An observational study in Prediction of Tooth Wear, sponsored by Hospices Civils de Lyon. Recruiting at 5 sites in 5 countries. Open to participants aged 18 Years to 80 Years. Per ClinicalTrials.gov, last updated 2024-11-08.
Sponsored by Hospices Civils de Lyon · Observational
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:
62 studies on the registry are indexed under Tooth Wear; 12 are open to participants now.
This study's planned enrollment of 1,000 is above the median of 71 across 21 observational studies indexed under Tooth Wear.
Browse Tooth Wear studies →Hospices Civils de Lyon is the lead sponsor of 1,866 studies on the registry; 448 are open to participants now.
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Avulsed teeth at the Oral Surgery Departement of the Lyon Dental University Hospital
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Exclusion Criteria:
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
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.
Time frame: only once
Plan to share: No
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