Orthognathic surgery (OGS) is frequently used to correct facial deformities associated with skeletal malocclusion and facial asymmetry. An accurate evaluation of facial symmetry is a critical for precise surgical planning and the execution of OGS. However, no facial symmetry scoring standard is available. Typically, orthodontists or physicians simply judge facial symmetry. Therefore, maintaining accuracy is difficult. We propose a convolutional neural network with a transfer learning approach for facial symmetry assessment based on 3-dimensional (3D) features to assist physicians in enhancing medical treatments. We trained a new model to score facial symmetry using transfer learning. Cone-beam computed tomography scans in 3D were transformed into contour maps that preserved 3D characteristics. We used various data preprocessing and amplification methods to determine the optimal results. The original data were enlarged by 100 times. We compared the quality of the four models in our experiment, and the neural network architecture was used in the analysis to import the pretraining model. We also increased the number of layers, and the classification layer was fully connected. We input random deformation data during training and dropout to prevent the model from overfitting. In our experimental results, the Xception model and the constant data amplification approach achieved an accuracy rate of 90%.

正颌外科手术（OGS）通常用于矫正与骨骼错合和面部不对称相关的面部畸形。面部对称性的准确评估对于精确的手术计划和OGS的执行至关重要。但是，没有可用的面部对称性评分标准。通常，正畸医生或医师只需判断面部对称性即可。因此，难以保持精度。我们提出了一种基于转移学习方法的卷积神经网络，用于基于3维（3D）特征的面部对称性评估，以帮助医师增强医学治疗效果。我们使用转移学习训练了一种新模型来对面部对称性进行评分。将3D锥束计算机断层扫描扫描转换为保留3D特征的轮廓图。我们使用了各种数据预处理和放大方法来确定最佳结果。原始数据放大了100倍。我们在实验中比较了这四个模型的质量，并在分析中使用了神经网络架构来导入预训练模型。我们还增加了层数，并完全连接了分类层。我们在训练和辍学期间输入随机变形数据，以防止模型过度拟合。在我们的实验结果中，Xception模型和恒定数据放大方法达到了90％的准确率。我们还增加了层数，并完全连接了分类层。我们在训练和辍学期间输入随机变形数据，以防止模型过度拟合。在我们的实验结果中，Xception模型和恒定数据放大方法达到了90％的准确率。我们还增加了层数，并完全连接了分类层。我们在训练和辍学期间输入随机变形数据，以防止模型过度拟合。在我们的实验结果中，Xception模型和恒定数据放大方法达到了90％的准确率。