Virtual reconstruction of glenoid bone defects using a statistical shape model.

Abstract

Description of the native shape of a glenoid helps surgeons to preoperatively plan the position of a shoulder implant. A statistical shape model (SSM) can be used to virtually reconstruct a glenoid bone defect and to predict the inclination, version, and center position of the native glenoid. An SSM-based reconstruction method has already been developed for acetabular bone reconstruction. The goal of this study was to evaluate the SSM-based method for the reconstruction of glenoid bone defects and the prediction of native anatomic parameters. First, an SSM was created on the basis of 66 healthy scapulae. Then, artificial bone defects were created in all scapulae and reconstructed using the SSM-based reconstruction method. For each bone defect, the reconstructed surface was compared with the original surface. Furthermore, the inclination, version, and glenoid center point of the reconstructed surface were compared with the original parameters of each scapula. For small glenoid bone defects, the healthy surface of the glenoid was reconstructed with a root mean square error of 1.2 ± 0.4 mm. Inclination, version, and glenoid center point were predicted with an accuracy of 2.4° ± 2.1°, 2.9° ± 2.2°, and 1.8 ± 0.8 mm, respectively. The SSM-based reconstruction method is able to accurately reconstruct the native glenoid surface and to predict the native anatomic parameters. Based on this outcome, statistical shape modeling can be considered a successful technique for use in the preoperative planning of shoulder arthroplasty.