Prediction of orthognathic surgery plan from 3D cephalometric analysis via deep learning
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...
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| Format: | Article |
| Language: | English |
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BMC
2023-03-01
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| Series: | BMC Oral Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12903-023-02844-z |
| _version_ | 1797863337365602304 |
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| author | Mengjia Cheng Xu Zhang Jun Wang Yang Yang Meng Li Hanjiang Zhao Jingyang Huang Chenglong Zhang Dahong Qian Hongbo Yu |
| author_facet | Mengjia Cheng Xu Zhang Jun Wang Yang Yang Meng Li Hanjiang Zhao Jingyang Huang Chenglong Zhang Dahong Qian Hongbo Yu |
| author_sort | Mengjia Cheng |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T22:33:59Z |
| format | Article |
| id | doaj.art-83960cd71d30454a9bd8329c9132a6de |
| institution | Directory Open Access Journal |
| issn | 1472-6831 |
| language | English |
| last_indexed | 2024-04-09T22:33:59Z |
| publishDate | 2023-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Oral Health |
| spelling | doaj.art-83960cd71d30454a9bd8329c9132a6de2023-03-22T12:34:41ZengBMCBMC Oral Health1472-68312023-03-0123111110.1186/s12903-023-02844-zPrediction of orthognathic surgery plan from 3D cephalometric analysis via deep learningMengjia Cheng0Xu Zhang1Jun Wang2Yang Yang3Meng Li4Hanjiang Zhao5Jingyang Huang6Chenglong Zhang7Dahong Qian8Hongbo Yu9Department of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People’s Hospital, College of Stomatology, Shanghai Jiao Tong University School of MedicineMechanical College, Shanghai Dianji UniversitySchool of Computer & Computing Science, Hangzhou City UniversityShanghai Lanhui Medical Technology Co.Department of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People’s Hospital, College of Stomatology, Shanghai Jiao Tong University School of MedicineDepartment of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People’s Hospital, College of Stomatology, Shanghai Jiao Tong University School of MedicineDepartment of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People’s Hospital, College of Stomatology, Shanghai Jiao Tong University School of MedicineDepartment of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People’s Hospital, College of Stomatology, Shanghai Jiao Tong University School of MedicineSchool of Biomedical Engineering, Shanghai Jiao Tong UniversityDepartment of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People’s Hospital, College of Stomatology, Shanghai Jiao Tong University School of MedicineAbstract 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.https://doi.org/10.1186/s12903-023-02844-zDento-maxillofacial deformityOrthognathic surgeryVirtual surgical planningDeep learningRegression predictionTransformer |
| spellingShingle | Mengjia Cheng Xu Zhang Jun Wang Yang Yang Meng Li Hanjiang Zhao Jingyang Huang Chenglong Zhang Dahong Qian Hongbo Yu Prediction of orthognathic surgery plan from 3D cephalometric analysis via deep learning BMC Oral Health Dento-maxillofacial deformity Orthognathic surgery Virtual surgical planning Deep learning Regression prediction Transformer |
| title | Prediction of orthognathic surgery plan from 3D cephalometric analysis via deep learning |
| title_full | Prediction of orthognathic surgery plan from 3D cephalometric analysis via deep learning |
| title_fullStr | Prediction of orthognathic surgery plan from 3D cephalometric analysis via deep learning |
| title_full_unstemmed | Prediction of orthognathic surgery plan from 3D cephalometric analysis via deep learning |
| title_short | Prediction of orthognathic surgery plan from 3D cephalometric analysis via deep learning |
| title_sort | prediction of orthognathic surgery plan from 3d cephalometric analysis via deep learning |
| topic | Dento-maxillofacial deformity Orthognathic surgery Virtual surgical planning Deep learning Regression prediction Transformer |
| url | https://doi.org/10.1186/s12903-023-02844-z |
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