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

A professor/influencer perspective on science communication.

iScience·2026
Same author

Rougan Granules ameliorates liver fibrosis through autophagy regulation: Insights from HPLC-Q-TOF-MS/MS, network pharmacology, and experimental validation.

Journal of chromatography. B, Analytical technologies in the biomedical and life sciences·2026
Same author

Obstetric Violence, Nurses' and midwives' perspectives and experiences: a meta-synthesis study.

BMC health services research·2026
Same author

Nirmatrelvir/ritonavir reduced mortality in severe or critical COVID-19 patients: a multicenter retrospective cohort study.

Frontiers in medicine·2026
Same author

An Automated Framework for Mandibular Reconstruction: Evaluation and Clinical Application.

Head & neck·2026
Same author

Matrix stiffness-driven cytoskeletal remodeling and tumor progression in anaplastic thyroid cancer via integrin-focal adhesion kinase signaling.

Oncogene·2026

Related Experiment Video

Updated: Jan 15, 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

Automated Orthognathic Surgery Planning Based on Shape-Aware Morphology Prediction and Anatomy-Constrained

Yan Guo, Chenyao Li, Haitao Li

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |January 13, 2026
    PubMed
    Summary

    OrthoPlanner automates orthognathic surgical planning using a novel two-stage framework. This AI-driven approach enhances precision, reduces workload, and ensures reproducible outcomes for better functional and aesthetic results.

    More Related Videos

    Treatment of Facial Deformities using 3D Planning and Printing of Patient-Specific Implants
    07:11

    Treatment of Facial Deformities using 3D Planning and Printing of Patient-Specific Implants

    Published on: May 23, 2020

    7.9K
    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

    Related Experiment Videos

    Last Updated: Jan 15, 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
    Treatment of Facial Deformities using 3D Planning and Printing of Patient-Specific Implants
    07:11

    Treatment of Facial Deformities using 3D Planning and Printing of Patient-Specific Implants

    Published on: May 23, 2020

    7.9K
    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

    Area of Science:

    • Medical Artificial Intelligence
    • Computational Geometry
    • Surgical Planning

    Background:

    • Orthognathic surgery requires meticulous preoperative planning for optimal functional and aesthetic outcomes.
    • Current planning methods are time-consuming and heavily reliant on surgeon expertise, leading to potential inefficiencies.

    Purpose of the Study:

    • To introduce OrthoPlanner, an automated two-stage framework for orthognathic surgical planning.
    • To leverage AI for precise prediction of postoperative bone morphology and automated surgical segment alignment.

    Main Methods:

    • Developed JawFormer, a transformer network predicting postoperative bone morphology from 3D point cloud data.
    • Integrated anatomical priors using a region-based feature alignment module for accurate structural modeling.
    • Implemented a symmetry-constrained rigid alignment algorithm for automated bone segment repositioning.

    Main Results:

    • Achieved superior quantitative performance and enhanced visualization compared to existing methods.
    • Demonstrated effectiveness through 65 experiments on real clinical datasets.
    • Significantly reduced planning time and manual effort while ensuring reproducible results.

    Conclusions:

    • OrthoPlanner offers an efficient, accurate, and reproducible solution for automated orthognathic surgical planning.
    • The framework enhances precision in predicting bone morphology and aligning surgical segments.
    • This AI-driven approach promises to improve surgical outcomes and streamline clinical workflows.