Critical shoulder angle (CSA), lateral acromial angle (LAA), and acromion index (AI) are common radiologic parameters used to distinguish between patients with rotator cuff tears (RCT) and those with an intact rotator cuff. This study aims to assess the predictive power of these parameters in degenerative RCT. This retrospective study included data from 92 patients who were divided into two groups: the RCT group, which included 47 patients with degenerative full-thickness supraspinatus tendon tears, and a control group of 45 subjects without tears. CSA, AI, and LAA measurements from standardized true anteroposterior radiographs were independently derived and analyzed by two orthopedic surgeons. Receiver operating characteristic (ROC) analyses were performed to determine the cutoff values. No significant differences were found between patients in the RCT and control groups in age (p = 0.079), gender (p = 0.804), or injury side (p = 0.552). Excellent inter-observer reliability was seen for CSA, LAA, and AI values. Mean CSA (38.1°) and AI (0.72) values were significantly larger in the RCT group than in the control group (34.56° and 0.67°, respectively, p < 0.001) with no significant difference between groups for LAA (RCT, 77.99° vs. control, 79.82°; p = 0.056). ROC analysis yielded an area under the curve (AUC) of 0.815 for CSA with a cutoff value of 37.95°, and CSA was found to be the strongest predictor of the presence of a RCT, followed by AI with an AUC of 0.783 and a cutoff value of 0.705. We conclude that CSA and AI may be useful predictive factors for degenerative RCT in the Turkish population

During shoulder arthroplasty the native functionality of the diseased shoulder joint is restored, this functionality is strongly dependent upon the native anatomy of the pre-diseased shoulder joint. Therefore, surgeons often use the healthy contralateral scapula to plan the surgery, however in bilateral diseases such as osteoarthritis this is not always feasible. Virtual reconstructions are then used to reconstruct the pre-diseased anatomy and plan surgery or subject-specific implants. In this project, we develop and validate a statistical shape modeling method to reconstruct the pre-diseased anatomy of eroded scapulae with the aim to investigate the existence of predisposing anatomy for certain shoulder conditions. The training dataset for the statistical shape model consisted of 110 CT images from patients without observable scapulae pathologies as judged by an experienced shoulder surgeon. 3D scapulae models were constructed from the segmented images. An open-source non-rigid B-spline-based registration algorithm was used to obtain point-to-point correspondences between the models. The statistical shape model was then constructed from the dataset using principle component analysis. The cross-validation was performed similarly to the procedure described by Plessers et al. Virtual defects were created on each of the training set models, which closely resemble the morphology of glenoid defects according to the Wallace classification method. The statistical shape model was reconstructed using the leave-one-out method, so the corresponding training set model is no longer incorporated in the shape model. Scapula reconstruction was performed using a Monte Carlo Markov chain algorithm, random walk proposals included both shape and pose parameters, the closest fitting proposal was selected for the virtual reconstruction. Automatic 3D measurements were performed on both the training and reconstructed 3D models, including glenoid version, critical shoulder angle, glenoid offset and glenoid center position. The root-mean-square error between the measurements of the training data and reconstructed models was calculated for the different severities of glenoid defects. For the least severe defect, the mean error on the inclination, version and critical shoulder angle (°) was 2.22 (± 1.60 SD), 2.59 (± 1.86 SD) and 1.92 (± 1.44 SD) respectively. The reconstructed models predicted the native glenoid offset and centre position (mm) an accuracy of 0.87 (± 0.96 SD) and 0.88 (± 0.57 SD) respectively. The overall reconstruction error was 0.71 mm for the reconstructed part. For larger defects each error measurement increased significantly. A virtual reconstruction methodology was developed which can predict glenoid parameters with high accuracy. This tool will be used in the planning of shoulder surgeries and investigation of predisposing scapular morphologies

Introduction. Glenoid inclination, defined as the angle formed by the intersection of a line made of the most superior and inferior points of the glenoid and a line formed by the supraspinatus fossa, has been postulated to impact the mechanical advantage of the rotator cuff in shoulder abduction. An increase in glenoid inclination has previously been reported in patients with massive rotator cuff tears and multiple studies have correlated rotator cuff tears to an increase of the critical shoulder angle, an angle comprised of both the glenoid inclination and acromical index. Glenoid inclination is best measured by the B-angle as it has been shown to be both an accurate and reliable. The purpose of this study was to determine the correlation of glenoid inclination and the presence of degenerative rotator cuff tears. Methods. Data was prospectively collected for study patients assigned to one of two groups. The tear group consisted of patients with degenerative, atraumatic rotator cuff tears, confirmed by MRI and the control group consisted of healthy volunteers without shoulder pain. Inclusion criteria for both groups included age 45 or older. Exclusion criteria included history of previous shoulder surgery, previous patient-recalled injury to the shoulder, presence of glenoid weak, and previous humerus or glenoid fracture. Patients were also excluded from the control group if any shoulder pain or history of rotator cuff disease was present. All patients had standard anterior/posterior shoulder radiographs taken and glenoid inclination was digitally measured with Viztek OpalRad PACS software (Konica Minolta, Tokyo, Japan). The beta angle was measured to determine the glenoid inclincation. Statistical analysis was performed using SPSS version 23 (IBM, Aramonk, NY). Patient age and glenoid inclination were examined with the Shapiro-Wilk test of normality and then compared with student t tests. Gender distribution was compared with chi square test. A p-value of 0.05 was used to represent significance. Results. The study included 26 patients in the tear group and 23 patients in the control group. There was no difference in the age of the two groups (57 vs 54, p=0.292) or gender distribution (p=0.774). The average glenoid inclination was 11.18 (SD=2.67) degrees for the tear group and 5.97 (SD=2.55) degrees for the control group. This difference was statistically significant (p<0.001). Discussion. Glenoid inclination is significantly increased in patients with degenerative rotator cuff tears compared to healthy controls. Tendon overload secondary to increased glenoid inclination may be the primary anatomical factor contributing to the development of degenerative rotator cuff tears