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
Methods
A clinical investigation into a new bone void filler is giving
first data on systemic and local exposure to the anti-infective
substance after implantation. A total of 20 patients with post-traumatic/post-operative bone
infections were enrolled in this open-label, prospective study.
After radical surgical debridement, the bone cavity was filled with
this material. The 21-day hospitalisation phase included determination
of gentamicin concentrations in plasma, urine and wound exudate, assessment
of wound healing, infection parameters, implant resorption, laboratory
parameters, and adverse event monitoring. The follow-up period was
six months. Objective
Method
