Accuracy of implant site preparation in robotic navigated dental implant surgery
Bibliographic record
Abstract
BACKGROUND: Modern technological advancements have led to increase in the development of surgical robots in dentistry, resulting in excellent clinical treatment outcomes. PURPOSE: This study aimed to determine the accuracy of automatic robotic implant site preparation for different implant sizes by correlating planned and posttreatment positions, and to compare the performance of robotic and human freehand drilling. METHOD: Seventy-six drilling sites on partially edentulous models were used, with three different implant sizes (Ø = 3.5 × 10 mm, 4.0 × 10 mm, 5.0 × 10 mm). The robotic procedure was performed using software for calibration and step-by-step drilling processes. After robotic drilling, deviations in the implant position from the planned position were determined. The angulation, depth, and coronal and apical diameters on the sagittal plane of sockets created by human and robotic drilling were measured. RESULTS: The deviation of the robotic system was 3.78° ± 1.97° (angulation), 0.58 ± 0.36 mm (entry point), and 0.99 ± 0.56 mm (apical point). Comparison of implant groups showed the largest deviation from the planned position for 5 mm implants. On the sagittal plane, there were no significant differences between robotic and human surgery except for the 5-mm implant angulation, indicating similar quality between human and robotic drilling. Based on standard implant measurements, robotic drilling exhibited comparable performance to freehand human drilling. CONCLUSIONS: A robotic surgical system can provide the greatest accuracy and reliability regarding the preoperative plan for small implant diameters. In addition, the accuracy of robotic drilling for anterior implant surgery can also be comparable to that of human drilling.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".