Post-operative Implanted Knee Kinematics Prediction in Total Knee Arthroscopy Using Clinical Big Data

Abstract

Total knee arthroscopy (TKA) is a very effective surgery for damaged knee joint treatment. Because, there are some TKA operation methods and TKA implant products, it is difficult to decide an appropriate one at the pre-operative planning. This study introduces a novel approach to assist surgeon for the pre-operative planning, and proposes a prediction method of post-operative knee joint kinematics. The method is based on principal component analysis (PCA) for characteristics extraction, and machine learning algorithms. The proposed method was validated by leave-one-out cross validation test in 46 osteoarthritis (OA) knee patients. The results show that the proposed method can predict the post-operative knee joint kinematics from the pre-operative one with a mean correlation coefficient of 0.69, and a root-mean-squared-error (RMSE) of 1.8 mm.