Predicting Knee Osteoarthritis using Deep Neural Network
Gauri Kitukale, Nitin Arvind Shelke, Rohit Agrawal, Navneet Pratap Singh, Sidharth Quamara
Abstract
Knee osteoarthritis (KOA) is a degenerative knee disease that affects the independence, quality of life, and motivation of millions of people. Early detection and accurate prediction of KOA can facilitate timely intervention and improve patient outcomes. In this paper, we propose a novel approach to predicting the onset and progression of knee OA using deep neural networks (DNNs). We use advanced neural network architectures to pull out complex patterns and relationships from the data. This lets the model learn and predict how knee OA will progress. The proposed models are tested and validated on historic data such as X-ray and MRI images. The results demonstrate the effectiveness of our proposed DNN-based approach in accurately predicting knee OA outcomes, surpassing traditional methods.