Predict Tooth Wear

Recruiting

• Conditions: Prediction of Tooth Wear

• Registration Number: NCT06681844
• Lead Sponsor: Hospices Civils de Lyon

Brief Summary

Tooth wear, resulting from gradual loss of dental hard tissue due to mechanical and chemical factors, impacts tooth structure, texture, and function. It affects quality of life, with varying prevalence (26.9% to 90.0%), and is traditionally detected visually during check-ups, often at advanced stages. Monitoring alterations in tooth shape via intraoral scanners aids early detection, but restoration remains challenging. Prevention through early detection is vital, as patients may not fully comprehend tooth structure loss until visible. Recently, statistical shape analysis (SSA) used to learn the tooth anatomy and define a reference shape (biogeneric tooth) using. However, assuring landmark consistency is challenging mostly due to biases of the operator. Recently, a robust method called MEG-IsoQuad offered automated, isotopological remeshing. Combining this with SSA holds promise for diagnostic and simulation purposes. This study aims to assess the reliability of a remeshing-SSA approach for altered and intact premolar analysis and compare machine learning algorithms for simulating the shape of the initially intact tooth or future altered one.

The clinical perspective of the current work offers possibilities to:

* Prevent future tooth wear by detecting it at an early stage; and communicate better to the patient by presenting him/her potential future altered teeth

* Simulate the adapted reconstruction for the altered tooth by simulating the initially intact one

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2023-11-10
• Study Start: 2023-12-12
• Status Verified: 2024-11
• Study First Submitted QC: 2024-11-07
• Last Update Submitted: 2024-11-07
• Study First Posted: 2024-11-08
• Last Update Posted: 2024-11-08
• Primary Completion: 2025-12-12
• Study Completion: 2027-12

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 1000

Inclusion Criteria

• teeth avulsed presenting a tooth wear index between 0 and 3
• mature incisor, canine, premolar or molars (1st and 2nd only)

Exclusion Criteria

• teeth avulsed presenting a tooth wear index over 3 (or presenting an oral rehabilitation representative of a similar wear)
• immature teeth or teeth without root edification
• wisdom teeth

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Prediction of the tooth wear index based on a dataset of dental shapes:a retrospective study	only once	Four machine learning (ML) algorithms: a linear discriminant analysis (LDA) a support vector machine (SVM), a random forest (RM) and a gradient boosting machine (GBM) will be used to predict the tooth type and the alteration of the anatomy. The data set will be split into a 60/40 train and holdout test data set and models will be three-fold cross validated. Model performances will be evaluated in confusion matrices leading to define precision, recall, F1 score and accuracy.

Trial Locations

Locations (5)

Indiana University Hospital; 🇺🇸 Indianapolis, Indiana, United States

KU Leuven University Hospital; 🇧🇪 Leuven, Belgium

Lyon Dental Hospital; 🇫🇷 Lyon, France

King George Medical University; 🇮🇳 Lucknow, India

Tel Aviv Universi; 🇮🇱 Tel Aviv, Israel