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Updated: Jan 10, 2026
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
Published on: February 23, 2024
Evaluating the relationship between clinician experience and accuracy in robotic-assisted dental implant placement: a
Miao Wang1,2,3, Hui Xia Zhou1,2,3, Cheng Peng Lv1,2
1Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an, China.
Objectives:
This study aims to evaluate the accuracy of Level of Autonomy 2 (LOA2) robotic-assisted implant placement by dentists with varying experience, analyze learning curves, and identify accuracy-related risk factors.
Materials And Methods:
This retrospective study included 362 patients (585 implants) who underwent LOA2 robotic-assisted implant placement (April 2022-April 2024). Six novice clinicians (no robotic experience) and two expert clinicians (> 50 robotic surgeries) were included. Seven deviation parameters were assessed by comparing planned and actual implant positions. Thirteen potential risk factors were evaluated using statistical analyses in R.
Results:
After extraoral robotic training, novice clinicians achieved accuracy comparable to experts, with minor exceptions: vertical platform deviation (0.373 ± 0.566 mm vs. 0.255 ± 0.438 mm, p = 0.007) and vertical apex deviation (0.348 ± 0.488 mm vs. 0.249 ± 0.437 mm, p = 0.009). Learning curves for both groups remained flat (coefficient < 0.001), with no significant accuracy improvement over cases increase. Key risk factors for increased deviations included: immediate/early implantation; implant length > 10 mm; poor bone quality; maxillary placement (p < 0.05), and machined-related factors.
Conclusions:
The LOA 2 robotic-assisted implant system minimizes the dependence of implant placement accuracy on clinician experience, with only minor differences in vertical deviations between novices and experts. Even though accuracy remains at a high level, high deviations are associated not only with traditional risk factors but also with machine-related risks.
