在不同封闭场景下,前后皇冠的人工智能驱动自动设计
Nan Hsu Myat Mon Hlaing1, Gülce Çakmak2,3, Duygu Karasan4
1Department of Prosthodontics, Seoul National University School of Dentistry, Seoul, Republic of Korea.
概括
尽管表面偏差较高,但深度学习 (DL) 的CAD软件产生了比传统的自动化 (CA) 软件更高质量的后部冠状. 闭塞类型显著影响自动化冠状设计的准确性和临床结果.
科学领域:
- 数字牙科
- 计算机辅助设计 (CAD)
- 牙科中的人工智能 (AI)
背景情况:
- 使用CAD软件的自动化设计正在发展.
- 对传统方法进行基于人工智能的软件评估对于临床采用至关重要.
- 封闭类型对数字皇冠制造提出了独特的挑战.
研究的目的:
- 将使用深度学习 (DL) 和传统自动化 (CA) CAD软件的自动生成冠状的几何精度和临床质量进行比较.
- 评估不同类型的遮对冠状设计结果的影响.
- 确定人工智能驱动的冠状设计技术的临床适用性.
主要方法:
- 为前后冠准备了五种不同遮的类型牙模型.
- 使用DL (Dentbird) 和CA (3Shape) 软件生成冠状设计.
- 分析了表面偏差 (RMS值) 和临床质量 (FDI标准).
- 进行了统计测试 (Scheirer-Ray-Hare,Fisher的精确测试).
主要成果:
- DL软件显示的表面偏差 (RMS) 比CA软件高 (p < 0.001).
- DL产生的牙冠具有显著更好的临床质量 (FDI得分),特别是后牙 (p < 0.05).
- 在前腹膜病例中,DL软件的结果不那么有利.
结论:
- 封闭条件和CAD软件类型显著影响自动化皇冠设计的准确性和质量.
- 与CA软件相比,基于DL的CAD软件为后部冠状提供了更高的临床质量.
- 需要进一步开发人工智能软件,以便在各种闭塞场景中得到一致的临床接受.
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