Automatic Assessment of 3-Dimensional Facial Soft Tissue Symmetry Before and After Orthognathic Surgery Using a Machine Learning Model: A Preliminary Experience.

PURPOSE

An objective and quantitative assessment of facial symmetry is essential for the surgical planning and evaluation of treatment outcomes in orthognathic surgery (OGS). This study applied the transfer learning model with a convolutional neural network based on 3-dimensional (3D) contour line features to evaluate the facial symmetry before and after OGS.

METHODS

A total of 158 patients were recruited in a retrospective cohort study for the assessment and comparison of facial symmetry before and after OGS from January 2018 to March 2020. Three-dimensional facial photographs were captured by the 3dMD face system in a natural head position, with eyes looking forward, relaxed facial muscles, and habitual dental occlusion before and at least 6 months after surgery. Three-dimensional contour images were extracted from 3D facial images for the subsequent Web-based automatic assessment of facial symmetry by using the transfer learning with a convolutional neural network model.

RESULTS

The mean score of postoperative facial symmetry showed significant improvements from 2.74 to 3.52, and the improvement degree of facial symmetry (in percentage) after surgery was 21% using the constructed machine learning model. A Web-based system provided a user-friendly interface and quick assessment results for clinicians and was an effective doctor-patient communication tool.

CONCLUSIONS

This work was the first attempt to automatically assess the facial symmetry before and after surgery in an objective and quantitative value by using a machine learning model based on the 3D contour feature map.