Virtual implant planning and fully guided implant surgery using magnetic resonance imaging—Proof of principle
Bibliographic record
Abstract
OBJECTIVES: To present a workflow of virtual implant planning and guided implant surgery with magnetic resonance imaging (MRI) and virtual dental models without the use of ionizing radiation. METHODS: Five patients scheduled for implant placement underwent an MR examination at three Tesla using individualized 2D and 3D turbo spin-echo (TSE) sequences and dedicated head coils. The MRI data and virtual dental models derived from either optical model scans or intraoral scans were imported to a virtual implant planning software (coDiagnostiX, Dental Wings, Montreal, Canada). Virtual prosthetic planning and implant planning were performed regarding the hard and soft tissue anatomy. A drill guide was designed on the virtual dental model using computer-aided design (CAD) and manufactured in-house, using a 3D printer (Eden 260V, Stratasys, Eden Prairie, MN, USA). RESULTS: The MRI displayed all relevant anatomical structures for dental implant planning such as cortical and cancellous bone, floor of the nasal and maxillary sinus, inferior alveolar nerve and neighboring teeth. The manual alignment of virtual dental models with the MRI was possible using anatomical landmarks. Dental implant planning, CAD/CAM of a drill guide and fully guided implant placement were successfully performed. CONCLUSIONS: Guided implant surgery is feasible with MRI without ionizing radiation. Further studies will have to be conducted to study the accuracy of the presented protocol and compare it to the current workflow of guided surgery using CBCT.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".