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

Research of orthodontic soft tissue profile prediction based on conditional generative adversarial networks.

Journal of dentistry·2025
Same author

Global Research Trends and Focuses on Pierre Robin Sequence From 1992 to 2023: A Bibliometric Analysis of the Past 3 Decades.

The Journal of craniofacial surgery·2024
Same author

STSN-Net: Simultaneous Tooth Segmentation and Numbering Method in Crowded Environments with Deep Learning.

Diagnostics (Basel, Switzerland)·2024
Same author

(68)Ga-labeled 3PRGD2 for dual PET and Cerenkov luminescence imaging of orthotopic human glioblastoma.

Bioconjugate chemistry·2015
Same author

An exploratory study on 99mTc-RGD-BBN peptide scintimammography in the assessment of breast malignant lesions compared to 99mTc-3P4-RGD2.

PloS one·2015
Same author

Chemoradiation therapy reduces aldehyde dehydrogenase 1 expression in cervical cancer but does not improve patient survival.

Medical oncology (Northwood, London, England)·2015
Same journal

Correction: Effectiveness of edutainment use in videobased learning on oral health education for school-age children: a randomized clinical trial.

BMC oral health·2026
Same journal

Intrapulpal thermal variation in standalone and dentifrice-assisted laser desensitisation at two time intervals.

BMC oral health·2026
Same journal

Evaluation of the frequency of dental anomalies in orthodontic patients with various malocclusions.

BMC oral health·2026
Same journal

State-of-the-art 3D analysis of soft tissue prediction for orthognathic correction of dentofacial deformities in the Egyptian population.

BMC oral health·2026
Same journal

pH-responsive ion release kinetics of a chitosan-based hydrogel incorporating nanohydroxyapatite and sodium fluoride for enamel remineralization.

BMC oral health·2026
Same journal

Effects of repeated preheating on polymerization shrinkage kinetics and Vickers microhardness of resin composites: an in vitro study.

BMC oral health·2026
See all related articles

Related Experiment Video

Updated: May 27, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.5K

Development of a diagnostic classification model for lateral cephalograms based on multitask learning.

Qiao Chang1, Shaofeng Wang1, Fan Wang1

  • 1Department of Orthodontics, Beijing Stomatological Hospital, Capital Medical University, Beijing, China.

BMC Oral Health
|February 15, 2025
PubMed
Summary
This summary is machine-generated.

This study developed an automatic cephalometric classification system using multitask learning. The model efficiently classifies eight diagnostic items from lateral cephalograms with high accuracy, offering a novel approach for orthodontic diagnosis.

Keywords:
CephalometryDeep learningImage classificationMultitask learning

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
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

2.6K

Related Experiment Videos

Last Updated: May 27, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.5K
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
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

2.6K

Area of Science:

  • Artificial Intelligence in Medicine
  • Orthodontics
  • Medical Imaging Analysis

Background:

  • Cephalometric analysis is crucial for orthodontic diagnosis.
  • Manual classification of cephalograms is time-consuming and prone to variability.
  • Developing automated methods can improve efficiency and accuracy.

Purpose of the Study:

  • To develop a cephalometric classification method using multitask learning.
  • To automate the classification of eight common diagnostic items from lateral cephalograms.
  • To evaluate the performance of the multitask learning model.

Main Methods:

  • A retrospective study utilizing 3,310 lateral cephalograms.
  • Manual annotation and verification of eight clinical classifications by orthodontists.
  • Development of a multitask learning model based on the ResNeXt50_32×4d network.
  • Evaluation using accuracy, precision, sensitivity, specificity, and AUC.

Main Results:

  • The model achieved simultaneous classification of eight diagnostic items in an average of 0.0096 seconds.
  • Classification accuracy ranged from 0.75-0.9 for different items.
  • Overall Area Under the Curve (AUC) values exceeded 0.9 for all classifications.

Conclusions:

  • An automatic diagnostic classification model for lateral cephalograms was successfully established using multitask learning.
  • The model demonstrated high performance and reduced computational costs.
  • This approach offers a novel perspective for automated orthodontic diagnosis.