Deep Learning–Based Facial and Skeletal Transformations for Surgical Planning

• Published in: Journal of dental research 2024-07, Vol.103 (8), p.809-819
• Main Authors: Bao, J., Zhang, X., Xiang, S., Liu, H., Cheng, M., Yang, Y., Huang, X., Xiang, W., Cui, W., Lai, H.C., Huang, S., Wang, Y., Qian, D., Yu, H.
• Format: Article
• Language: English
• Subjects: Adult; Anatomic Landmarks; Deep Learning; Dentofacial Deformities - diagnostic imaging; Dentofacial Deformities - surgery; Face - anatomy & histology; Face - diagnostic imaging; Facial Bones - anatomy & histology; Facial Bones - diagnostic imaging; Facial Bones - surgery; Female; Humans; Imaging, Three-Dimensional - methods; Male; Orthognathic Surgical Procedures - methods; Patient Care Planning; Predictions; Skeleton; Soft tissues; Surgery
• ISSN: 0022-0345; 1544-0591; 1544-0591
• DOI: 10.1177/00220345241253186

The increasing application of virtual surgical planning (VSP) in orthognathic surgery implies a critical need for accurate prediction of facial and skeletal shapes. The craniofacial relationship in patients with dentofacial deformities is still not understood, and transformations between facial and skeletal shapes remain a challenging task due to intricate anatomical structures and nonlinear relationships between the facial soft tissue and bones. In this study, a novel bidirectional 3-dimensional (3D) deep learning framework, named P2P-ConvGC, was developed and validated based on a large-scale data set for accurate subject-specific transformations between facial and skeletal shapes. Specifically, the 2-stage point-sampling strategy was used to generate multiple nonoverlapping point subsets to represent high-resolution facial and skeletal shapes. Facial and skeletal point subsets were separately input into the prediction system to predict the corresponding skeletal and facial point subsets via the skeletal prediction subnetwork and facial prediction subnetwork. For quantitative evaluation, the accuracy was calculated with shape errors and landmark errors between the predicted skeleton or face with corresponding ground truths. The shape error was calculated by comparing the predicted point sets with the ground truths, with P2P-ConvGC outperforming existing state-of-the-art algorithms including P2P-Net, P2P-ASNL, and P2P-Conv. The total landmark errors (Euclidean distances of craniomaxillofacial landmarks) of P2P-ConvGC in the upper skull, mandible, and facial soft tissues were 1.964 ± 0.904 mm, 2.398 ± 1.174 mm, and 2.226 ± 0.774 mm, respectively. Furthermore, the clinical feasibility of the bidirectional model was validated using a clinical cohort. The result demonstrated its prediction ability with average surface deviation errors of 0.895 ± 0.175 mm for facial prediction and 0.906 ± 0.082 mm for skeletal prediction. To conclude, our proposed model achieved good performance on the subject-specific prediction of facial and skeletal shapes and showed clinical application potential in postoperative facial prediction and VSP for orthognathic surgery.