Adequate deltoid and rotator cuff lengthening in total shoulder arthroplasty (TSA) is crucial to maximize the postoperative functional outcome and to avoid complications. Hence surgeons and patients could benefit from including muscle length information in preoperative planning software.Although different methods have been introduced to automatically indicate patient-specific muscle attachment and wrapping points, the definition of a fast and accurate workflow is still a challenge, due to the large variability in bone shapes. Therefore, the goal of this study is to develop and evaluate the accuracy of a novel method to automatically estimate muscle elongation, based on a statistical shape modelling (SSM) approach.Firstly, humerus and scapula SSMs were used to automatically indicate the attachment points of the main shoulder muscles: subscapularis, supraspinatus, infraspinatus, teres minor and deltoid. Secondly, a wrapping algorithm was applied to identify the points where muscles wrap around bones or potential implants. Finally, the accuracy of the automatically indicated landmarks and its effect on the muscle elongation were evaluated by comparing the manually indicated landmarks with the landmarks identified through the SSM for a set of 40 healthy shoulder CT-scans.The low errors on elongation values suggest that the presented automated workflow is a promising tool for allowing surgeons to evaluate patient-specific muscle elongations during preoperative planning. Although the evaluation was limited to healthy joints, this method allows to easily process large datasets and to potentially find a correlation between muscle elongations and postoperative outcome.
Eccentric Reaming for B2 Glenoids: History, Preoperative Planning, Surgical Technique, and Outcome
