Aims

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.

The February 2015 Knee Roundup. 360 . looks at: Intra-operative sensors for knee balance; Mobile bearing no advantage; Death and knee replacement: a falling phenomenon; The swings and roundabouts of unicompartmental arthroplasty; Regulation, implants and innovation; The weight of arthroplasty responsibility!; BMI in arthroplasty

The aim of this prospective multicentre study was to report the patient satisfaction after total knee replacement (TKR), undertaken with the aid of intra-operative sensors, and to compare these results with previous studies. A total of 135 patients undergoing TKR were included in the study. The soft-tissue balance of each TKR was quantified intra-operatively by the sensor, and 18 (13%) were found to be unbalanced. A total of 113 patients (96.7%) in the balanced group and 15 (82.1%) in the unbalanced group were satisfied or very satisfied one year post-operatively (p = 0.043). . A review of the literature identified no previous study with a mean level of satisfaction that was greater than the reported level of satisfaction of the balanced TKR group in this study. Ensuring soft-tissue balance by using intra-operative sensors during TKR may improve satisfaction. . Cite this article: Bone Joint J 2014;96-B:1333–8