Orthodontic Collision Detection Using 3D Grid Segmentation
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Solution Overview
Problem
Current orthodontic simulation systems fail to accurately detect collisions between teeth during treatment, leading to inaccuracies in simulated tooth movement and limitations in planning effective orthodontic treatments, while also requiring significant processing resources.
Innovation Solution
A method using 3D point cloud representations and axis-aligned bounding boxes to detect collisions between teeth and orthodontic appliances by mapping overlapping areas onto a 3D grid, determining distances between vector points, and adjusting the treatment plan to avoid collisions, thereby reducing computational demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional collision detection methods are used in orthodontic simulation, then collision detection capability is provided, but computational resources are excessively consumed
Solution Approach 1:
The simulation space is divided into a 3D grid with multiple cells, and teeth models are segmented into multiple portions. Collision detection is performed separately in each cell for relevant tooth portions, rather than checking all teeth against all other teeth in the entire simulation space. This segmentation dramatically reduces the number of computational operations required while maintaining detection accuracy.
Solution Approach 2:
The patent introduces a spatial grid dimension to organize the simulation space. By mapping teeth portions to specific grid cells based on their spatial coordinates, the system transforms a complex N-body collision problem into a series of simpler local checks within individual cells, reducing computational complexity from O(N²) to approximately O(N) where N is the number of teeth portions.
2Measurement precision
If comprehensive collision detection is performed between all teeth, then collision accuracy is improved, but processing time increases
Solution Approach 1:
The simulation space is divided into a 3D grid with multiple cells, and teeth models are segmented into multiple portions. Collision detection is performed separately in each cell for relevant tooth portions, rather than checking all teeth against all other teeth in the entire simulation space. This segmentation dramatically reduces the number of computational operations required while maintaining detection accuracy.
Solution Approach 2:
The system performs preliminary spatial indexing by assigning each tooth portion to a grid cell before collision detection. This preliminary organization allows the system to quickly identify which tooth portions are spatially close and require collision checking, eliminating the need for exhaustive pairwise comparisons and significantly reducing processing time.
Data Source
AI summary
Methods of determining an orthodontic treatment comprising: obtaining 3D model of simulated position of teeth following proposed orthodontic treatment; the 3D model comprising a point cloud representation having vector points representative of teeth surface; generating an axis aligned boundary box around each tooth; identifying a pair of tooth-tooth bounding boxes of adjacent teeth that intersect; defining an area of overlap of the pair of tooth-tooth bounding boxes; mapping the area of overlap onto the plurality of vector points, the plurality of vector points populating a 3D grid, having cells, of a simulation space; identifying a subset of the cells including the mapped overlap area, and for only the subset of the cells determining a distance between the vector points relating to adjacent teeth; and determining the proposed orthodontic treatment as the determined orthodontic treatment if the determined distance is more than a predetermined distance.