桥台
重复性
组内相关
牙冠(牙科)
口腔正畸科
牙科
牙齿修复
协议(科学)
医学
再现性
计算机科学
数学
统计
工程类
土木工程
替代医学
病理
作者
Stefan Holst,Matthias Karl,Manfred Wichmann,Ragai Edward Matta
出处
期刊:Quintessence International
日期:2011-09-01
卷期号:42 (8): 651-657
被引量:74
摘要
Objective: Assessing the level of precision entailed by the virtual fit of dental restorations is a very challenging issue. A cement space between an abutment tooth and a dental restoration is a clinical requisite that precludes the application of conventional best-fit registration protocols routinely applied in industrial precision measurements. Since two-dimensional fit assessment techniques currently used in dentistry miss important information about the third dimension, a new protocol was developed to provide threedimensional information for the virtual registration of the digitized restoration with respect to the abutment. Method and Materials: CAD/CAM was used to produce 10 titanium single crown copings for five gypsum master casts each, representing a molar prepared for a full crown. An industrial noncontact scanner was used for digitizing the components. Registration of surface data sets was achieved by a new triple-scan protocol. For statistical analysis and repeatability testing of the triple-scan protocol, mean distances of the cement space of all copings on their respective abutments were measured three times. Results: The validity of the approach is verified by intraclass correlation coefficients that revealed an almost perfect coefficient for repeatability (ICC = 0.981, P < .001) with a 95% confidence range between 0.970 and 0.989. Conclusion: The triple-scan protocol represents a reliable registration approach for surface data sets in dental applications and eliminates the limitations of conventional best-fit registration protocols when a cement space or gap is present between a restoration and its underlying abutment. Future fit assessment investigations can implement this approach of obtaining detailed information of component precision in all spatial orientation.
科研通智能强力驱动
Strongly Powered by AbleSci AI