Accuracy of Two Robotic Computer‐Aided Implant System Registration Methods for Dental Implantation: A Prospective Study
Bibliographic record
Abstract
BACKGROUND: Robot-assisted implant surgery has been shown to achieve high levels of accuracy. However, there is currently a paucity of clinical studies evaluating the accuracy of marker-based intraoral scanner (IOS) registration (IR) methods. PURPOSE: The purpose of this study was to compare the accuracy of the marker-based cone beam computed tomography (CBCT) registration (CR) method and the IR method in the dental implant in the robotic computer-aided implant system (R-CAIS). MATERIALS AND METHODS: This retrospective study included 20 participants, with 10 undergoing implant surgery using the CR method within a robotic system, and the remaining 10 receiving implants using the IR method. Preoperative CBCT images used for implant planning were aligned with the postoperative CBCT images to assess and quantify positional deviations in implant placement. The primary outcome measures were FRE, entry deviation, apical deviation, and angular deviation. A Student's t-test was performed to compare differences between the two groups, with a p-value of < 0.05 considered statistically significant. RESULTS: The mean ± standard deviation values for FRE were 0.027 ± 0.007 mm for the CR group and 0.031 ± 0.006 mm for the IR group (p = 0.149). The mean ± standard deviation values for entry deviation were 0.58 ± 0.11 mm for the CR group and 0.53 ± 0.15 mm for the IR group (p = 0.072). The mean ± standard deviation values for apical deviation were 0.52 ± 0.12 mm for the CR group and 0.50 ± 0.14 mm for the IR group (p = 0.730). The mean ± standard deviation values for apical deviation were 1.10 ± 0.34 mm for the CR group and 1.17 ± 0.23 mm for the IR group (p = 0.730). CONCLUSIONS: In R-CAIS, the IR method demonstrated accuracy comparable to that of the CR method, with both methods yielding clinically satisfactory outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".