AI-driven crown generation: A comparative analysis of point cloud completion models for mandibular first molar restoration.

OBJECTIVES

This study aimed to adapt and evaluate three established artificial intelligence (AI)-driven point cloud completion models-PF-Net, PCN, and PoinTr-for generating anatomically accurate dental crowns for mandibular first molars, thereby advancing prosthetically guided implant surgery planning.

METHODS

A dataset of 120 intraoral scans from non-pathological dentitions was processed into 3D point clouds. Partial dentition models (with tooth 36 missing) served as inputs, while the corresponding natural crowns served as the ground truth. The dataset was partitioned into training (90), validation (20), and test (10) sets. All models were trained identically using Chamfer Distance loss and evaluated for geometric fidelity (Chamfer Distance, Hausdorff Distance, Root Mean Square Error, Mean Negative Deviation), clinical applicability (buccolingual/mesiodistal diameters), and computational efficiency. Statistical analyses included ANOVA/Tukey's HSD or Kruskal-Wallis/Dunn's tests (α = 0.05).

RESULTS

PoinTr demonstrated superior clinical efficacy and geometric accuracy, exhibiting the lowest mesiodistal diameter deviation (0.14 ± 0.33 mm, p = 0.008 vs. PCN) and minimal surface errors (Chamfer Distance: 0.10 mm; Hausdorff Distance: 0.22 mm). PF-Net exhibited the fastest generation time (3.22 s/crown) but clinically unacceptable morphological deviations (>6 mm). PoinTr balanced accuracy (significantly outperforming PF-Net/PCN in all geometric metrics, p < 0.05) and efficiency (5.52 s/crown).

CONCLUSION

PoinTr generates crowns with optimal morphological fidelity to natural teeth and precise dimensional control, enabling virtual prostheses that align with prosthetically guided implant principles. Its integration into digital workflows can enhance surgical planning precision and efficiency while reducing technician dependency.

CLINICAL SIGNIFICANCE: The proposed PoinTr-based approach provides a clinically significant tool for prosthetically guided implant surgery by generating crowns with high anatomical fidelity and dimensional precision, directly informing optimal implant positioning and surgical guide design. Furthermore, the methodology extends beyond implant planning, offering an efficient and automated solution for designing tooth-supported crowns in conventional fixed prosthodontics, thereby enhancing accuracy, consistency, and workflow efficiency in digital restorative dentistry.