A biomechanical digital twin of Legg–Calvé–Perthes disease deformity
Bibliographic record
Abstract
PURPOSE: The dynamic stress environment in the hip joint is thought to contribute to pain and osteoarthritis (OA) in people with Legg-Calvé-Perthes disease (LCPD) deformity, but is poorly understood, limiting clinical management options. The objective of this study was to develop and evaluate a patient-specific biomechanical "digital twin" model of LCPD to predict chondrolabral shear stress in dynamic and static loading scenarios. METHODS: We produced a digital twin model of both hips in a patient with unilateral LCPD deformity using anatomical magnetic resonance imaging (MRI) and the ArtiSynth modeling platform. We evaluated the model's sensitivity to changes in material properties and joint angles during a typical gait cycle, and verified its stress and femoral translation predictions against upright open MRI of the hip in high-flexion postures. RESULTS: The model's prediction of the highest chondrolabral shear stress during a gait cycle was 22-93% greater in the LCPD hip than in the unaffected hip. The model was sensitive to changes in material parameters and joint angles, but could accurately reproduce femoral translation and expected stress distribution in extreme static postures. CONCLUSION: This study demonstrates the importance of both dynamic motion and morphology in the stress environment of highly aspherical hip joints. Although some challenges remain, digital twin models are a promising tool to study the long-term outcomes of LCPD, and could be applied in future to aid clinical management.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".