AI4TRT: Automatic Simulation of Teeth Restoration Treatment

AI4TRT: Automatic Simulation of Teeth Restoration Treatment

Feihong Shen, Yuer Ye

Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence
AI4Tech: AI Enabling Technologies. Pages 9322-9329. https://doi.org/10.24963/ijcai.2025/1036

Visualizing restoration treatments is a crucial task in dentistry. Traditionally, dentists drag the standard template tooth line onto the inner image from the front view to simulate the outcome of the restoration. This process lacks the precision needed for patient presentation. We find that calculating the camera pose and the relative positions of the upper and lower jaws can enhance visualization accuracy and efficiency while assisting dentists in treatment design. In this work, we leverage the optical flow model and a customized point renderer to help dentists show the treatment outcome to the patient. Specifically, we take the 3D scan model and the intraoral image pair as input. Our framework automatically outputs the camera pose and the relative position of the upper and lower jaws. With these parameters, dentists can directly design the restoration treatment on the 3D scan model without caring about the 2D visualization. Then the designed tooth line and other simulation modalities can be rendered on the intraoral image with our customized renderer. Our framework relieves the labor of dentists and shows the case precisely.
Keywords:
Domain-specific AI4Tech: AI4Care and AI4Health
Advanced AI4Tech: Data-driven AI4Tech
AI4Tech infrastructure/systems: AI office software