The aim of this study was to report patient and clinical outcomes following robotic-assisted total knee arthroplasty (RA-TKA) at multiple institutions with a minimum two-year follow-up. This was a multicentre registry study from October 2016 to June 2021 that included 861 primary RA-TKA patients who completed at least one pre- and postoperative patient-reported outcome measure (PROM) questionnaire, including Forgotten Joint Score (FJS), Knee Injury and Osteoarthritis Outcomes Score for Joint Replacement (KOOS JR), and pain out of 100 points. The mean age was 67 years (35 to 86), 452 were male (53%), mean BMI was 31.5 kg/m2 (19 to 58), and 553 (64%) cemented and 308 (36%) cementless implants.Aims
Methods
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
