Accuracy of an autonomous dental implant robotic system in dental implant surgery

Abstract

Statement of problem

Autonomous dental implant robot systems (ADIRSs) have been marketed for dental implant surgery; however, research on their accuracy is limited.

Purpose

The purpose of this clinical study was to examine the accuracy of an ADIRS.

Material and methods

Seventy-four participants with partial edentulism underwent implant surgery by using the ADIRS. Before surgery, a cone beam computed tomography (CBCT) scan was conducted, and its image data were fed into the ADIRS navigation and positioning system for iterative reconstruction and surgical path planning. After local anesthesia administration, area disinfection, and patient registration, the robot prepared the osteotomy and placed the implant under guidance of the navigation system. A postoperative CBCT scan was made to assess deviations between the planned and actual implant positions. The global coronal, apical, and angular deviations were measured and analyzed. A generalized linear mixed model (GLMM) was used to compare the differences in major outcome variables (α=.05).

Results

All implant procedures were successfully carried out without any complications during surgery. A total of 86 implants were placed in 74 participants. The mean ±standard deviation global coronal deviation, global apical deviation, and angular deviation was 0.61 ±0.20 mm (95% CI: 0.23 to 1.0 mm), 0.79 ±0.32 mm (95% CI: 0.17 to 1.41 mm), and 2.56 ±1.10 degrees (95% CI: 0.42 to 4.70 degrees), respectively. The findings from the GLMM revealed that the implant region (premolar, molar; maxilla, mandible; left, right), implant length, and surgeon did not significantly impact accuracy (P>.05).

Conclusions

ADIRS showed high accuracy for implant placement because of the control of angular deviation and axial errors. The implant region, implant length, and surgeon had no significant influence on the accuracy of implant placement. Nevertheless, more clinical studies are required to support evidence-based clinical outcomes.