The Normal 3D Gleno-humeral Relationship and Anatomy of the Glenoid Planes

Deep learning algorithms enable MRI-based scapular morphology analysis with values comparable to CT-based assessments

Scapular morphological attributes show promise as prognostic indicators of retear following rotator cuff repair. Current evaluation techniques using single-slice magnetic-resonance imaging (MRI) are, however, prone to error, while more accurate computed tomography (CT)-based three-dimensional techniques, are limited by cost and radiation exposure. In this study we propose deep learning-based methods that enable automatic scapular morphological analysis from diagnostic MRI despite the anisotropic resolution and reduced field of view, compared to CT. A deep learning-based segmentation network was trained with paired CT derived scapula segmentations. An algorithm to fuse multi-plane segmentations was developed to generated high-resolution 3D models of the scapula on which morphological landmark- and axes were predicted using a second deep learning network for morphological analysis. Using the proposed methods, the critical shoulder angle, glenoid inclination and version were measured from MRI with accuracies of -1.3 ± 1.7 degrees, 1.3 ± 2.1 degree, and - 1.4 ± 3.4 degrees respectively, compared to CT. Inter-class correlation between MRI and CT derived metrics were substantial for the glenoid version and almost perfect for the other metrics. This study demonstrates how deep learning can overcome reduced resolution, bone border contrast and field of view, to enable 3D scapular morphology analysis on MRI.

Identification of threshold pathoanatomic metrics in primary glenohumeral osteoarthritis

BACKGROUND

An assessment of the pathoanatomic parameters of the arthritic glenohumeral joint (GHJ) has the potential to identify discriminating metrics to differentiate glenoid types in shoulders with primary glenohumeral osteoarthritis (PGHOA). The aim was to identify the morphometric differences and threshold values between glenoid types including normal and arthritic glenoids with the various types in the Walch classification. We hypothesized that there would be clear morphometric discriminators between the various glenoid types and that specific numeric threshold values would allow identification of each glenoid type.

METHODS

The computed tomography scans of 707 shoulders were analyzed: 585 obtained from shoulders with PGHOA and 122 from shoulders without glenohumeral pathology. Glenoid morphology was classified according to the Walch classification. All computed tomography scans were imported in a dedicated automatic 3D-software program that referenced measurements to the scapular body plane. Glenoid and humeral modeling was performed using the best-fit sphere method, and the root-mean-square error was calculated. The direction and orientation of the glenoid and humerus described glenohumeral relationships.

RESULTS

Among shoulders with PGHOA, 90% of the glenoids and 85% of the humeral heads were directed posteriorly in reference to the scapular body plane. Several discriminatory pathoanatomic parameters were identified: GHJ narrowing < 3 mm was a discriminatory metric for type A glenoids. Posterior humeral subluxation > 70% discriminated type B1 from normal GHJs. The root-mean-square error was a discriminatory metric to distinguish type B2 from type A, type B3, and normal GHJs. Type B3 glenoids differed from type A2 by greater retroversion (>13°) and subluxation (>71%). The type C glenoid retroversion inferior limit was 21°, whereas normal glenoids never presented with retroversion > 16°.

CONCLUSION

Pathoanatomic metrics with the identified threshold values can be used to discriminate glenoid types in shoulders with PGHOA.

Age-related differences in humerothoracic, scapulothoracic, and glenohumeral kinematics during elevation and rotation motions

Age affects gross shoulder range of motion (ROM), but biomechanical changes over a lifetime are typically only characterized for the humerothoracic joint. Suitable age-related baselines for the scapulothoracic and glenohumeral contributions to humerothoracic motion are needed to advance understanding of shoulder injuries and pathology. Notably, biomechanical comparisons between younger or older populations may obscure detected differences in underlying shoulder motion. Herein, biplane fluoroscopy and skin-marker motion analysis quantified humerothoracic, scapulothoracic, and glenohumeral motion during 3 static poses (resting neutral, internal rotation to L4-L5, and internal rotation to maximum reach) and 2 dynamic activities (scapular plane abduction and external rotation in adduction). Orientations during static poses and rotations during active ROM were compared between subjects <35 years and >45 years of age (N=10 subjects per group). Numerous age-related kinematic differences were measured, ranging 5–25°, where variations in scapular orientation and motion were consistently observed. These disparities are on par with or exceed mean clinically important differences and standard error of measurement of clinical ROM, which indicates that high resolution techniques and appropriately matched controls are required to avoid confounding results of studies that investigate shoulder kinematics. Understanding these dissimilarities will help clinicians manage expectations and treatment protocols where indications and prevalence between age groups tend to differ. Where possible, it is advised to select age-matched control cohorts when studying the kinematics of shoulder injury, pathology, or surgical/physical therapy interventions to ensure clinically important differences are not overlooked.

Reliable interpretation of scapular kinematics depends on coordinate system definition

BACKGROUND

Interpretation of shoulder motion across studies has been complicated due to the use of numerous scapular coordinate systems in the literature. Currently, there are no simple means by which to compare scapular kinematics between coordinate system definitions when data from only one coordinate system is known.

RESEARCH QUESTION

How do scapular kinematics vary based on the choice of coordinate system and can average rotation matrices be used to accurately convert kinematics between scapular local coordinate systems?

METHODS

Average rotation matrices derived from anatomic landmarks of 51 cadaver scapulae (29 M/22 F; 59 ± 13 yrs; 26R/25 L; 171 ± 11 cm; 70 ± 19 kg; 23.7 ± 5.5 kg/m²) were generated between three common scapular coordinate systems. Absolute angle of rotation was used to determine if anatomical variability within the cadaver population influenced the matrices. To quantify the predictive capability to convert kinematics between the three coordinate systems, the average rotation matrices were applied to scapulothoracic motion data collected from 19 human subjects (10 M/9 F; 43 ± 17 yrs; 19R; 173 ± 9 cm; 71 ± 16 kg; 23.6 ± 4.5 kg/m²) using biplane fluoroscopy. Root mean squared error (RMSE) was used to compare kinematics from an original coordinate system to the kinematics expressed in each alternative coordinate system.

RESULTS

The choice of scapular coordinate system resulted in mean differences in scapulothoracic rotation of up to 23°, with overall different shapes and/or magnitudes of the curves. A single average rotation matrix between any two coordinate systems achieved accurate conversion of scapulothoracic kinematics to within 4° of RMSE of the known solution. The average rotation matrices were independent of sex, side, decomposition sequence, and motion.

SIGNIFICANCE

Scapulothoracic kinematic representations vary in shape and magnitude based solely on the choice of local coordinate system. The results of this study enhance interpretability and reproducibility in expressing scapulothoracic motion data between laboratories by providing a simple means to convert data between common coordinate systems. This is necessitated by the variety of available motion analysis techniques and their respective scapular landmark definitions.