Influence of Bone Condition and Implant Design on Accuracy of Semi‐Autonomous Robotic Dental Implant Surgery In Vitro
Bibliographic record
Abstract
OBJECTIVES: 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). MATERIALS AND METHODS: 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. RESULTS: 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). CONCLUSIONS: 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. CLINICAL SIGNIFICANCE: 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".