ADAPTATION DES METHODES DE RECONSTRUCTION 3D RAPIDES PAR STÉRÉORADIOGRAPHIE : MODÉLISATION DU MEMBRE INFÉRIEUR ET CALCUL DES INDICES CLINIQUES EN PRÉSENCE DE DÉFORMATION STRUCTURALE
Bibliographic record
Abstract
The anatomy of the lower limb and its 3D angular abnormalities are paramount in diagnosis, therapeutic applications and/or surgical planning. In this context, it is important to explore this knowledge in three dimensions. Within a collaborative project between the Laboratoire de Biomécanique (Paris) and the Laboratoire de recherche en Imagerie et Orthopédie (Montréal) 3D reconstruction methods have been developed based on the EOS™ imaging system (Biospace Med, Paris). These methods allow clinical analysis in standing position with low radiation dose. However, despite their accuracy, these reconstruction techniques are time-consuming and therefore difficult to use on a routine basis in clinical environment. The aim of this Ph.D. thesis is to adapt and optimize these methods for lower limb's bony structures with structural distortions. We developed and evaluated a reconstruction method of the lower limb based on parametric models of femurs and tibias and statistical inferences. This method provides a preliminary so-called “simplified” model of the lower limb in approximately 1 minute, allowing an accurate and reproducible computation of most currently used clinical parameters. Starting from the “simplified” model, we calculated an accurate customized 3D reconstruction of the lower limb in approximately 5 minutes. Based on this 3D model, we developed and validated several calculation modes for a larger panel of clinical parameters (including torsions and rotations) in order to identify the most robust and pertinent ones that could be standardized for clinical practice. The current work opens practical perspectives for the clinical transfer of such methods to be used in practice. One of the most clinical popular applications of the 3D reconstruction techniques and clinical parameters calculation is the surgical planning of lower limb osteotomy and arthroplasty.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".