PDF(Pubmed)
Abstract：
Preoperative planning of orthognathic surgery is indispensable for achieving ideal surgical outcome regarding the occlusion and jaws\' position. However, orthognathic surgery planning is sophisticated and highly experience-dependent, which requires comprehensive consideration of facial morphology and occlusal function. This study aimed to investigate a robust and automatic method based on deep learning to predict reposition vectors of jawbones in orthognathic surgery plan.
A regression neural network named VSP transformer was developed based on Transformer architecture. Firstly, 3D cephalometric analysis was employed to quantify skeletal-facial morphology as input features. Next, input features were weighted using pretrained results to minimize bias resulted from multicollinearity. Through encoder-decoder blocks, ten landmark-based reposition vectors of jawbones were predicted. Permutation importance (PI) method was used to calculate contributions of each feature to final prediction to reveal interpretability of the proposed model.
VSP transformer model was developed with 383 samples and clinically tested with 49 prospectively collected samples. Our proposed model outperformed other four classic regression models in prediction accuracy. Mean absolute errors (MAE) of prediction were 1.41 mm in validation set and 1.34 mm in clinical test set. The interpretability results of the model were highly consistent with clinical knowledge and experience.
The developed model can predict reposition vectors of orthognathic surgery plan with high accuracy and good clinically practical-effectiveness. Moreover, the model was proved reliable because of its good interpretability.
A regression neural network named VSP transformer was developed based on Transformer architecture. Firstly, 3D cephalometric analysis was employed to quantify skeletal-facial morphology as input features. Next, input features were weighted using pretrained results to minimize bias resulted from multicollinearity. Through encoder-decoder blocks, ten landmark-based reposition vectors of jawbones were predicted. Permutation importance (PI) method was used to calculate contributions of each feature to final prediction to reveal interpretability of the proposed model.
VSP transformer model was developed with 383 samples and clinically tested with 49 prospectively collected samples. Our proposed model outperformed other four classic regression models in prediction accuracy. Mean absolute errors (MAE) of prediction were 1.41 mm in validation set and 1.34 mm in clinical test set. The interpretability results of the model were highly consistent with clinical knowledge and experience.
The developed model can predict reposition vectors of orthognathic surgery plan with high accuracy and good clinically practical-effectiveness. Moreover, the model was proved reliable because of its good interpretability.
摘要：
背景：正颌手术的术前计划对于在咬合和颌骨位置方面获得理想的手术效果是必不可少的。然而,正颌手术计划复杂且高度依赖经验，这需要综合考虑面部形态和咬合功能。本研究旨在研究一种基于深度学习的鲁棒自动方法来预测正颌手术计划中颌骨的重定位向量。
方法：基于Transformer体系结构，开发了一种名为VSPTransformer的回归神经网络。首先,采用3D头颅测量分析来量化骨骼-面部形态作为输入特征。接下来,使用预训练结果对输入特征进行加权,以最小化多重共线性导致的偏差.通过编码器-解码器块，预测了十个基于界标的颌骨重定位向量。使用置换重要性（PI）方法来计算每个特征对最终预测的贡献，以揭示所提出模型的可解释性。
结果：用383个样本开发了VSP变压器模型，并用49个前瞻性收集的样本进行了临床试验。我们提出的模型在预测精度上优于其他四个经典回归模型。预测的平均绝对误差（MAE）在验证集中为1.41mm，在临床测试集中为1.34mm。模型的可解释性结果与临床知识和经验高度一致。
结论：所开发的模型可以预测正颌手术计划的重新定位向量，具有较高的准确性和良好的临床实用性。此外，由于其良好的可解释性，该模型被证明是可靠的。
方法：基于Transformer体系结构，开发了一种名为VSPTransformer的回归神经网络。首先,采用3D头颅测量分析来量化骨骼-面部形态作为输入特征。接下来,使用预训练结果对输入特征进行加权,以最小化多重共线性导致的偏差.通过编码器-解码器块，预测了十个基于界标的颌骨重定位向量。使用置换重要性（PI）方法来计算每个特征对最终预测的贡献，以揭示所提出模型的可解释性。
结果：用383个样本开发了VSP变压器模型，并用49个前瞻性收集的样本进行了临床试验。我们提出的模型在预测精度上优于其他四个经典回归模型。预测的平均绝对误差（MAE）在验证集中为1.41mm，在临床测试集中为1.34mm。模型的可解释性结果与临床知识和经验高度一致。
结论：所开发的模型可以预测正颌手术计划的重新定位向量，具有较高的准确性和良好的临床实用性。此外，由于其良好的可解释性，该模型被证明是可靠的。
