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Updated: Jul 9, 2026

Quantitative Assessment Protocol for Facial Soft Tissue Volumetric Changes with Stereophotogrammetry
Published on: December 9, 2025
Reliability of a Method for Computing Facial Symmetry Plane and Degree of Asymmetry Based on 3D-data
Jutta Hartmann1, Philipp Meyer-Marcotty, Michaela Benz
1Department of Orthodontics, Julius Maximilian University, Würzburg, Germany. j.hartmann@gmx.de
Objective:
The objective of this study was to analyze the reliability of a landmark-independent method for determining the facial symmetry plane and degree of asymmetry based on three-dimensional data from the facial surface from two sets of recordings, one performed consecutively and one performed on different days.
Materials And Methods:
We used an optical 3D-sensor to obtain the facial data of one male subject in two sets of ten measurements: the first taken consecutively and the second on different days. The symmetry plane and degree of asymmetry were calculated for each of the resulting twenty sets of data. One set of data was analyzed ten times for control purposes. The calculation of the mean deviation angle between the symmetry planes served as a measure of the reproducibility of these results.
Results:
Although the mean angular deviations of the computed symmetry planes, 0.134 degrees (for ten consecutively captured images) and 0.177 degrees (for the ten images captured on different days), were each significantly higher than the mean angular deviation (0.028 degrees) calculated from ten analyses of a single image, they can still be regarded as very small. There were no significant differences in the degree of asymmetry among the three measurement sets. The standard deviations revealed low values.
Conclusions:
This method can be used to compute with high reliability the symmetry planes and degree of asymmetry of facial 3D-data. The color-coded visualization of asymmetrical facial regions makes it possible for this analytical procedure to capture the asymmetries of facial soft tissue with substantially greater precision than 2-dimensional en face images.
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