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
Treatment for osteoarthritis (OA) has traditionally
focused on joint replacement for end-stage disease. An increasing number
of surgical and pharmaceutical strategies for disease prevention
have now been proposed. However, these require the ability to identify
OA at a stage when it is potentially reversible, and detect small
changes in cartilage structure and function to enable treatment
efficacy to be evaluated within an acceptable timeframe. This has
not been possible using conventional imaging techniques but recent
advances in musculoskeletal imaging have been significant. In this
review we discuss the role of different imaging modalities in the
diagnosis of the earliest changes of OA. The increasing number of
MRI sequences that are able to non-invasively detect biochemical
changes in cartilage that precede structural damage may offer a
great advance in the diagnosis and treatment of this debilitating
condition. Cite this article:
