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

Factors influencing the head and neck microbiome.

Advances in immunology·2026
Same author

Microbial biomarkers for OPMD progression.

Advances in immunology·2026
Same author

The microbiome of the head and neck region.

Advances in immunology·2026
Same author

Bioengineered nasal septum implant with 3D-printed silicone and chondrocyte-seeded fibrin hydrogel.

Regenerative biomaterials·2026
Same author

INHBA-S100A16 dysregulation enables a non-invasive molecular stratification platform for rapid detection of oral squamous cell carcinoma: results from a large diagnostic case-control study.

Biomarker research·2026
Same author

Fibrin, from blood to bone: a review.

Bone reports·2026

Related Experiment Video

Updated: Jun 18, 2025

Mimicking and Measuring Occlusal Erosive Tooth Wear with the "Rub&Roll" and Non-contact Profilometry
08:47

Mimicking and Measuring Occlusal Erosive Tooth Wear with the "Rub&Roll" and Non-contact Profilometry

Published on: February 2, 2018

12.2K

Isotopological remeshing and statistical shape analysis: Enhancing premolar tooth wear classification and simulation

Pauline Binvignat1, Akhilanand Chaurasia2, Pierre Lahoud3

  • 1Hospices Civils de Lyon, PAM Odontologie, Lyon, France.

Journal of Dentistry
|August 2, 2024
PubMed
Summary

This study shows that combining isotopological remeshing with statistical shape analysis (SSA) accurately captures tooth anatomy. Machine learning algorithms also show promise for diagnosing altered teeth, improving dental care.

Keywords:
Artificial IntelligenceDeep learning/machine learningDental anatomyDiagnostic systemsStatisticsTooth wear

More Related Videos

Precision of In Vivo Quantitative Tooth Wear Measurement Using Intra-Oral Scans
09:10

Precision of In Vivo Quantitative Tooth Wear Measurement Using Intra-Oral Scans

Published on: July 12, 2022

2.9K
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

1.1K

Related Experiment Videos

Last Updated: Jun 18, 2025

Mimicking and Measuring Occlusal Erosive Tooth Wear with the "Rub&Roll" and Non-contact Profilometry
08:47

Mimicking and Measuring Occlusal Erosive Tooth Wear with the "Rub&Roll" and Non-contact Profilometry

Published on: February 2, 2018

12.2K
Precision of In Vivo Quantitative Tooth Wear Measurement Using Intra-Oral Scans
09:10

Precision of In Vivo Quantitative Tooth Wear Measurement Using Intra-Oral Scans

Published on: July 12, 2022

2.9K
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

1.1K

Area of Science:

  • Dental morphology
  • Computational anatomy
  • Biomedical engineering

Background:

  • Accurate analysis of tooth anatomy is crucial for diagnosis and treatment planning.
  • Identifying and simulating dental alterations presents a significant challenge in restorative dentistry.

Purpose of the Study:

  • To evaluate the accuracy of a combined isotopological remeshing and statistical shape analysis (SSA) approach for capturing tooth anatomical features.
  • To compare the effectiveness of four Machine Learning (ML) algorithms in identifying and simulating tooth alterations.

Main Methods:

  • 113 premolar surfaces were analyzed using isotopological remeshing and SSA.
  • Seven anatomical features were extracted, and correlations with shape modes were explored.
  • Four ML algorithms were assessed for classification accuracy, with validation through cross-validation.

Main Results:

  • The combined method reliably captured key anatomical features with minimal deviation (mean 10.4 µm).
  • The first five shape modes explained 76.1% of shape variability.
  • Optimal ML algorithms achieved >83% accuracy and >86% precision in classifying tooth alterations.

Conclusions:

  • Isotopological remeshing combined with SSA is a reliable method for analyzing tooth anatomy.
  • ML algorithms show potential for aiding practitioners in diagnosing and planning treatments for altered teeth.