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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Recurrent dislocation is both a cause and consequence of glenoid bone loss, and the extent of the bony defect is an indicator guiding operative intervention. Literature suggests that loss greater than 25% requires glenoid reconstruction. Measuring bone loss is controversial; studies use different methods to determine this, with no clear evidence of reproducibility. A systematic review was performed to identify existing CT-based methods of quantifying glenoid bone loss and establish their reliability and reproducibility A Preferred Reporting Items for Systematic reviews and Meta-Analyses-compliant systematic review of conventional and grey literature was performed.Aims
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Giant cell tumour of bone (GCTB) treatment changed since the introduction of denosumab from purely surgical towards a multidisciplinary approach, with recent concerns of higher recurrence rates after denosumab. We evaluated oncological, surgical, and functional outcomes for distal radius GCTB, with a critically appraised systematic literature review. We included 76 patients with distal radius GCTB in three sarcoma centres (1990 to 2019). Median follow-up was 8.8 years (2 to 23). Seven patients underwent curettage, 38 curettage with adjuvants, and 31 resection; 20 had denosumab.Aims
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