Equivalence assessment of weight bearing cone beam CT and multidetector CT through 3D Knee bone modelling
Bibliographic record
Abstract
Weight-bearing cone beam computed tomography (WB-CBCT, or simply WBCT), which captures high-resolution 3D images in a natural standing position, has gained increasing interest in recent years. This study examines the potential of WBCT as an alternative to multidetector computed tomography (MDCT) for 3D bone modelling. We generated 3D knee joint models from manually annotated WBCT and MDCT scans, performed rigid registration of these models, and assessed their similarity by evaluating the mean difference, standard deviation, and confidence intervals of the aligned models. The mean differences were computed as the average surface distances between corresponding WBCT and MDCT 3D bone models after rigid registration, providing a quantitative measure of their geometric similarity. Validation was conducted using both patient and cadaver scans to assess WBCT's clinical applicability under realistic conditions and its technical reliability with controlled samples. Our findings reveal an average absolute difference of less than 0.35 mm for patient scans and 0.30 mm for cadaveric scans between WBCT and MDCT. The patella demonstrated the smallest mean difference (-0.20 mm to 0.10 mm) and standard deviation (0.28 mm to 0.55 mm) across all scans. These results confirm the comparability of WBCT to MDCT for 3D bone modelling, highlighting WBCT's capacity to deliver appropriate image quality for the clinical assessment of bone joints.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".