Machine learning (ML) holds significant promise in optimizing various aspects of total shoulder arthroplasty (TSA), potentially improving patient outcomes and enhancing surgical decision-making. The aim of this systematic review was to identify ML algorithms and evaluate their effectiveness, including those for predicting clinical outcomes and those used in image analysis. We searched the PubMed, EMBASE, and Cochrane Central Register of Controlled Trials databases for studies applying ML algorithms in TSA. The analysis focused on dataset characteristics, relevant subspecialties, specific ML algorithms used, and their performance outcomes.Aims
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Scapular notching is thought to have an adverse effect on the outcome of reverse total shoulder arthroplasty (RTSA). However, the matter is still controversial. The aim of this study was to determine the clinical impact of scapular notching on outcomes after RTSA. Three electronic databases (PubMed, Cochrane Database, and EMBASE) were searched for studies which evaluated the influence of scapular notching on clinical outcome after RTSA. The quality of each study was assessed. Functional outcome scores (the Constant-Murley scores (CMS), and the American Shoulder and Elbow Surgeons (ASES) scores), and postoperative range of movement (forward flexion (FF), abduction, and external rotation (ER)) were extracted and subjected to meta-analysis. Effect sizes were expressed as weighted mean differences (WMD).Aims
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