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
The use of robots in orthopaedic surgery is an
emerging field that is gaining momentum. It has the potential for significant
improvements in surgical planning, accuracy of component implantation
and patient safety. Advocates of robot-assisted systems describe
better patient outcomes through improved pre-operative planning
and enhanced execution of surgery. However, costs, limited availability,
a lack of evidence regarding the efficiency and safety of such systems
and an absence of long-term high-impact studies have restricted
the widespread implementation of these systems. We have reviewed
the literature on the efficacy, safety and current understanding of
the use of robotics in orthopaedics. Cite this article:
