Magnetic resonance imaging based <scp>computer‐guided</scp> dental implant surgery—A clinical pilot study
Bibliographic record
Abstract
BACKGROUND: Computer-guided implant surgery is currently based on radiographic techniques exposing patients to ionizing radiation. PURPOSE: To assess, whether computer-assisted 3D implant planning with template-guided placement of dental implants based on magnetic resonance imaging (MRI) is feasible. MATERIALS AND METHODS: 3-Tesla MRI was performed in 12 subjects as a basis for prosthetically driven virtual planning and subsequent guided implant surgery. To evaluate the transferability of the virtually planned implant position, deviations between virtually planned and resulting implant position were studied. Matching of occlusal surfaces was assessed by comparing surface scans with MRI-derived images. In addition, the overall image quality and the ability of depicting anatomically important structures were rated. RESULTS: MRI-based guided implant surgery with subsequent prosthetic treatment was successfully performed in nine patients. Mean deviations between virtually planned and resulting implant position (error at entry point 0.8 ± 0.3 mm, error at apex 1.2 ± 0.6 mm, angular deviation 4.9 ± 3.6°), mean deviation of occlusal surfaces between surface scans and MRI-based tooth reconstructions (mean 0.254 ± 0.026 mm) as well as visualization of important anatomical structures were acceptable for clinical application. CONCLUSION: Magnetic resonance imaging (MRI) based computer-assisted implant surgery is a feasible and accurate procedure that avoids exposure to ionizing radiation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".