Related Experiment Video
Updated: Jun 7, 2026
Dynamic Navigation for Dental Implant Placement
Published on: September 13, 2022
Accuracy and Efficiency of Dynamic Computer-Assisted Implant Surgery Using Three Different Registration Methods-A
Toshiki Nojiri1, Kevser Pala2,3, Ryo Yamamoto1
1Division of Fixed Prosthodontics and Oral Implantology, Department of Prosthodontics, Faculty of Dental Medicine, Iwate Medical University, Morioka, Japan.
Introduction:
Accurate implant placement with Computer-Assisted Implant Surgery (CAIS) is critical to ensure long-term success. Dynamic-CAIS systems enhance surgical precision through real-time feedback, though comparative data on their accuracy and efficiency remain limited. This study evaluated how different registration methods in d-CAIS systems influence implant placement accuracy and procedural time.
Materials And Methods:
In this laboratory study, three registration methods were assessed: ND group, XC group, and XM group. Five experienced operators placed 25 implants per group (75 total) in partially edentulous maxillary models. Postoperative CBCT scans were used to measure deviations at the implant entry point, apex, vertical depth, and angle. One-way ANOVA and Tukey's HSD test were used for statistical analysis.
Results:
Mean 3D deviations at the implant entry point were 1.06 mm (ND), 0.71 mm (XC), and 0.90 mm (XM); at the apex 1.23 mm, 0.83 mm, and 1.07 mm, respectively. Vertical depth deviations at the apex were similar across groups: 0.61 mm (ND), 0.59 mm (XC), and 0.64 mm (XM). Angular deviation was highest in the ND group (2.96°) compared to XC (1.17°) and XM (1.02°), with a significant difference between ND and XM (p = 0.028). The average procedural time was shortest in the XM group (7.20 ± 2.48 min), though differences were not statistically significant.
Conclusions:
Although registration methods minimally affect overall accuracy, system-specific factors may influence angular deviation. Future studies should prioritize clinical trials with larger sample sizes and explore the influence of operator experience and learning curves.
