Abstract
Objectives: The aim of this in vitro study was to compare the accuracy of printed implant casts from digital impressions with two intra-oral scanners (10S) to stone casts from conventional impressions. The hypothesis was that printed casts would be more accurate than stone casts from conventional impressions.
Materials and methods: A mandibular stone cast with Kennedy class II edentulism was fabricated using two internal connection tissue-level implants at 30 degrees to each other (Replace Select RP, Nobel Biocare) to serve as master. Digital impressions (n = 10) were made with the white light (WL) and Active Wavefront Sampling technology (AWST) IOS. The resultant standard tessellation language (STL) datasets were used to print implant casts through stereolithography (SLA) prototyping. The conventional casts (n = 10) were produced with splinted open tray impression technique and polyether material in type IV stone. The master cast and all groups were digitized with lab reference scanner. The test groups STL datasets were superimposed to master cast STL in inspection software (Geomagic control 2015) to calculate root-mean-square error.
Results: The conventional, WL IOS and AWST IOS groups had mean values of 53.49 mu m (SD 9.47), 108.09 mu m (SD 9.59) and 120.39 mu m (SD 5.91), respectively. The Shapiro-Wilk test showed no evidence of nonnormality (p = 0.131) and Levene's test showed no evidence of heterogeneity of variance (p = 0.518). The one-way ANOVA demonstrated a statistically significant difference (p < 0.001). Tukey's honest significant difference (HSD) showed statistically significant differences between all groups: for the comparison of AWST IOS and WL IOS, the p-value was 0.009, and the p-values of the other post hoc tests were <0.001.
Conclusion: Printed casts generated from digital impressions for partially edentulous posterior mandibular arches had inferior accuracy to conventional stone casts fabricated from splinted open tray impressions. The printed casts from WL IOS had better accuracy compared to AWST IOS.