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
Methods
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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The aim of this study was to review the current evidence surrounding curve type and morphology on curve progression risk in adolescent idiopathic scoliosis (AIS). A comprehensive search was conducted by two independent reviewers on PubMed, Embase, Medline, and Web of Science to obtain all published information on morphological predictors of AIS progression. Search items included ‘adolescent idiopathic scoliosis’, ‘progression’, and ‘imaging’. The inclusion and exclusion criteria were carefully defined. Risk of bias of studies was assessed with the Quality in Prognostic Studies tool, and level of evidence for each predictor was rated with the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach. In all, 6,286 publications were identified with 3,598 being subjected to secondary scrutiny. Ultimately, 26 publications (25 datasets) were included in this review.Aims
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Transforming growth factor-beta2 (TGF-β2) is recognized as a versatile cytokine that plays a vital role in regulation of joint development, homeostasis, and diseases, but its role as a biological mechanism is understood far less than that of its counterpart, TGF-β1. Cartilage as a load-resisting structure in vertebrates however displays a fragile performance when any tissue disturbance occurs, due to its lack of blood vessels, nerves, and lymphatics. Recent reports have indicated that TGF-β2 is involved in the physiological processes of chondrocytes such as proliferation, differentiation, migration, and apoptosis, and the pathological progress of cartilage such as osteoarthritis (OA) and rheumatoid arthritis (RA). TGF-β2 also shows its potent capacity in the repair of cartilage defects by recruiting autologous mesenchymal stem cells and promoting secretion of other growth factor clusters. In addition, some pioneering studies have already considered it as a potential target in the treatment of OA and RA. This article aims to summarize the current progress of TGF-β2 in cartilage development and diseases, which might provide new cues for remodelling of cartilage defect and intervention of cartilage diseases.
