Stiffness is a common complication after total knee arthroplasty (TKA). Pathogenesis is not understood, treatment options are limited, and diagnosis is challenging. The aim of this study was to investigate if MRI can be used to visualize intra-articular scarring in patients with stiff, painful knee arthroplasties. Well-functioning primary TKAs (n = 11), failed non-fibrotic TKAs (n = 5), and patients with a clinical diagnosis of fibrosisAims
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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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