Soft tissue structures resisting anterior instability in a computational glenohumeral joint model

Development of a more biofidelic musculoskeletal model with humeral head translation and glenohumeral ligaments

Computational musculoskeletal modeling is useful for understanding upper extremity biomechanics, especially when in vivo tests are unfeasible. A musculoskeletal model of the upper limb with increased biofidelity was developed by including humeral head translation (HHT) and ligaments. The model was validated and ligament contribution and effect of shoulder (thoracohumeral) elevation on HHT was evaluated. Humerus translated superiorly with increased elevation, with translations closely matching (avg. difference 2.83 mm) previous in vitro studies. HHT and ligament inclusion in the model will improve biomechanical predictions of upper extremity movements and study of conditions, like subacromial impingement, rotator cuff tear, or shoulder instability.

Neural Network Optimization of Ligament Stiffnesses for the Enhanced Predictive Ability of a Patient-Specific, Computational Foot/Ankle Model

Computational models of diarthrodial joints serve to inform the biomechanical function of these structures, and as such, must be supplied appropriate inputs for performance that is representative of actual joint function. Inputs for these models are sourced from both imaging modalities as well as literature. The latter is often the source of mechanical properties for soft tissues, like ligament stiffnesses; however, such data are not always available for all the soft tissues nor is it known for patient-specific work. In the current research, a method to improve the ligament stiffness definition for a computational foot/ankle model was sought with the greater goal of improving the predictive ability of the computational model. Specifically, the stiffness values were optimized using artificial neural networks (ANNs); both feedforward and radial basis function networks (RBFNs) were considered. Optimal networks of each type were determined and subsequently used to predict stiffnesses for the foot/ankle model. Ultimately, the predicted stiffnesses were considered reasonable and resulted in enhanced performance of the computational model, suggesting that artificial neural networks can be used to optimize stiffness inputs.

Differences in glenohumeral joint morphology between patients with anterior shoulder instability and healthy, uninjured volunteers

BACKGROUND

Traumatic glenohumeral joint (GHJ) dislocations are common, resulting in significant shoulder disability and pain. Previous research indicates that bony morphology is associated with an increased risk of injury in other joints (eg, the knee), but the extent to which bony morphology is associated with traumatic GHJ dislocation is unknown. This study assessed GHJ morphology in patients with anterior GHJ instability and in a control population of healthy volunteers.

METHODS

Bilateral computed tomography scans were used to measure GHJ morphology in both shoulders of 11 patients with instability and 11 control subjects. Specific outcome measures included the glenoid radius of curvature (ROC) in the anterior/posterior (A/P) and superior/inferior (S/I) directions, humeral head ROC, A/P and S/I conformity index, and A/P and S/I stability angle.

RESULTS

Compared with the control subjects, the glenoid of the instability the injured shoulder in patients with instability was flatter (ie, higher ROC) in the A/P (P = .001) and S/I (P = .01) directions and this finding was also true for uninjured, contralateral shoulder (A/P: P = .01, S/I: P = .03). No differences in GHJ morphology were detected between the instability patients' injured and contralateral shoulders (P > .07). Similarly, no differences in GHJ morphology were detected between the control subjects' dominant and nondominant shoulders (P > .51).

CONCLUSIONS

There are significant differences in GHJ morphology between healthy control subjects and both shoulders (injured and uninjured, contralateral) of patients diagnosed with anterior instability after GHJ dislocation. These findings are important clinically because they suggest that glenoid morphology may influence the risk of GHJ dislocation.