Introduction

Although total knee arthroplasty (TKA) is generally considered successful, 16–30% of patients are dissatisfied. There are multiple reasons for this, but some of the most frequent reasons for revision are instability and joint stiffness. A possible explanation for this is that the implant alignment is not optimized to ensure joint stability in the individual patient. In this work, we used an artificial neural network (ANN) to learn the relation between a given standard cruciate-retaining (CR) implant position and model-predicted post-operative knee kinematics. The final aim was to find a patient-specific implant alignment that will result in the estimated post-operative knee kinematics closest to the native knee.