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 Experiment Video

Updated: Jun 9, 2026

Measuring Maxillary Posterior Tooth Movement: A Model Assessment using Palatal and Dental Superimposition
07:32

Measuring Maxillary Posterior Tooth Movement: A Model Assessment using Palatal and Dental Superimposition

Published on: February 23, 2024

Applying Automated Artificial Intelligence Models on Lateral Cephalometric Parameters to Accurately Classify Arab

Kareem Midlej1, Peter Proff2, Nezar Watted3,4

  • 1Department of Clinical Microbiology and Immunology, Gray Faculty of Medicine and Health Sciences, Tel Aviv University, Tel Aviv, Israel.

Clinical and Experimental Dental Research
|June 7, 2026
PubMed
Summary

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

Individualizing ANB angle based on Arab and German orthodontic cephalometric analysis: a cross-sectional multi-center study.

Scientific reports·2026
Same author

Sensor-derived wearing time predicts 3D orthodontic side effects during mandibular advancement therapy of obstructive sleep apnea.

Clinical oral investigations·2026
Same author

Machine Learning and Clustering Analysis of Class II and III Malocclusions.

Clinical and experimental dental research·2026
Same author

Mapping Genetic Modifiers of Polyp Formation in <i>Smad4</i>-Deficient Juvenile Polyposis Using the Collaborative Cross Mouse Population.

Cells·2026
Same author

Regenerative periodontal surgery and orthodontic therapy in the treatment of patients with stage IV periodontitis and pathologic tooth migration.

Periodontology 2000·2026
Same author

Investigating a potential association between agenesis of the third molars and variations in dental crown dimensions.

PloS one·2026

Accurate classification of orthodontic patients is crucial for treatment outcomes. This study utilized artificial intelligence (AI) on lateral cephalograms, achieving 98% accuracy in classifying skeletal malocclusion using key cephalometric parameters.

Area of Science:

  • Orthodontics and Dental Imaging
  • Artificial Intelligence in Healthcare
  • Cephalometric Analysis

Background:

  • Malocclusion significantly impacts patients' quality of life, encompassing physical and psychological well-being.
  • Precise patient classification is essential for successful orthodontic treatment planning and outcomes.
  • Current diagnostic methods for malocclusion can be enhanced through advanced computational approaches.

Purpose of the Study:

  • To develop and validate an AI-driven model for accurate classification of orthodontic patients using lateral cephalograms.
  • To improve the diagnostic process for skeletal malocclusion (Class I, II, III) through machine learning algorithms.
  • To identify the most influential cephalometric parameters for accurate malocclusion classification.

Main Methods:

Keywords:
cephalometric parametersdeep‐learningdiagnosismachine‐learningpersonalized medicineskeletal malocclusion

More Related Videos

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images
05:49

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Published on: February 23, 2024

Related Experiment Videos

Last Updated: Jun 9, 2026

Measuring Maxillary Posterior Tooth Movement: A Model Assessment using Palatal and Dental Superimposition
07:32

Measuring Maxillary Posterior Tooth Movement: A Model Assessment using Palatal and Dental Superimposition

Published on: February 23, 2024

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images
05:49

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Published on: February 23, 2024

  • Employed machine learning algorithms including LDA, RF, DT, KNN, SVM, and NB on a dataset of 1014 Arab patients.
  • Utilized impurity decrease and leave-one-feature-out (LOFO) techniques to determine parameter importance.
  • Applied an Artificial Neural Network (ANN) for final patient classification based on cephalometric data.

Main Results:

  • A model incorporating Wits appraisal, SNB, SNA, and ML-NSL angles achieved 0.98 classification accuracy.
  • LOFO analysis identified Calculated_ANB and Wits appraisal as the most critical parameters in Random Forest models.
  • Decision tree analysis revealed distinct ANB angle ranges for skeletal classes in this ethnic group, with specific thresholds for Class III (<0.084°) and Class II (>1.23°).

Conclusions:

  • The study successfully developed an AI model for precise orthodontic patient classification.
  • The findings highlight the potential of AI in enhancing cephalometric analysis for malocclusion diagnosis.
  • Further research is recommended to validate these findings across diverse ethnic populations.