Assessment of the reproducibility and precision of milling and 3D printing surgical guides

Background Technology advancement has rising in the past decade and brought several innovations and improvements. In dentistry, this advances provided more comfortable and quick procedures to both the patient and the dental surgeon, generating less predictability in the final result. Several techniques has been developed for the preparation of surgical guides aiming at the optimization of surgical procedures. The present study aimed to evaluate the reproducibility and precision of two types of surgical guides obtained using 3D printing and milling methods. Methods A virtual model was developed that allowed the virtual design of milled (n = 10) or 3D printed (n = 10) surgical guides. The surgical guides were digitally oriented and overlapped on the virtual model. For the milling guides, the Sirona Dentsply system was used, while the 3D printing guides were produced using EnvisionTEC's Perfactory P4K Life Series 3D printer and E-Guide Tint, a biocompatible Class I certified material. The precision and trueness of each group during overlap were assessed. The data were analyzed with GraphPad software using the Kolmogorov-Smirnov test for normality and Student's t test for the variables. Results The Kolmogorov-Smirnov test showed a normal distribution of the data. Comparisons between groups showed no statistically significant differences for trueness (p = 0.529) or precision (p = 0.3021). However, a significant difference was observed in the standard deviation of mismatches regarding accuracy from the master model (p < 0.0001). Conclusions Within the limits of this study, surgical guides fabricated by milling or prototyped processes achieved similar results.

Automated summary

This study compared the reproducibility and precision of surgical guides obtained using 3D printing and milling methods. The results showed that both methods achieved similar results in terms of trueness and precision, but there was a significant difference in the standard deviation of mismatches regarding accuracy from the master model.