From Mesh Completion to AI Designed Crown

2025-01-09

International Conference on Medical Image Computing and Computer-Assisted Intervention

AI Summary

To address the time-consuming, manual-intensive nature of dental crown design—where balancing accuracy and manufacturability remains challenging—this paper proposes a geometry-prior-guided generative 3D completion framework. Methodologically, it integrates conditional mesh reconstruction using implicit neural representations (INRs), dental anatomical constraint embedding, multi-scale geometric adversarial training, and CAD-compatible topology optimization, enabling an end-to-end AI-driven pipeline from incomplete crown scans to clinically deployable designs. Its key innovation lies in the first unified modeling of anatomical semantic consistency, biomechanical adaptability, and CAD/CAM production readiness. Evaluated on real-world clinical data, the method achieves a mean surface error of only 0.087 mm and attains a 92% pass rate on automated Dental CAD validation; average design time per case is reduced to under 15 minutes.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Humans and AI: Game Design — Procedural Content Generation & Storytelling

Application Category

• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Problem

• Dental Crown Design
• Simplification
• Accuracy and Quality Maintenance

Innovation

• Dental Mesh Completion
• Point Cloud Analysis
• Automated Crown Design