Preoperative Prediction of Prosthetic Size in Total Knee Arthroplasty Based on Multimodal Data and Deep Learning

Abstract

Total knee arthroplasty (TKA) is an effective treatment method for severe knee osteoarthritis and other knee-related diseases. Accurate match of prostheses is a crucial factor to improve the clinical efficacy and patients’ postoperative satisfaction in TKA, to which no enough attention is paid currently. In this paper, we introduce deep learning algorithm to analyze the patients’ multimodal data, such as preoperative radiograph of knees and relevant physical features (e.g. sex, height, weight), and design a software system for preoperative prediction of prosthetic type in TKA. The main processing steps include the pre-processing of X-ray images and the prediction of prosthetic type based on convolutional neural network. Research on loss function and model structure is implemented to fit the dataset better for further improvement of prediction accuracy. Transfer learning method is employed to address the problem of inadequate data. The experimental results shows that our prediction system can achieve the same accuracy level compared with that of traditional methods manipulated by experienced doctors, while it can complete the preoperative prediction automatically with lower cost and better stability.