparison of automated vs. manual planning based on accuracy, bone contact, and time.

Main Results:

  • Automated method achieved high accuracy (DSC: 0.874) and real-time efficiency.
  • Significantly improved bone contact area (106.2 vs. 94.6 mm²).
  • Reduced planning time by 85% with excellent postoperative implant alignment (2.1 mm deviation).

Conclusions:

  • The automated framework enhances mandibular reconstruction efficiency and quality.
  • The method meets clinical needs for improved surgical outcomes.
  • Future work will incorporate biomechanical considerations for further optimization.