Introduction.
Our aim was to determine the most repeatable three-dimensional measurement of glenoid orientation and to compare it between shoulders with intact and torn rotator cuffs. Our null hypothesis was that glenoid orientation in the scapulae of shoulders with a full-thickness tear of the rotator cuff was the same as that in shoulders with an intact rotator cuff. We studied 24 shoulders in cadavers, 12 with an intact rotator cuff and 12 with a full-thickness tear. Two different observers used a three-dimensional digitising system to measure glenoid orientation in the scapular plane (ie glenoid inclination) using six different techniques. Glenoid version was also measured. The overall precision of the measurements revealed an error of less than 0.6°. Intraobserver reliability (correlation coefficients of 0.990 and 0.984 for each observer) and interobserver reliability (correlation coefficient of 0.985) were highest for measurement of
Pre-operative 3D glenoid planning improves component placement in terms of version, inclination, offset and orientation. Version and inclination measurements require the position of the inferior angle. As a consequence, current planning tools require a 3D model of the full scapula to accurately determine the glenoid parameters. Statistical shape models (SSMs) can be used to reconstruct the missing anatomy of bones. Therefore, the objective of this study is to develop and validate an SSM for the reconstruction of the inferior scapula, hereby reducing the irradiation exposure for patients. The training dataset for the statistical shape 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. A statistical shape model was then constructed from the dataset using principal component analysis. Leave-one-out cross-validation was performed to evaluate the accuracy of the predicted glenoid parameters from virtual partial scans. Five types of virtual partial scans were created on each of the training set models, where an increasing amount of scapular body was removed to mimic a partial CT scan. 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. 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