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
Hyaline articular cartilage has been known to
be a troublesome tissue to repair once damaged. Since the introduction
of autologous chondrocyte implantation (ACI) in 1994, a renewed
interest in the field of cartilage repair with new repair techniques
and the hope for products that are regenerative have blossomed.
This article reviews the basic science structure and function of
articular cartilage, and techniques that are presently available
to effect repair and their expected outcomes.
