Prediction of Dental Implants Primary Stability With Cone Beam Computed Tomography‐Based Homogenized Finite Element Analysis
Bibliographic record
Abstract
ABSTRACT Objectives Predicting implant stability preoperatively remains a challenge. Computed tomography (CT) based finite element (FE) simulations virtually evaluate the mechanical performance of the bone‐implant construct. However, translation requires trustworthy simulations based on clinically relevant CT data. The aim of the present study was to evaluate the prediction accuracy of FE models created from cone‐beam CT (CBCT) images against experimental results of primary implant stability in human bone specimens. Material and Methods Twenty‐three dental implants were inserted into bone biopsies extracted from three cadaveric mandibles, and biomechanical testing was performed to determine the load‐bearing capacity in a previous study. CBCT‐based sample‐specific homogenized FE (hFE) models were used to predict ultimate force. The accuracy of the CBCT‐based hFE model predictions was compared to the experimental results and to previous μCT‐based hFE models. Results The ultimate load predicted by the CBCT‐based hFE models correlated well with the experimental one (R2 = 0.66) and was a better estimator than the peri‐implant CBCT‐based bone density (R2 = 0.39) or μCT‐based bone volume fraction (R2 = 0.57). Although the results of the two hFE models were strongly correlated (R2 = 0.91), the μCT‐based simulation better predicted the experiments (R2 = 0.81). Conclusion By showing that CBCT‐based hFE modeling can predict primary stability, this study represents an important step forward toward the clinical translatability of these numerical models as preoperative predictors of primary stability. Nevertheless, several challenges remain to be addressed, such as the lack of an accurate and quantitative way to calibrate CBCT images.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".