Prediction of post-operative implanted knee function using machine learning in clinical big data

Total knee arthroplasty (TKA) is one of the common knee surgeries. Because there are some types of TKA implant, it is hard to select appropriate type of TKA implant for individual patient. For the sake of pre-operative planning, this study presents a novel approach, which predicts post-operative implanted knee function of individuals. It is based on a clinical big data analysis. The big data is composed by a set of pre-operative knee mobility function and post-operative knee function. The method constructs a post-operative knee function prediction model by means of a machine learning approach. It extracts features using principal component analysis, and constructs a mapping function from pre-operative feature space to post-operative feature space. The method was validated by applying to prediction of post-operative anterior-posterior translation in 52 TKA operated knees. Leave-one-out cross validation test revealed the prediction performances with a mean correlation coefficients of 0.79 and a mean root-mean-squared-error of 3.44 mm.