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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It is important to understand the rate of complications associated with the increasing burden of revision shoulder arthroplasty. Currently, this has not been well quantified. This review aims to address that deficiency with a focus on complication and reoperation rates, shoulder outcome scores, and comparison of anatomical and reverse prostheses when used in revision surgery. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) systematic review was performed to identify clinical data for patients undergoing revision shoulder arthroplasty. Data were extracted from the literature and pooled for analysis. Complication and reoperation rates were analyzed using a meta-analysis of proportion, and continuous variables underwent comparative subgroup analysis.Aims
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