Author:
Cheng Mengjia,Zhang Xu,Wang Jun,Yang Yang,Li Meng,Zhao Hanjiang,Huang Jingyang,Zhang Chenglong,Qian Dahong,Yu Hongbo
Abstract
Abstract
Background
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.
Methods
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.
Results
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.
Conclusions
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.
Funder
National Natural Science Foundation of China
Shanghai scientific and technological projects
the Research Fund of Medicine and Engineering of Shanghai Jiao Tong University
Clinical Research Project of Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine
Publisher
Springer Science and Business Media LLC
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