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

Data-driven soliton manifold approximations for dark and bright waves: Some prototypical 1D case examples.

Chaos (Woodbury, N.Y.)·2026
Same author

Generative Artificial Intelligence for Orthognathic Planning: Patient-Specific 3-Dimensional Jaw Reference Forms Conditioned on the Cranial Base.

Journal of oral and maxillofacial surgery : official journal of the American Association of Oral and Maxillofacial Surgeons·2026
Same author

The combination of low-intensity resistance exercise and electrical muscle stimulation effectively enhances executive function in men.

Clinical physiology and functional imaging·2026
Same author

Assessing county-level high-quality development and spatial adaptation paths under the space of flows perspective: a coupling detrended fluctuation analysis approach.

Scientific reports·2026
Same author

SinusNet+: Deep Condition-Label-Free Segmentation of Maxillary Sinus Conditions in CBCT images.

Dento maxillo facial radiology·2026
Same author

Analysis of pathogen distribution and risk factors for extended antibiotic course in children with microbiological-based protracted bacterial bronchitis in southwest China.

Frontiers in pediatrics·2026

Related Experiment Video

Updated: Jun 3, 2025

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

Automatic Reproduction of Natural Head Position in Orthognathic Surgery Using a Geometric Deep Learning Network.

Ji-Yong Yoo1, Su Yang2, Sang-Heon Lim3

  • 1Department of Biomedical Radiation Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Republic of Korea.

Diagnostics (Basel, Switzerland)
|January 11, 2025
PubMed
Summary

A new deep learning model, NHP-Net, accurately determines natural head position (NHP) from CT scans for orthognathic surgery. This improves surgical planning precision and patient outcomes by automating NHP reproduction.

Keywords:
computed tomographygeometric deep learninghead pose estimationnatural head positionorthognathic surgery

More Related Videos

Real-Time Dynamic Navigation System for the Precise Quad-Zygomatic Implant Placement in a Patient with a Severely Atrophic Maxilla
05:54

Real-Time Dynamic Navigation System for the Precise Quad-Zygomatic Implant Placement in a Patient with a Severely Atrophic Maxilla

Published on: October 18, 2021

1.7K
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

6.5K

Related Experiment Videos

Last Updated: Jun 3, 2025

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
Real-Time Dynamic Navigation System for the Precise Quad-Zygomatic Implant Placement in a Patient with a Severely Atrophic Maxilla
05:54

Real-Time Dynamic Navigation System for the Precise Quad-Zygomatic Implant Placement in a Patient with a Severely Atrophic Maxilla

Published on: October 18, 2021

1.7K
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

6.5K

Area of Science:

  • Medical Imaging
  • Computational Geometry
  • Artificial Intelligence

Background:

  • Accurate natural head position (NHP) determination is critical for orthognathic surgery planning.
  • Traditional NHP methods lack reproducibility and rely on external factors, impacting surgical accuracy.
  • Existing techniques can lead to inaccuracies in orthognathic surgical plans.

Purpose of the Study:

  • To develop a deep learning model for automatic NHP reproduction from CT scans.
  • To enhance the precision of surgical planning in orthognathic procedures.
  • To overcome limitations of traditional NHP determination methods.

Main Methods:

  • A geometric deep learning network (NHP-Net) was designed to predict NHP from CT scans.
  • 150 orthognathic surgery patient CT scans were used to train and validate the model.
  • 3D skull meshes were converted to point clouds, normalized, and used to train NHP-Net to predict rotation matrices for NHP alignment.

Main Results:

  • NHP-Net achieved a low rotation error (RE) of 1.918° ± 1.099°.
  • The model demonstrated significantly lower mean absolute errors (MAE) for roll and pitch angles compared to other deep learning models (p < 0.05).
  • NHP-Net accurately aligns CT-acquired postures to the NHP, enhancing surgical planning precision.

Conclusions:

  • NHP-Net effectively and accurately reproduces NHP from CT scans.
  • The model improves the efficiency of NHP determination, reducing surgeon workload.
  • Enhanced accuracy in NHP reproduction supports precise orthognathic surgery and better patient outcomes.