The influence of the model superimposition method on the assessment of accuracy and predictability of setup models

The aim of this study was to evaluate the influence of different superimposition methods on the accuracy and predictability of conventional and virtual diagnostic setups.

Ten finished cases were used to make a conventional setup and a virtual setup. Second molars were not moved in the two setup situations to allow a reference for superimposition. Conventional and virtual setups were superimposed and compared by second molar registration and the whole surface best fit method (WSBF). Conventional and virtual setups were compared to the posttreatment models with WSBF and palatal rugae best fit (PRBF). Anterior, intermediate, and posterior regions of the dental arches were compared. The paired t-test was used to compare the mean differences between conventional and virtual setups, posttreatment models and both conventional and virtual setups by the WSBF method, and between maxillary posttreatment and virtual setup models using the WSBF and PRBF methods.

Conventional and virtual setups differed depending on the two superimposition methods used. Superimposition of the posttreatment models and both setups using WSBF presented no statistically significant differences. There were statistically significant differences between posttreatment and virtual setup models using WSBF and PRBF superimposition methods.

The model superimposition method influenced the assessment of accuracy and predictability of setup models. There were statistically significant differences between the maxillary posttreatment and virtual setup models using the WSBF and the PRBF superimposition methods. It is important to establish stable structures to evaluate the accuracy and predictability of setup models.