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

Deep learning for orbital fracture detection and reconstruction: A systematic review on diagnostic accuracy and surgical planning.

Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery·2025
Same author

Efficacy of the methods of age determination using artificial intelligence in panoramic radiographs - a systematic review.

International journal of legal medicine·2024
Same author

A prospective non-fatal injuries assessment: A multivariate analysis in medical-legal examinations.

Journal of forensic and legal medicine·2023
Same author

Efficiency of maxillomandibular advancement for the treatment of obstructive apnea syndrome: a comprehensive overview of systematic reviews.

Clinical oral investigations·2022
Same author

Mandibular shape prediction model using machine learning techniques.

Clinical oral investigations·2022
Same author

Biotypic classification of facial profiles using discrete cosine transforms on lateral radiographs.

Archives of oral biology·2021

Related Experiment Video

Updated: May 2, 2026

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

6.6K

Mandiblemath: External validation of a predictive tool for mandibular reconstruction.

Tania Camila Niño-Sandoval1, Belmiro Ce Vasconcelos2

  • 1Department of Oral and Maxillofacial Surgery, Universidade de Pernambuco - School of Dentistry (UPE/FOP), Recife, Brazil.

Journal of Cranio-Maxillo-Facial Surgery : Official Publication of the European Association for Cranio-Maxillo-Facial Surgery
|April 30, 2026
PubMed
Summary

Mandiblemath, an AI tool, predicts 3D mandibular shape from 2D data for reconstructive surgery. It shows clinical potential for accurate, low-cost, and reproducible surgical planning.

Keywords:
CephalometryMachine learningMandibular shapePredictionSoftware validation

More Related Videos

Author Spotlight: 3D Movement Assessment of Maxillary Posterior Teeth in Clear Aligner Treatment
07:32

Author Spotlight: 3D Movement Assessment of Maxillary Posterior Teeth in Clear Aligner Treatment

Published on: February 23, 2024

2.1K
3D Planning and Printing of Patient Specific Implants for Reconstruction of Bony Defects
08:15

3D Planning and Printing of Patient Specific Implants for Reconstruction of Bony Defects

Published on: August 4, 2020

6.0K

Related Experiment Videos

Last Updated: May 2, 2026

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

6.6K
Author Spotlight: 3D Movement Assessment of Maxillary Posterior Teeth in Clear Aligner Treatment
07:32

Author Spotlight: 3D Movement Assessment of Maxillary Posterior Teeth in Clear Aligner Treatment

Published on: February 23, 2024

2.1K
3D Planning and Printing of Patient Specific Implants for Reconstruction of Bony Defects
08:15

3D Planning and Printing of Patient Specific Implants for Reconstruction of Bony Defects

Published on: August 4, 2020

6.0K

Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Medicine
  • Surgical Planning

Background:

  • Mandibular reconstruction presents challenges in accurately predicting 3D morphology.
  • Existing methods may lack efficiency and reproducibility in surgical planning.

Purpose of the Study:

  • To develop and validate Mandiblemath, an AI-driven software for predictive mandibular reconstruction.
  • To infer 3D mandibular morphology from 2D craniofacial data for surgical planning.

Main Methods:

  • Developed Mandiblemath using Python, integrating scientific computing, 3D visualization, and supervised learning.
  • Trained sex-specific classifiers (random forest, SVM, gradient boosting) on 91 CT scans.
  • Validated predictions using 18 independent CT scans and surface-based metrics.

Main Results:

  • Achieved high accuracy in external validation (88.8% for group identification, 66.6-70.4% for ranking).
  • Predicted models showed clinically acceptable discrepancies (RMSD 1.2-3.3 mm) and strong overlap (F1@2.5 mm ≥ 0.85).
  • Demonstrated technical feasibility and clinical potential as a decision-support tool.

Conclusions:

  • Mandiblemath offers a low-cost, reproducible method for predicting mandibular morphology.
  • The software has potential for integration into digital surgical workflows for reconstructive planning.
  • Further multicenter validation is supported by these findings.