The principles of evidence-based medicine (EBM) are the foundation of modern medical practice. Surgeons are familiar with the commonly used statistical techniques to test hypotheses, summarize findings, and provide answers within a specified range of probability. Based on this knowledge, they are able to critically evaluate research before deciding whether or not to adopt the findings into practice. Recently, there has been an increased use of artificial intelligence (AI) to analyze information and derive findings in orthopaedic research. These techniques use a set of statistical tools that are increasingly complex and may be unfamiliar to the orthopaedic surgeon. It is unclear if this shift towards less familiar techniques is widely accepted in the orthopaedic community. This study aimed to provide an exploration of understanding and acceptance of AI use in research among orthopaedic surgeons. Semi-structured in-depth interviews were carried out on a sample of 12 orthopaedic surgeons. Inductive thematic analysis was used to identify key themes.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
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:
Surgeons need to be able to measure angles and distances in three dimensions in the planning and assessment of knee replacement. Computed tomography (CT) offers the accuracy needed but involves greater radiation exposure to patients than traditional long-leg standing radiographs, which give very little information outside the plane of the image. There is considerable variation in CT radiation doses between research centres, scanning protocols and individual scanners, and ethics committees are rightly demanding more consistency in this area. By refining the CT scanning protocol we have reduced the effective radiation dose received by the patient down to the equivalent of one long-leg standing radiograph. Because of this, it will be more acceptable to obtain the three-dimensional data set produced by CT scanning. Surgeons will be able to document the impact of implant position on outcome with greater precision.
