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Updated: Nov 21, 2025
A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible
Published on: January 28, 2020
Automatic Assessment of 3-Dimensional Facial Soft Tissue Symmetry Before and After Orthognathic Surgery Using a
Lun-Jou Lo1, Chao-Tung Yang2, Cheng-Ting Ho3
1From the Department of Plastic and Reconstructive Surgery, and Craniofacial Research Center, Chang Gung Memorial Hospital, Chang Gung University, Taoyuan.
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.
