Linear measurements of sinus floor elevation based on voxel‐based superimposition of cone beam computed tomography images
Bibliographic record
Abstract
BACKGROUND: Postsurgical evaluation of sinus floor elevation regularly involves linear measurements of the elevated volumes in the cone beam computed tomography (CBCT) images. The accuracy of measurements could be compromised due to ill-defined sinus floor outline if implants are placed simultaneously. PURPOSE: The aim was to examine a CBCT superimposition method to improve the measurement accuracy. MATERIALS AND METHODS: Twenty patients who received transalveolar sinus floor elevation with immediate implantation were enrolled. CBCTs before and after surgery were transformed into digital imaging and communications in medicine format and imported into the Dolphin Imaging software. Voxel-based superimposition was automated to merge the files. In the superimposed image, parameters including alveolar bone height, protruded implant length, and total elevated height were measured. The superimposition and measurements were performed independently by two examiners and in two timepoints with 1-week time interval. We used intraclass correlation coefficient (ICC) to analyze the interexaminer and intraexaminer agreements. RESULTS: Of measured parameters, the mean of difference between two timepoints ranged from 0.18 to 0.26 mm by examiner 1, and from 0.16 to 0.20 mm by examiner 2. ICCs were equal or greater than 0.98, indicating perfect intraexaminer agreement. For interexaminer reliability, the largest mean of difference was 0.27 mm in measuring alveolar bone height between two examiners. ICCs were greater than 0.98, showing perfect interexaminer agreement. CONCLUSIONS: The voxel-based superimposition of pre- and post-surgical CBCT images with Dolphin Imaging is an effective and reliable way for linear measurements so as to assess the surgical outcome. There is minimal effect on reproducibility of measured data by different timepoints or performers.
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".