Automatic three‐dimensional analysis of bone volume and quality change after maxillary sinus augmentation
Bibliographic record
Abstract
INTRODUCTION: Maxillary sinus augmentation is a widely used surgical procedure to increase the bone volume before implant placement. In order to predict the stability of the implant, analysis of the change in bone volume and quality after a sinus graft procedure is necessary. The purpose of this study was to analyze the change in volume and quality of bone graft material after maxillary sinus augmentation using cone beam computed tomography (CBCT). METHODS AND MATERIALS: Maxillary sinus lift procedures using bovine bone materials (Bio-Oss, Geistrich, Swiss) without immediate implantation were performed at the Pusan National University Dental Hospital in 22 patients, from 2014 to 2017. CBCT images were captured before surgery (T1), a day after surgery (T2), and after 4 to 7 months at follow-up (T3). The T2 and T3 images were registered to the T1 image using histogram matching and intensity-based registration. A total of 30 sinuses were analyzed three-dimensionally (3-D), using self-made software MATLAB 2018a (MathWorks, Natick, Massachusetts). The volume and structural indices of the bone graft material were measured and analyzed. RESULTS: The average volume of graft material showed a decrease, while the average gray value showed an increase during the follow-up period, but these changes were not statistically significant. The structural indices of the graft material after histogram matching showed a significant difference in homogeneity, connectivity, thickness, and roughness at the postoperative follow-up. CONCLUSIONS: The volume and gray value showed no statistically significant changes after the maxillary sinus lift procedures. The results of this study show that structural analysis using histogram matching can be used as a promising tool to analyze the quality of graft materials.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".