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Clinical Trial Summary

This study is aimed to develop and assess the validity of an algorithm for automated three-dimensional cephalometry.


Clinical Trial Description

Cephalometric analysis is a standardized diagnostic method used daily by orthodontists and maxillofacial surgeons. It is based on linear and angular measurements performed on radiographic images. This examination is traditionally done manually on two-dimensional radiographs, which does not allow to analyze finely the bilateral structures which are found superimposed. Cephalometric analysis of three-dimensional imaging (cone beam computed tomography (CBCT) or computed tomography scan (CT-Scan)) may provide additional diagnostic information, particularly for patients with maxillofacial abnormalities or marked asymmetries. One of the obstacle to the clinical use of three-dimensional cephalometric analysis is the time and expertise needed to manually place the landmarks. Automatic methods described in literature are preliminary and lack validation in a clinical context. Our retrospective observational study is aimed to develop and validate a new automated three-dimensional cephalometry method. This method will be based on a deep learning algorithm trained from a database of pre-surgery CT-Scans of patients with have undergone an orthognathic surgery. These CT-Scans will be manually annotated to provide a reference standard for the training of the algorithm and its evaluation. The validation of our results will focus on demonstrating the diagnostic effectiveness and robustness of three-dimensional cephalometric measurements obtained in this clinical context. ;


Study Design


Related Conditions & MeSH terms


NCT number NCT04464252
Study type Observational
Source Assistance Publique - Hôpitaux de Paris
Contact
Status Completed
Phase
Start date April 30, 2022
Completion date April 30, 2022

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