3D reconstruction of the proximal femur with low-dose digital stereoradiography
Bibliographic record
Abstract
OBJECTIVE: Accurate three-dimensional (3D) geometry of the proximal femur may be helpful for fracture risk evaluation, as well as for planning and assisting surgical procedures. The purpose of this study was to apply and validate a stereoradiographic 3D reconstruction method on the proximal femur from radiographic contours identified on bi-planar radiographs. MATERIALS AND METHODS: Twenty-five excised non-pathologic proximal femurs were investigated using a low-dose digital radiographic device. Three-dimensional personalized models were reconstructed using the Non-Stereo Corresponding Contours (NSCC) algorithm. Three-dimensional CT-scan reconstructions were defined as geometric references for the comparison protocol, in order to assess the accuracy and reproducibility of the personalized 3D stereoradiographic reconstructions. In addition, the reliability of a set of 3D parameters obtained from stereoradiographic models was evaluated. RESULTS: This study demonstrated the validity of the NSCC method when applied to the proximal femur, with good results for accuracy (mean error = 0.7 mm) and reproducibility (Wilcoxon test: p > 0.28). Moreover, a precision study for the set of 3D parameters yielded a coefficient of variation lower than 5%. CONCLUSIONS: Once this approach has been validated in vivo, it should find multiple applications in therapeutic fields (e.g., for surgical planning, computer assisted surgery, etc.), as well as in diagnostic contexts (e.g., equilibrium studies or osteoporosis fracture risk assessment).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".