A Novel Dental Implant Guided Surgery Based on Integration of Surgical Template and Augmented Reality
Bibliographic record
Abstract
BACKGROUND: Stereoscopic visualization concept combined with head-mounted displays may increase the accuracy of computer-aided implant surgery. PURPOSE: The aim of this study was to develop an augmented reality-based dental implant placement system and evaluate the accuracy of the virtually planned versus the actual prepared implant site created in vitro. MATERIALS AND METHODS: Four fully edentulous mandibular and four partially edentulous maxillary duplicated casts were used. Six implants were planned in the mandibular and four in the maxillary casts. A total of 40 osteotomy sites were prepared in the casts using stereolithographic template integrated with augmented reality-based surgical simulation. During the surgery, the dentist could be guided accurately through a head-mounted display by superimposing the virtual auxiliary line and the drill stop. The deviation between planned and prepared positions of the implants was measured via postoperative computer tomography generated scan images. RESULTS: Mean and standard deviation of the discrepancy between planned and prepared sites at the entry point, apex, angle, depth, and lateral locations were 0.50 ± 0.33 mm, 0.96 ± 0.36 mm, 2.70 ± 1.55°, 0.33 ± 0.27 mm, and 0.86 ± 0.34 mm, respectively, for the fully edentulous mandible, and 0.46 ± 0.20 mm, 1.23 ± 0.42 mm, 3.33 ± 1.42°, 0.48 ± 0.37 mm, and 1.1 ± 0.39 mm, respectively, for the partially edentulous maxilla. There was a statistically significant difference in the apical deviation between maxilla and mandible in this surgical simulation (p < .05). CONCLUSIONS: Deviation of implant placement from planned position was significantly reduced by integrating surgical template and augmented reality technology.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".