Comparison of Accuracy and Systematic Precision Between Autonomous Dental Robot and Static Guide: A Retrospective Study
Bibliographic record
Abstract
OBJECTIVE: This study aimed to compare the implant placement accuracy and systematic precision between Robotic-Assisted Implant Surgery (RAIS) and Fully Guided Static Computer-Assisted Implant surgery (sCAIS), as well as to explore factors influencing implant placement accuracy. MATERIALS AND METHODS: Patients who underwent digital guided implant surgery between October 2022 and July 2024 were included in this study. The patients were divided into the RAIS and sCAIS groups. Post-operative CBCT scans were performed to measure three-dimensional (3D) deviations and overlap rate (OR) of each implant. The differences in 3D deviations and OR between the two CAIS methods were analyzed, along with factors that could impact implant accuracy, such as anterior versus posterior sites, maxilla versus mandible, bone defects, implant morphology, and free-end sites. RESULTS: 254 patients were enrolled, with 125 patients receiving 227 implants in the RAIS group and 129 patients receiving 227 implants in the sCAIS group. The RAIS group demonstrated significantly better performance than the sCAIS group in coronal global deviation (0.69 [0.52] mm vs. 0.97 [0.64] mm), apical global deviation (0.75 [0.57] mm vs. 1.40 [0.82] mm), angular deviation (1.51 [1.43]° vs. 3.44 [2.78]°), and OR (80 [17]% vs. 64 [20]%) (p < 0.001). There were no significant differences between the two groups in coronal horizontal mesiodistal deviation at the anterior sites, nor in coronal horizontal mesiodistal and buccolingual deviations at the posterior sites. CONCLUSION: In most edentulous cases, implant placement accuracy assisted by the RAIS system was significantly higher than that of the sCAIS system. The control of Coronal horizontal (mesiodistal) deviation by sCAIS is comparable to that of RAIS. Additionally, the RAIS system demonstrated better systematic precision.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".