Effects of Type and Amount of Orthodontic Tooth Movement on Digital Model Superimposition Accuracy

Objective: To assess the impact of the type or degree of tooth movement on the success of 30 model superimposition using 2 different algorithms. Methods: The sample consisted of pre-treatment digital maxillary models of 40 patients. Eight different groups were created by applying 8 different virtual setups (VS) to each model. Teeth crowns were moved by 1 mm or 2 mm in different directions (sagittal, transversal, vertical, combination) using the Ortho Analyzer software. Each model obtained from the VS was overlapped with the original model using the landmark-based (LB) and local best-fit (LBF) algorithms. In the post-superimposition assessment, the area of the palate vault which was not affected by teeth movements was selected. Both groups and algorithms were compared using the numeric data of root mean square (RMS) and percentage of perfectly matched areas (PMA). In addition, the displacement of the right canine (RC) was measured after superimposition. The comparison of the superposition outcomes among the groups was evaluated with one-way ANOVA and Kruskal-Wallis tests. The Student's t-test was used to compare the two algorithms. Results: Both the algorithms were not affected by the type of tooth movement. However, the increase in the amount of tooth movement negatively affected the performance of the LB algorithm. LBF achieved the model superimpositions more effectively and faster than LB. No difference was found in RC measurements between the LB and LBF algorithms. Conclusion: The results indicate that LBF offers more sensitive and successful 3D model superimposition. The performance of the LB algorithm was, however, acceptable for analysis of 3D tooth movement.

Automated summary

The study found that the LB algorithm was negatively affected by an increase in the amount of tooth movement, while the LBF algorithm achieved 3D model superimpositions more effectively and faster. Overall, LBF offered a more sensitive and successful approach to 3D model superimposition compared to LB, although LB's performance was still acceptable for analyzing 3D tooth movement.