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

Modulation of Orthodontic Tooth Movement by Statins: A Systematic Review of Animal Studies.

Dentistry journal·2026
Same author

The Bisphosphonate Accumulation Index (BAI): A Quantitative Metric for Cumulative Antiresorptive Exposure in Pre-Procedural Dental and Surgical Assessment.

Dentistry journal·2026
Same author

A novel autonomic pathway: implications for vagal-sympathetic reflex physiology.

Clinical autonomic research : official journal of the Clinical Autonomic Research Society·2026
Same author

Effects of microplastic release from 3D-printed orthodontic aligners: a histological and immunological bioassay approach.

European journal of orthodontics·2026
Same author

Additive manufacturing and in vitro characterization of scaffolds consisting of PCL/P<sub>2</sub>O<sub>5</sub>-free bioactive glasses composites with angiogenic and osteogenic potential.

Biomaterials·2026
Same author

Beyond GLM: Inter-Subject Variability as a Complementary Approach to Detect Longitudinal Changes in Emotion Processing in Multiple Sclerosis.

Journal of imaging·2026

Related Experiment Video

Updated: Jan 13, 2026

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans
10:23

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Published on: September 8, 2023

3.6K

Automated 3D cephalometry: A lightweight V-net for landmark localization on CBCT.

Benedetta Baldini1, Giulia Rubiu2, Marco Serafin3

  • 1Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan 20133, Italy.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|January 10, 2026
PubMed
Summary

A new deep learning model automates cephalometric analysis by accurately locating anatomical landmarks on cone beam CT scans. This AI tool offers a fast and reliable alternative to manual measurements for orthodontic treatment planning.

Keywords:
Automated localizationCbctCephalometric analysisDeep learningMedical image analysis

More Related Videos

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

1.3K
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

Related Experiment Videos

Last Updated: Jan 13, 2026

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans
10:23

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Published on: September 8, 2023

3.6K
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

1.3K
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

Area of Science:

  • Medical Imaging and Artificial Intelligence
  • Orthodontics and Dental Diagnostics

Background:

  • Cephalometric analysis is crucial for orthodontic clinical decision support, traditionally requiring manual landmark identification on 3D cone beam CT (CBCT) scans.
  • Manual analysis is time-consuming and operator-dependent, highlighting the need for automated solutions in clinical workflows.

Purpose of the Study:

  • To develop and validate a lightweight deep learning (DL) model for the automatic localization of 16 key anatomical landmarks on CBCT scans.
  • To assess the accuracy and reliability of the DL model's landmark identification and subsequent cephalometric measurements compared to manual methods.

Main Methods:

  • A V-net deep learning architecture was trained on 350 manually annotated CBCT scans from diverse imaging systems and patient demographics.
  • The model's performance was evaluated based on mean landmark localization error and compared with manually derived linear and angular cephalometric measurements.
  • Bland-Altman analysis was employed to assess the agreement between automated and manual measurements.

Main Results:

  • The DL model achieved a mean landmark localization error of 1.95 ± 1.06 mm, within the clinically acceptable 2 mm threshold.
  • Automated cephalometric measurements showed minimal errors (-0.15 ± 0.95° for angular, 0.20 ± 0.28 mm for linear) with strong agreement to manual values.
  • Mean inference time was under 32 seconds per scan, demonstrating computational efficiency.

Conclusions:

  • The developed lightweight DL model reliably automates cephalometric landmark identification and measurement, offering a viable alternative to manual procedures.
  • The model's accuracy, robustness across heterogeneous datasets, and fast inference times support its potential as a clinical decision support tool in orthodontics.
  • Automated cephalometric analysis can enhance efficiency and consistency in orthodontic treatment planning, particularly for critical parameters like the ANB angle.