Automatic diagnosis of knee osteoarthritis severity using Swin transformer
This work addresses early detection and severity assessment of knee osteoarthritis for clinical intervention, but it is incremental as it builds on existing transformer methods.
The paper tackled automated diagnosis of knee osteoarthritis severity using a Swin Transformer model, achieving accurate prediction as demonstrated in experiments with radiographic datasets.
Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. Early detection and diagnosis are crucial for successful clinical intervention and management to prevent severe complications, such as loss of mobility. In this paper, we propose an automated approach that employs the Swin Transformer to predict the severity of KOA. Our model uses publicly available radiographic datasets with Kellgren and Lawrence scores to enable early detection and severity assessment. To improve the accuracy of our model, we employ a multi-prediction head architecture that utilizes multi-layer perceptron classifiers. Additionally, we introduce a novel training approach that reduces the data drift between multiple datasets to ensure the generalization ability of the model. The results of our experiments demonstrate the effectiveness and feasibility of our approach in predicting KOA severity accurately.