VALIDATING INTERNAL DENSITY CALIBRATION IN THE PROXIMAL HUMERUS TO ESTIMATE BONE STIFFNESS FOR STEMLESS SHOULDER ARTHROPLASTY
Notice bibliographique
Résumé
INTRODUCTION Stemless humeral head components have emerged as a popular choice for patients undergoing shoulder arthroplasty for end-stage OA since they preserve non-diseased bone for future surgical revisions. Current pre-operative clinical measures are limited in assessing volumetric bone mineral density (vBMD) and mechanical properties in the region of bone directly supporting the component. Gold-standard phantom calibration, used to determine vBMD in CT images, is seldom utilized in clinical practice requiring alternative density measures for accurate vBMD. Internal density calibration using internal tissues as references has yet to be validated in the proximal humerus, and vBMD values have yet to be linked to finite element model (FEM) estimated stiffness in the context of stemless shoulder arthroplasty. OBJECTIVE 1) To determine the correlation between vBMD and finite element model (FEM) estimated stiffness 2) To determine the bias in internal density-based vBMD using three different referent tissue combinations compared to phantom-based vBMD, in the proximal humerus. METHODS Non-pathologic cadaveric single-energy CT images (n = 25), containing a K 2 HPO 4 phantom, were used to analyze a 10 mm region directly below the anatomic neck. Phantom-based vBMD was calculated for each region and used as input to image-based FEMs (ROD). Internal calibration used air (A), adipose (A), skeletal muscle (M), and cortical bone (C) to generate calibrated images from three different referent tissue combinations (AACM, ACM, AAC). Images were used to generate FEMs for each tissue combination. Results were compared between vBMD (mg K 2 HPO 4 /cc) and apparent modulus (E app ) for each internal calibration tissue combination to the phantom calibration using linear regression. Bland-Altman analysis was used to determine the agreement between tissue combination and phantom calibration for estimated stiffness values (E app ). RESULTS Linear regression (Figure 1) showed strong correlations between estimated stiffness and vBMD values for each calibration method (AACM R 2 = 0.7524; ACM R 2 = 0.7723; AAC R 2 = 0.7384; ROD R 2 = 0.7854) and slopes not significantly different from 1 (p < 0.001). Bland-Altman analysis (Figure 2) revealed the ACM tissue combination had the lowest error bounds in apparent modulus, compared to phantom-vBMD derived FEMs, with a mean bias of 80.15 MPa and 95% limits of agreement ranging from -164.55 to 324.86 MPa. CONCLUSION The results of this study support the use of internal density calibration as a valid method for using internal density calibrated images as input to FEMs for estimating stiffness in the proximal humerus. The ACM tissue combination provided the highest agreement with the gold standard phantom calibration. This internal density calibration method may provide a solution for determining vBMD in patients undergoing shoulder arthroplasty for end-stage OA where phantoms are not present in the CT image. By linking bone density measures with estimated stiffness values, the mechanical properties of bone in the region supporting the humeral component are considered, which has the potential to improve preoperative planning for stemless shoulder arthroplasty. Next steps are to apply the ACM internal calibration method and estimated stiffness values in retrospective CT images from patients who have undergone shoulder arthroplasty for end-stage OA (n = 88) to link surgical outcomes to stiffness measures.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».