The application of a fully digital approach in the treatment of skeletal class III malocclusion: a preliminary study

Deep Learning-Based Facial and Skeletal Transformations for Surgical Planning

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.

The Skeletal Stability of Combined Surgery First Approach and Clear Aligners in Skeletal Class III Malocclusion Correction: A Randomized Controlled Trial

The surgery first approach (SFA) and clear aligners technique can address traditional treatment defects, such as prolonged waiting times for surgery and a less desirable facial appearance due to wire aligners. However, the curative effect of the combination remains uncertain. The randomized controlled study aimed to evaluate the skeletal stability of the SFA compared to the conventional orthodontic first approach (OFA), both of which were applied with clear aligners. A total of 74 participants were randomly allocated to two groups: the SFA group (experimental) and the OFA group (control). The skeletal deviation was calculated using reconstruction models from computed tomography scans taken immediately and 6 months after surgery. The largest median deviations were detected in the y-axis of the mandible for both two groups, separately 1.36 mm in the experimental group and 1.19 mm in the control group. Apart from the maxillary yaw dimension (p = 0.005), there were no significant differences between the two groups in terms of linear and angular deviation. The experimental group had an overall treatment time of 18.05 ± 2.53 months, while the control group took 22.83 ± 3.60 months (p < 0.05). Therefore, the combined surgery-first and clear aligners treatment can achieve comparable skeletal stability to the conventional approach, while also saving significant time.