How Accurate Is <scp>CBCT</scp> in Measuring Bone Density? A Comparative <scp>CBCT‐CT</scp> In Vitro Study
Bibliographic record
Abstract
PURPOSE: Recently, cone beam computed tomography (CBCT) has become widely used for oral and maxillofacial imaging. Twenty dry mandibles were CBCT and conventional multislice CT scanned to evaluate if there is a statistically significant difference between the bone density values they produce, defined as gray density values, and to determine any correlation between them. MATERIALS AND METHODS: Using software and a radiographic template, the CT and CBCT scan images were overlapped, and two data sets were created, each one giving the respective gray values (voxel value [VV] or Hounsfield unit [HU]) of the same area with the same spatial coordinates. For the statistical analysis, t-test, Pearson's correlation, and Pearson's r were used. RESULTS: The differences between the CBCT (VV) and CT (HU) gray density values were statistically significant (p ≤ .05), whereas the Pearson's correlation coefficients and Pearson's r-values demonstrated a statistically significant linear correlation between VV and HU gray density values. CONCLUSION: The lower radiation dose and reduced costs of CBCT make this a useful substitute for CT; however, this study has shown that, in order to more accurately define the bone density with CBCT, a conversion ratio needs to be applied to the VV.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".