Aims

Accurate identification of the ankle joint centre is critical for estimating tibial coronal alignment in total knee arthroplasty (TKA). The purpose of the current study was to leverage artificial intelligence (AI) to determine the accuracy and effect of using different radiological anatomical landmarks to quantify mechanical alignment in relation to a traditionally defined radiological ankle centre.

The kinematic alignment (KA) approach to total knee arthroplasty (TKA) has recently increased in popularity. Accordingly, a number of derivatives have arisen and have caused confusion. Clarification is therefore needed for a better understanding of KA-TKA. Calipered (or true, pure) KA is performed by cutting the bone parallel to the articular surface, compensating for cartilage wear. In soft-tissue respecting KA, the tibial cutting surface is decided parallel to the femoral cutting surface (or trial component) with in-line traction. These approaches are categorized as unrestricted KA because there is no consideration of leg alignment or component orientation. Restricted KA is an approach where the periarthritic joint surface is replicated within a safe range, due to concerns about extreme alignments that have been considered ‘alignment outliers’ in the neutral mechanical alignment approach. More recently, functional alignment and inverse kinematic alignment have been advocated, where bone cuts are made following intraoperative planning, using intraoperative measurements acquired with computer assistance to fulfill good coordination of soft-tissue balance and alignment. The KA-TKA approach aims to restore the patients’ own harmony of three knee elements (morphology, soft-tissue balance, and alignment) and eventually the patients’ own kinematics. The respective approaches start from different points corresponding to one of the elements, yet each aim for the same goal, although the existing implants and techniques have not yet perfectly fulfilled that goal.

Limb alignment in total knee arthroplasty (TKA) influences periarticular soft-tissue tension, biomechanics through knee flexion, and implant survival. Despite this, there is no uniform consensus on the optimal alignment technique for TKA. Neutral mechanical alignment facilitates knee flexion and symmetrical component wear but forces the limb into an unnatural position that alters native knee kinematics through the arc of knee flexion. Kinematic alignment aims to restore native limb alignment, but the safe ranges with this technique remain uncertain and the effects of this alignment technique on component survivorship remain unknown. Anatomical alignment aims to restore predisease limb alignment and knee geometry, but existing studies using this technique are based on cadaveric specimens or clinical trials with limited follow-up times. Functional alignment aims to restore the native plane and obliquity of the joint by manipulating implant positioning while limiting soft tissue releases, but the results of high-quality studies with long-term outcomes are still awaited. The drawbacks of existing studies on alignment include the use of surgical techniques with limited accuracy and reproducibility of achieving the planned alignment, poor correlation of intraoperative data to long-term functional outcomes and implant survivorship, and a paucity of studies on the safe ranges of limb alignment. Further studies on alignment in TKA should use surgical adjuncts (e.g. robotic technology) to help execute the planned alignment with improved accuracy, include intraoperative assessments of knee biomechanics and periarticular soft-tissue tension, and correlate alignment to long-term functional outcomes and survivorship.