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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".