Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Effect of mobile applications in oral health promotion among elderly people: a systematic review and meta-analysis.

BMC oral health·2026
Same author

Prognosis of intentional replantation for periapical periodontitis teeth: a systematic review and meta-analysis.

BMC oral health·2025
Same author

Exploring the capabilities of GenAI for oral cancer consultations in remote consultations : Author.

BMC oral health·2025
Same author

Utilizing GPT-4 to interpret oral mucosal disease photographs for structured report generation.

Scientific reports·2025
Same author

Automatic classification and segmentation of multiclass jaw lesions in cone-beam CT using deep learning.

Dento maxillo facial radiology·2024
Same author

A Stage-Wise Residual Attention Generation Adversarial Network for Mandibular Defect Repairing and Reconstruction.

International journal of neural systems·2024

Related Experiment Video

Updated: May 27, 2025

Three-Dimensional Reconstruction of Orbital Fractures
08:18

Three-Dimensional Reconstruction of Orbital Fractures

Published on: May 16, 2025

108

3D Deep Learning for Virtual Orbital Defect Reconstruction: A Precise and Automated Approach.

Fangfang Yu1, Chang Liu1, Chenglan Zhong2

  • 1State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology.

The Journal of Craniofacial Surgery
|February 17, 2025
PubMed
Summary

A new 3D U-Net+++ model precisely reconstructs orbital defects, offering a fast and automated solution for both unilateral and bilateral cases. This AI-driven approach significantly improves virtual surgical planning for complex orbital fractures.

Keywords:
Computer-aided designcraniomaxillofacialdeep learningorbital reconstructionvirtual surgical planning

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.6K
Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

1.7K

Related Experiment Videos

Last Updated: May 27, 2025

Three-Dimensional Reconstruction of Orbital Fractures
08:18

Three-Dimensional Reconstruction of Orbital Fractures

Published on: May 16, 2025

108
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.6K
Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

1.7K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Computer-Aided Surgery

Background:

  • Accurate virtual orbital reconstruction is vital for preoperative planning.
  • Traditional methods like mirroring are inadequate for complex orbital defects and are inefficient.
  • Existing techniques struggle with defects crossing the midline.

Purpose of the Study:

  • To introduce a modified 3D U-Net+++ architecture for precise and automated orbital defect reconstruction.
  • To overcome limitations of traditional methods in handling complex and bilateral orbital defects.
  • To enhance the accuracy and efficiency of virtual surgical planning.

Main Methods:

  • Developed and trained a modified 3D U-Net+++ model on 300 synthetic orbital defects from CT scans.
  • Validated the model on 15 clinical cases of orbital fractures.
  • Evaluated performance using quantitative metrics (HD95, ASSD, Surface DSC, PSNR, SSIM) and surgeon assessments (Likert scale).

Main Results:

  • Achieved high accuracy on synthetic data (HD95 < 2.0 mm, Surface DSC > 0.94) and outperformed other networks.
  • Clinical validation showed excellent surgeon ratings (>4/5) for structural integrity, edge consistency, and morphology.
  • Demonstrated high precision for clinical defects (HD95 ~ 2.5 mm, Surface DSC > 0.91) with rapid processing (~10 seconds/case).

Conclusions:

  • The modified 3D U-Net+++ provides a precise and highly automated solution for orbital defect reconstruction.
  • This method is particularly effective for challenging bilateral and trans-midline orbital defects.
  • The approach promises significant improvements in clinical practice and preoperative planning for orbital surgery.