人工智能驱动的皇冠生成:对 mandibular 第一个骨恢复点云完成模型的比较分析
Zhiyuan Shu1, Miaomiao Tian1, Hongbo Wei1
1State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Center for Oral Diseases, Shaanxi Engineering Research Center for Dental Materials and Advanced Manufacture, Department of Oral Implants, School of Stomatology, The Fourth Military Medical University, Xi'an 710072, China.
Journal of dentistry
|February 18, 2026
概括
PoinTr AI 模型为植入器手术规划生成准确的牙冠. 这种人工智能方法提高了数字牙科工作流程的精度和效率.
科学领域:
- 人工智能在牙科中的应用
- 3D点云处理 3D点云处理
- 牙修术和植入学 牙修术和植入学
背景情况:
- 开发解剖学精确的牙假体对于成功的植入物手术至关重要.
- 人工智能 (AI) 提供了自动化和提高牙冠生成准确性的潜力.
- 评估现有的AI模型对于特定的临床应用,如假肢引导植入体手术是必要的.
研究的目的:
- 适应和评估三种人工智能驱动的点云完成模型 (PF-Net,PCN,PoinTr) 以生成解剖学准确的牙冠.
- 评估这些人工智能模型的几何准确性,临床适用性和计算效率,用于下第一个冠.
- 通过精确的虚拟假体生成,推进假体引导植入体手术规划.
主要方法:
- 使用了120个口腔内扫描数据集,部分牙模型作为输入,自然皇冠作为基本真相.
- 三个人工智能模型 (PF-Net,PCN,PoinTr) 使用Chamfer距离损失进行训练.
- 模型被评估在几何准确度上 (查默尔距离,豪斯多夫距离,RMSE,M.N.D. ),临床尺寸和生成时间.
主要成果:
- PoinTr显示出卓越的临床疗效和几何准确性,与最小的半径直径偏差 (0.14±0.33毫米).
- PoinTr实现了最小的表面误差 (Chamfer距离:0.10毫米;豪斯多夫距离:0.22毫米) 和平衡精度与效率 (5.52秒/皇冠).
- PF-Net是最快的 (3.22秒/皇冠),但有不可接受的形态偏差;PCN显示性能低于PoinTr.
结论:
- PoinTr产生的牙冠具有最佳的形态忠诚度和精确的维度控制为假肢引导植入体手术.
- 基于PoinTr的方法提高了植入物规划和传统固定假牙的数字工作流效率和准确性.
- 这种人工智能工具改善了手术规划,手术指南设计,并减少了恢复性牙科技术人员的依赖.
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