Predict Tooth Wear
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Tooth Shape assessed using an intraoral scanner one after avulsion.
- Who it may be relevant to
- Registry conditions: Prediction of Tooth Wear. Basic parameters: 18 years — 80 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- United States, Belgium, France, India, Israel
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Official title
Prediction of the Tooth Wear Index Based on a Dataset of Dental Shapes:a Retrospective Study
Overview
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
Interventions
- 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
Primary outcome measures
- Prediction of the tooth wear index based on a dataset of dental shapes:a retrospective study [Time frame: only once]
Eligibility criteria
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
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
United States · 1 center
- Indiana University Hospital — Indianapolis
Belgium · 1 center
- KU Leuven University Hospital — Leuven
France · 1 center
- Lyon Dental Hospital — Lyon
India · 1 center
- King George Medical University — Lucknow
Israel · 1 center
- Tel Aviv Universi — Tel Aviv
Identifiers
NCT: NCT06681844 · 69HCL23_1046