Track papers across labs, topics, and trends.
Search papers, labs, and topics across Lattice.
This paper introduces a deep reinforcement learning framework for automating 3D geometric tooth alignment planning, essential for modern digital orthodontics. By formulating the planning process as a Markov Decision Process and employing the Deep Deterministic Policy Gradient algorithm, the authors optimize alignment paths while incorporating spatial constraints like collision avoidance. Experimental results on a dataset of 10,000 expert-designed treatment plans show that the proposed method significantly outperforms existing baselines in both path safety and geometric efficiency.
Automating 3D tooth alignment planning with deep reinforcement learning achieves superior path safety and efficiency compared to traditional methods.
3D geometric tooth alignment planning, which determines sequential trajectories from initial malocclusion to the final target alignment, is a cornerstone of modern digital orthodontics. This paper presents a novel deep reinforcement learning (DRL) framework to automate the generation of these alignment paths. We formulate the planning process as a Markov Decision Process (MDP) to capture its sequential decision-making nature, focusing on optimizing geometric trajectories while integrating essential spatial constraints, such as inter-dental collision avoidance and path efficiency. The proposed method leverages the Deep Deterministic Policy Gradient (DDPG) algorithm, enhanced by three key innovations: (1) a Transformer-based agent to model complex spatial interactions between teeth and manage high-dimensional state-action spaces; (2) a dynamic masking scheme that restricts movement to a sparse subset of teeth per step, better reflecting the clinical logic of sequential alignment; and (3) a two-stage curriculum learning strategy that gradually increases task difficulty to ensure training stability and efficient path discovery. We evaluate our approach on a dataset of 10K expert-designed treatment plans based on clinical data. Experimental results demonstrate that our method outperforms existing baselines in terms of path safety and geometric efficiency, providing a robust and automated solution for 3D geometric orthodontic alignment planning.