Osteoarthritis

Osteoarthritis (OA) is a prevalent degenerative joint disease,particularly affecting the knees. Early and accurate detection of OA and its severity, often graded using the Kellgren-Lawrence (KL) scale, is crucial for timely intervention and management. This study explores the application of deep learning techniques to automatically detect OA and assign KL grades from knee X-ray images. We propose a novel deep learning architecture that effectively extracts relevant features from X-ray images and classifies them into different KL grades. Our model demonstrates promising results in terms of accuracy and sensitivity, potentially aiding radiologists in making faster and more accurate diagnoses.

骨关节炎（OA）是一种普遍的退行性关节疾病，尤其影响膝关节。早期且准确的骨关节炎及其严重程度的检测，通常采用Kellgren-Lawrence（KL）分级法，对于及时干预与管理至关重要。本研究探讨了深度学习技术在自动检测骨关节炎并从膝关节X射线图像中分配KL分级中的应用。我们提出了一种新颖的深度学习架构，能够有效提取X射线图像中的相关特征，并将它们分类为不同的KL分级。我们的模型在准确性和敏感性方面展现出有希望的成果，有望协助放射科医生做出更快、更准确的诊断。