Safety concerns surrounding osseointegration are a significant barrier to replacing socket prosthesis as the standard of care following limb amputation. While implanted osseointegrated prostheses traditionally occur in two stages, a one-stage approach has emerged. Currently, there is no existing comparison of the outcomes of these different approaches. To address safety concerns, this study sought to determine whether a one-stage osseointegration procedure is associated with fewer adverse events than the two-staged approach. A comprehensive electronic search and quantitative data analysis from eligible studies were performed. Inclusion criteria were adults with a limb amputation managed with a one- or two-stage osseointegration procedure with follow-up reporting of complications.Aims
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The use of technology to assess balance and alignment during total knee surgery can provide an overload of numerical data to the surgeon. Meanwhile, this quantification holds the potential to clarify and guide the surgeon through the surgical decision process when selecting the appropriate bone recut or soft tissue adjustment when balancing a total knee. Therefore, this paper evaluates the potential of deploying supervised machine learning (ML) models to select a surgical correction based on patient-specific intra-operative assessments. Based on a clinical series of 479 primary total knees and 1,305 associated surgical decisions, various ML models were developed. These models identified the indicated surgical decision based on available, intra-operative alignment, and tibiofemoral load data.Aims
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