Transformer-based Tooth Alignment Prediction with Occlusion and Collision Constraints

The planning of digital orthodontic treatment requires providing tooth alignment, which relays clinical experiences heavily and consumes a lot of time and labor to determine manually. In this work, we proposed an automatic tooth alignment neural network based on Swin-transformer. We first re-organized 3D point clouds based on dental arch lines and converted them into order-sorted multi-channel textures, improving both accuracy and efficiency. We then designed two new orthodontic loss functions that quantitatively evaluate the occlusal relationship between the upper and lower jaws. They are important clinical constraints, first introduced and lead to cutting-edge prediction accuracy. To train our network, we collected a large digital orthodontic dataset in more than 2 years, including various complex clinical cases. We will release this dataset after the paper's publishment and believe it will benefit the community. Furthermore, we proposed two new orthodontic dataset augmentation methods considering tooth spatial distribution and occlusion. We compared our method with most SOTA methods using this dataset, and extensive ablation studies and experiments demonstrated the high accuracy and efficiency of our method.