Buccal bone thickness assessment for immediate anterior dental implant planning: A pilot study comparing cone‐beam computed tomography and 3D double‐echo steady‐state MRI
Bibliographic record
Abstract
PURPOSE: To evaluate image quality and diagnostic accuracy of buccal bone thickness assessment in maxillary and mandibular anterior region using cone-beam computed tomography (CBCT) and 3-dimensional double-echo steady-state (DESS) MRI for preoperative planning of immediate dental implants in healthy individuals. METHODS: One hundred and twenty teeth in 10 volunteers were retrospectively evaluated for image quality and artifacts using Likert scale (4 = excellent to 0 = decreased). Buccal bone thickness was measured at three measurement points (M1 = 2 mm from the cementoenamel junction, M2 = middle of the root, and M3 = at the root apex) for each tooth in the maxillary (13-23) and the mandibular anterior region (33-43). Descriptive statistics and two-way ANOVA with Tukey's Post-hoc test were performed to evaluate the significant differences (α = 0.05) between both imaging modalities. RESULTS: Image quality showed little to no artifacts and enabled confident diagnostic interpretation (CBCT (3.72 ± 0.46); MRI (3.65 ± 0.49)), with no significant differences between both imaging modalities (p > 0.05). Regarding the assessment of buccal bone thickness at M1-M3 for the teeth 13-23 and 33-43, no significant differences were noted (p > 0.05). MRI demonstrated slight, nonsignificant overestimation of thickness with the canines having mainly a thick buccal bone wall, where thin buccal wall was evident for the central incisors. CONCLUSION: Black bone MRI sequences, such as 3D-DESS MRI, for immediate implant planning provided confidential diagnostic accuracy in bone thickness assessment without significant disadvantages compared to CBCT. Thus, the implementation of no-dose protocols for dental rehabilitation using an immediate loading approach seems promising and could further improve the treatment strategy for dental rehabilitation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".