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.

翻译：膝关节骨关节炎（KOA）是一种常见疾病，可导致膝关节慢性疼痛和僵硬。早期检测与诊断对于成功实施临床干预和管理、预防活动能力丧失等严重并发症至关重要。本文提出一种采用Swin Transformer自动预测KOA严重程度的自动化方法。我们的模型利用公开的放射学数据集（基于Kellgren-Lawrence评分）实现早期检测与严重程度评估。为提升模型精度，我们采用基于多层感知器分类器的多预测头架构。此外，我们引入了一种新型训练方法，该方法能有效减少多数据集间的数据漂移，确保模型的泛化能力。实验结果表明，我们的方法在准确预测KOA严重程度方面具有有效性和可行性。