Clinical trial NCT04464252: Development and Validation of an Automated Three-dimensional Cephalometry Method
Official title: Development and Validation of an Automated Three-dimensional Cephalometry Method
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Orthognathic Surgery
Dentofacial Deformities
Keywords: Imaging, three-dimensional
Cephalometry
Anatomic Landmarks
Radiographic Image Interpretation
Deep Learning
Orthodontics
Lead sponsor: Assistance Publique - Hôpitaux de Paris
Sponsor class: OTHER
Collaborators: name: Laboratoire de Biomécanique Georges Charpak
First posted: 2020-07-09
Enrollment: 453
Sex: ALL
Minimum age (years): 15
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Primary care hospital (hopital de la Pitié-Salpêtrière, AP-HP, Paris, France), maxillofacial surgery department

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

Detailed 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.

Eligibility criteria: Inclusion Criteria:

1. Patients followed in the maxillofacial surgery department of Pitie-Salpetriere hospital (AP-HP, Paris, France) since 2014;
2. who underwent orthognathic surgery;
3. who had, for surgery planning, a three-dimensional radiographic examination (CT-Scan) which has been segmented for the manufacture of a custom-made device;
4. who did not object to the research.

Primary outcomes:
measure: Average distances (in mm) between the points placed automatically and the points placed manually (reference).
timeFrame: through study completion, an average of 1 year
Secondary outcomes:
measure: Percentage of points placed automatically within 2 / 3 / 4 mm of the the points placed manually (reference)
timeFrame: through study completion, an average of 1 year
Other outcomes:
Locations:
city: Paris
country: France
facility: Service de Chirurgie Maxillo-Faciale, Service de Chirurgie Maxillo-Faciale
Study officials:
References: