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 27, 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

Development and External Validation of an Explainable AHP-ML Model for Orthodontic Tooth Extraction and Anchorage

Yang Yi1, Xinhang Shen1, Bin Wu1

  • 1College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.

Bioengineering (Basel, Switzerland)
|June 26, 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

DMXAA accelerates orthodontic tooth movement via macrophage-mediated Rab13-enriched extracellular vesicle release and chemokine secretion.

Progress in orthodontics·2026
Same author

Expert consensus on orthodontic-associated alveolar ridge augmentation for adult patients.

International journal of oral science·2026
Same author

Macrophage ATF6 accelerates corticotomy-assisted orthodontic tooth movement through promoting Tnfα transcription.

International journal of oral science·2025
Same author

Expert consensus on early orthodontic treatment of class III malocclusion.

International journal of oral science·2025
Same author

Formulation of Hyperelastic Constitutive Model for Human Periodontal Ligament Based on Fiber Volume Fraction.

Materials (Basel, Switzerland)·2025
Same author

Mediating role of immune cells in association between volatile organic compounds and periodontitis: NHANES 2011-2014.

Journal of periodontology·2024

This study introduces an explainable orthodontic decision-support model combining expert Analytic Hierarchy Process (AHP) weighting with machine learning. The model aids in crucial treatment planning decisions like tooth extraction and anchorage, enhancing clinical interpretability.

Area of Science:

  • Orthodontics and Dental Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Decision Support Systems

Background:

  • Current machine learning models for orthodontic treatment planning lack transparency and clinical interpretability.
  • Key orthodontic decisions like tooth extraction and anchorage assessment require trustworthy decision support tools.

Purpose of the Study:

  • To develop and externally validate an explainable orthodontic treatment decision-support model.
  • To integrate expert-derived Analytic Hierarchy Process (AHP) weighting with machine learning for improved transparency.

Main Methods:

  • Established a diagnostic indicator framework with 18 orthodontic variables.
  • Developed and validated machine learning models incorporating AHP scores for tooth extraction and maximum anchorage prediction.
Keywords:
Analytic Hierarchy Processanchorage controlexplainable machine learningorthodontic treatment decision supporttooth extraction

More Related Videos

The Establishment of a Murine Maxillary Orthodontic Model
04:11

The Establishment of a Murine Maxillary Orthodontic Model

Published on: October 27, 2023

Related Experiment Videos

Last Updated: Jun 27, 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

The Establishment of a Murine Maxillary Orthodontic Model
04:11

The Establishment of a Murine Maxillary Orthodontic Model

Published on: October 27, 2023

  • Utilized SHAP analysis for feature contribution interpretation and external validation on a separate cohort.

    • Main Results:

    • The AHP-Random Forest (RF) extraction model and AHP-Logistic Regression (LR) anchorage model showed high performance (AUCs 0.864 and 0.822).
    • SHAP analysis confirmed clinical relevance by identifying key predictors consistent with expert reasoning.
    • External validation demonstrated the model's preliminary transportability, classifying 78% of extraction cases and 83% of anchorage cases.

    Conclusions:

    • Integrating AHP expert knowledge with machine learning creates an explainable and clinically interpretable orthodontic decision-support model.
    • This approach has potential value in standardizing evidence-informed, patient-specific orthodontic decision-making.
    • The developed framework enhances trustworthiness in AI-driven orthodontic treatment planning.