Determination of predisposing scapular anatomy with a statistical shape model-Part II: shoulder osteoarthritis

Covariations between scapular shape and bone density in B-glenoids: A statistical shape and density modeling-approach

B-type glenoids are characterized by posterior humeral head migration and/or bony-erosion-induced glenoid retroversion. Patients with this type of osteoarthritic glenoids are known to be at increased risk of glenoid component loosening after anatomic total shoulder arthroplasty (aTSA). One of the main challenges in B glenoid surgical planning is to find a balance between correcting the bony shape and maintaining the quality of the bone support. This study aims to systematically quantify variabilities in terms of scapular morphology and bone mineral density in patients with B glenoids and to identify patterns of covariation between these two features. Using computed tomography scan images of 62 patients, three-dimensional scapular surface models were constructed. Rigid and nonrigid surface registration of the scapular surfaces, followed by volumetric registration and material mapping, enabled us to develop statistical shape model (SSM) and statistical density model (SDM). Partial least square correlation (PLSC) was used to identify patterns of covariation. The developed SSM and SDM represented 85.9% and 56.6% of variabilities in terms of scapular morphology and bone density, respectively. PLSC identified four modes of covariation, explaining 66.0% of the correlation between these two variations. Covariation of posterior-inferior glenoid erosion with posterior sclerotic bone formation in association with reduction of bone density in the anterior and central part of the glenoid was detected as the primary mode of covariation. Identification of these asymmetrical distribution of bone density can inform us about possible reasons behind glenoid component loosening in B glenoids and surgical guidelines in terms of the compromise between bony shape correction and bone support quality.

Glenohumeral Osteoarthritis: A Biological Advantage or a Missed Diagnosis?

(1) Background: Osteoarthritis is a degenerative joint disease that is commonly diagnosed in the aging population. Interestingly, the lower extremity joints have a higher published incidence of osteoarthritis than the upper extremity joints. Although much is known about the disease process, it remains unclear why some joints are more affected than others. (2) Methods: A comprehensive literature review was conducted utilizing the search engines PubMed, Google Scholar, and Elsevier from 2014 to 2024, directing our search to osteoarthritis of various joints, with the focus being on glenohumeral osteoarthritis. (3) Results and Discussion: The literature review revealed a publication difference, which may be explained by the inconsistency in classification systems utilized in the diagnosis of shoulder osteoarthritis. For instance, there are six classification systems employed in the diagnosis of glenohumeral osteoarthritis, making the true incidence and, therefore, the prevalence unobtainable. Furthermore, susceptibility to osteoarthritis in various joints is complicated by factors such as joint anatomy, weight-bearing status, and prior injuries to the joint. (4) Conclusions: This review reveals the lack of understanding of shoulder osteoarthritis's true incidence and prevalence while considering the anatomy and biomechanics of the glenohumeral joint. In addition, this is the first paper to suggest a single criterion for the diagnosis of glenohumeral osteoarthritis.

Nonunion scaphoid bone shape prediction using iterative kernel principal polynomial shape analysis

BACKGROUND

The scaphoid is an important mechanical stabilizer for both the proximal and distal carpal columns. The precise estimation of the complete scaphoid bone based on partial bone geometric information is a crucial factor in the effective management of scaphoid nonunion. Statistical shape model (SSM) could be utilized to predict the complete scaphoid shape based on the defective scaphoid. However, traditional principal component analysis (PCA) based SSM is limited by its linearity and the inability to adjust the number of modes used for prediction.

PURPOSE

This study proposes an iterative kernel principal polynomial shape analysis (iKPPSA)-based SSM to predict the pre-morbid shape of the scaphoid, aiming at enhancing the accuracy as well as the robustness of the model.

METHODS

Sixty-five sets of scaphoid images were used to train SSM and nine sets of scaphoid images were used for validation. For each validation image set, three defect types (tubercle, proximal pole, and avascular necrosis) were virtually created. The predicted shapes of the scaphoid by PCA, PPSA, KPCA, and iKPPSA-based SSM were evaluated against the original shape in terms of mean error, Hausdorff distance error, and Dice coefficient.

RESULTS

The proposed iKPPSA-based scaphoid SSM demonstrates significant robustness, with a generality of 0.264 mm and a specificity of 0.260 mm. It accounts for 99% of variability with the first seven principal modes of variation. Compared to the traditional PCA-based model, the iKPPSA-based scaphoid model prediction demonstrated superior performance for the proximal pole type fracture, with significant reductions of 25.2%, 24.7%, and 24.6% in mean error, Hausdorff distance, and root mean square error (RMSE), respectively, and a 0.35% improvement in Dice coefficient.

CONCLUSION

This study showed that the iKPPSA-based SSM exploits the nonlinearity of data features and delivers high reconstruction accuracy. It can be effectively integrated into preoperative planning for scaphoid fracture management or morphology-based biomechanical modeling of the scaphoid.

A predictive model for the critical shoulder angle based on a three-dimensional analysis of scapular angular and linear morphometrics

BACKGROUND

The purpose of this study was to define the features of scapular morphology that are associated with changes in the critical shoulder angle (CSA) by developing the best predictive model for the CSA based on multiple potential explanatory variables, using a completely 3D assessment.

METHODS

3D meshes were created from CT DICOMs using InVesalius (Vers 3.1.1, RTI [Renato Archer Information Technology Centre], Brazil) and Meshmixer (3.4.35, Autodesk Inc., San Rafael, CA). The analysis included 17 potential angular, weighted linear and area measurements. The correlation of the explanatory variables with the CSA was investigated with the Pearson's correlation coefficient. Using multivariable linear regression, the approach for predictive model-building was leave-one-out cross-validation and best subset selection.

RESULTS

Fifty-three meshes were analysed. Glenoid inclination (GI) and coronal plane angulation of the acromion (CPAA) [Pearson's r: 0.535; -0.502] correlated best with CSA. The best model (adjusted R-squared value 0.67) for CSA prediction contained 10 explanatory variables including glenoid, scapular spine and acromial factors. CPAA and GI were the most important based on their distribution, estimate of coefficients and loss in predictive power if removed.

CONCLUSIONS

The relationship between scapular morphology and CSA is more complex than the concept of it being dictated solely by GI and acromial horizontal offset and includes glenoid, scapular spine and acromial factors of which CPAA and GI are most important. A further investigation in a closely defined cohort with rotator cuff tears is required before drawing any clinical conclusions about the role of surgical modification of scapular morphology.

LEVEL OF EVIDENCE

Level 4 retrospective observational cohort study with no comparison group.