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

MAP4K4 inhibition enhances the anti-ferroptosis effect of the FTO/YAP/ATF4 axis in doxorubicin-induced cardiotoxicity.

Cell biology and toxicology·2026
Same author

Terahertz polarization sensing with a four-level cesium Rydberg-atom system.

Optics express·2026
Same author

Crystallization regulation and electrochemical optimization of free-standing carbon nanofiber-confined vanadium oxide nanodots for advanced flexible zinc ion batteries.

Nanoscale·2026
Same author

Formation Mechanism and Dielectric Properties of Ultra-High-Voltage Anodic Al Foils Investigated by ReaxFF-MD and DFT.

Materials (Basel, Switzerland)·2026
Same author

Sphingolipid homeostasis and dysregulation in liver function and disease.

Life metabolism·2026
Same author

Differential Associations of Human Herpesviruses With Oral Bacteria and Periodontitis Severity: A Cross-Sectional Analysis.

International dental journal·2026

Related Experiment Video

Updated: Jan 11, 2026

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

2.2K

A Mandibular Defect Dataset for Autonomous Reconstruction Planning in Oral and Maxillofacial Surgery.

Jinyang Wu1,2,3, Lai Jiang1,2,3, Liangjing Shao4,5

  • 1Department of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Scientific Data
|November 10, 2025
PubMed
Summary

This study introduces the first clinical dataset for mandibular defect reconstruction, crucial for advancing AI algorithms in oral surgery. The dataset enhances AI model generalizability for better patient outcomes.

More Related Videos

A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible
10:42

A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible

Published on: January 28, 2020

6.9K
Guided Endodontics: Three-Dimensional Planning and Template-Aided Preparation of Endodontic Access Cavities
07:14

Guided Endodontics: Three-Dimensional Planning and Template-Aided Preparation of Endodontic Access Cavities

Published on: May 24, 2022

5.0K

Related Experiment Videos

Last Updated: Jan 11, 2026

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

2.2K
A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible
10:42

A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible

Published on: January 28, 2020

6.9K
Guided Endodontics: Three-Dimensional Planning and Template-Aided Preparation of Endodontic Access Cavities
07:14

Guided Endodontics: Three-Dimensional Planning and Template-Aided Preparation of Endodontic Access Cavities

Published on: May 24, 2022

5.0K

Area of Science:

  • Oral and Maxillofacial Surgery
  • Artificial Intelligence in Medicine
  • Medical Imaging and Data Science

Background:

  • Deep learning algorithms show promise for mandibular defect reconstruction in oral and maxillofacial surgery.
  • High-quality datasets are essential for developing effective deep learning models but are currently a bottleneck.
  • Existing datasets often lack the clinical complexity and diversity needed for robust AI development.

Purpose of the Study:

  • To introduce the first clinically derived Mandibular Defect Dataset.
  • To provide a valuable resource for training and validating AI models for mandibular reconstruction.
  • To support future clinical research by including detailed defect information.

Main Methods:

  • Compilation of 147 manually annotated models of various mandibular defects.
  • Annotation by experienced surgeons adhering to strict clinical standards.
  • Inclusion of HCL classification diagnoses and relevant information for each defect model.

Main Results:

  • The dataset accurately represents complex clinical defect boundaries and patient-specific anatomy.
  • It offers a diverse range of mandibular defects, unlike previous datasets.
  • The dataset facilitates the development of more generalizable and adaptable AI models.

Conclusions:

  • The Mandibular Defect Dataset is a significant advancement for AI in oral and maxillofacial surgery.
  • It addresses the critical need for high-quality, clinically relevant data.
  • This resource will accelerate the development of AI-driven solutions for mandibular reconstruction.