Predict Tooth Wear (PREDITOOTH)

Prediction of the Tooth Wear Index Based on a Dataset of Dental Shapes:a Retrospective Study

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

Study Overview

• Status: Recruiting
• Conditions: Prediction of Tooth Wear
• Intervention / Treatment: Other: Tooth Shape assessed using an intraoral scanner one after avulsion

• Study Type: Observational
• Enrollment (Estimated): 1000

Contacts and Locations

• Study Contact: Name: Raphael Richet, Clinical Assistant; Phone Number: +33669523314; Email: raphael.richert@univ-lyon1.fr
• Study Contact Backup: Name: Maxime Ducret, Professor; Email: maxime.ducret@univ-lyon1.fr

Study Locations

• Belgium
  • Leuven, Belgium
    • Recruiting
    • KU Leuven University Hospital
    • Contact: Pierre Lahoud
• France
  • Lyon, France, 69007
    • Recruiting
    • Lyon Dental Hospital
    • Contact: Raphael Richert, PhD; Phone Number: +33669523314; Email: raphael.richert@univ-lyon1.fr
• India
  • Lucknow, India
    • Recruiting
    • King George Medical University
    • Contact: Akhilanand Chaurasia
• Israel
  • Tel Aviv, Israel
    • Recruiting
    • Tel Aviv Universi
    • Contact: Rachel Sarig
• United States
  • Indiana
    • Indianapolis, Indiana, United States, 46202
      • Recruiting
      • Indiana University Hospital
      • Contact: Anderson Hara

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Probability Sample

Study Population

Avulsed teeth at the Oral Surgery Departement of the Lyon Dental University Hospital

Description

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 Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
all mature teeth presenting a tooth wear intact or presenting a tooth wear altered; Scaling of the tooth before scanning by the operator Evaluation of the tooh wear index 0 or 1 (by 2 calibrated experts )	Other: Tooth Shape assessed using an intraoral scanner one after avulsion; Tooth Shape assessed using an intraoral scanner one after avulsion and stored as StereoLithography (STL) file; Age, gender, reason of avulsion, type of tooth taken from the database and stored in a google sheet

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Prediction of the tooth wear index based on a dataset of dental shapes:a retrospective study	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.	only once

Collaborators and Investigators

• Sponsor: Hospices Civils de Lyon

Study record dates

Study Major Dates

• Study Start (Actual): December 12, 2023
• Primary Completion (Estimated): December 12, 2025
• Study Completion (Estimated): December 1, 2027

Study Registration Dates

• First Submitted: November 10, 2023
• First Submitted That Met QC Criteria: November 7, 2024
• First Posted (Estimated): November 8, 2024

Study Record Updates

• Last Update Posted (Estimated): November 8, 2024
• Last Update Submitted That Met QC Criteria: November 7, 2024
• Last Verified: November 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Stomatognathic Diseases; Tooth Diseases; Tooth Wear; Tooth Attrition
• Other Study ID Numbers: 69HCL23_1046

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No