This study aimed to evaluate the effects of bone density, cortical bone thickness, and implant design on the accuracy of implant placement using a novel semi-autonomous robotic-assisted surgery system (sa-RASS). A total of 160 implants were placed in artificial bone models simulating four bone densities (D1, D2, D3, D4) and three cortical bone thicknesses (0.5, 1, 1.5 mm) using sa-RASS. Two implant designs (self-tapping and non-self-tapping) were evaluated under standardized cortical bone thickness conditions. The postoperative CBCT data and preoperative surgical plan were superimposed to calculate the deviations of the implant. Deviations were quantified for platform/apex positions (global, horizontal, vertical) and implant angulation. The sa-RASS achieved mean deviations of 0.58 ± 0.19 mm at platform, 0.60 ± 0.24 mm at apex, and 1.46° ± 0.78° for angulation. Bone density significantly influenced accuracy (p < 0.05), with maximum deviations in medium-density (D2/D3) models and minimal errors in high-density (D1) and low-density (D4) groups. Cortical thickness exhibited a moderate positive correlation with linear deviations (platform: r = 0.598; apex: r = 0.593; both p < 0.001). Self-tapping implants demonstrated superior precision compared to non-self-tapping designs (p < 0.05), with reduced deviations at both platform (0.48 ± 0.16 mm) and apex (0.49 ± 0.16 mm). This in vitro study demonstrated that bone condition and implant design significantly influence the accuracy of sa-RASS. Understanding these factors can help optimize robotic-assisted implant placement and improve clinical outcomes. Bone condition and implant design significantly affect the accuracy of robotic-assisted implant placement. Preoperative assessment and proper implant selection can enhance precision and improve clinical outcomes.
Tang et al. (2025) studied this question.