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. Patients with full-limb radiographs from the Osteoarthritis Initiative were included. A sub-cohort of 250 radiographs were annotated for landmarks relevant to knee alignment and used to train a deep learning (U-Net) workflow for angle calculation on the entire database. The radiological ankle centre was defined as the midpoint of the superior talus edge/tibial plafond. Knee alignment (hip-knee-ankle angle) was compared against 1) midpoint of the most prominent malleoli points, 2) midpoint of the soft-tissue overlying malleoli, and 3) midpoint of the soft-tissue sulcus above the malleoli.Aims
Methods
We performed A total of 12 cadaveric lower limbs were tested with a commercial
image-free navigation system using trackers secured by bone screws.
We then tested a non-invasive fabric-strap system. The lower limb
was secured at 10° intervals from 0° to 60° of knee flexion and
100 N of force was applied perpendicular to the tibia. Acceptable
coefficient of repeatability (CR) and limits of agreement (LOA)
of 3 mm were set based on diagnostic criteria for anterior cruciate
ligament (ACL) insufficiency.Objectives
Methods
