Purpose: Deep learning algorithms offer the potential to automate dental crown generation, reducing time-intensive manual design in dental laboratories. However, achieving crowns suitable for direct clinical use requires both geometric precision and functional accuracy to minimize post-generation adjustments. Current approaches focus primarily on shape completion without explicitly modeling critical spatial relationships, including margin line boundaries, occlusal contact patterns, and adjacent tooth interactions. These limitations result in generated crowns lacking spatial accuracy necessary for direct clinical application. Approach: We present a comprehensive framework employing transformer encoder-decoder architecture integrated with differentiable Poisson surface reconstruction for direct dental crown mesh generation. The framework incorporates two key innovations to address clinical limitations. First, margin line data is integrated as direct network input, concatenated with master and antagonist arch geometries, providing explicit boundary constraints during crown generation. Second, spatial constraint losses ensure anatomically valid relationships through antagonist interaction loss for proper occlusal contact patterns and intersection loss to prevent crown penetration into adjacent teeth. Results: The proposed framework achieves substantial improvements over existing state-of-the-art methods, with geometric accuracy gains ranging from 35.9% to 40.6% across evaluation metrics. Margin line integration yields a 31.2% improvement in geometric precision, with maximum boundary errors reduced from 1.37 to 0.74 mm and a 58.4% reduction in variability. Antagonist interaction loss provides 9.51% improvement in occlusal alignment, while intersection loss substantially reduces crown penetration into adjacent teeth. Conclusions: Substantial performance improvements validate the effectiveness of integrating spatial constraint modeling and direct margin line input into the generation process, establishing a foundation for clinical deployment of automated dental crown design systems.
Hosseinimanesh et al. (Sat,) studied this question.