Fast 3D reconstruction of the lower limb using a parametric model and statistical inferences and clinical measurements calculation from biplanar X-rays
Bibliographic record
Abstract
In clinical routine, lower limb analysis relies on conventional X-ray (2D view) or computerised tomography (CT) Scan (lying position). However, these methods do not allow 3D analysis in standing position. The aim of this study is to propose a fast and accurate 3D-reconstruction-method based on parametric models and statistical inferences from biplanar X-rays with clinical measurements' (CM) assessment in standing position for a clinical routine use. For the reproducibility study, the 95% CI was under 2.7° for all lower limbs' angular measurements except for tibial torsion, femoral torsion and tibiofemoral rotation ( < 5°). The 95% CI were under 2.5 mm for lower limbs' lengths and 1.5 to 3° for the pelvis' CM. Comparisons between X-rays and CT-scan based 3D shapes in vitro showed mean differences of 1.0 mm (95% CI = 2.4 mm). Comparisons of 2D lower limbs' and 3D pelvis' CM between standing 'Shifted-Feet' and 'Non-Shifted-Feet' position showed means differences of 0.0 to 1.4°. Significant differences were found only for pelvic obliquity and rotation. The reconstruction time was about 5 min.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".