Diagnosing knee joint osteoarthritis (KOA), a major cause of disability worldwide, is challenging due to subtle radiographic indicators and the varied progression of the disease. Using deep learning for KOA diagnosis requires broad, comprehensive datasets. However, obtaining these datasets poses significant challenges due to patient privacy concerns and data collection restrictions. Additive data augmentation, which enhances data variability, emerges as a promising solution. Yet, it's unclear which augmentation techniques are most effective for KOA. This study explored various data augmentation methods, including adversarial augmentations, and their impact on KOA classification model performance. While some techniques improved performance, others commonly used underperformed. We identified potential confounding regions within the images using adversarial augmentation. This was evidenced by our models' ability to classify KL0 and KL4 grades accurately, with the knee joint omitted. This observation suggested a model bias, which might leverage unrelated features for classification currently present in radiographs. Interestingly, removing the knee joint also led to an unexpected improvement in KL1 classification accuracy. To better visualize these paradoxical effects, we employed Grad-CAM, highlighting the associated regions. Our study underscores the need for careful technique selection for improved model performance and identifying and managing potential confounding regions in radiographic KOA deep learning.

翻译：诊断膝关节骨关节炎（KOA）这一全球主要致残原因，面临因放射学指征细微及疾病进展多样化的挑战。利用深度学习进行KOA诊断需要广泛且全面的数据集。然而，由于患者隐私顾虑和数据采集限制，获取此类数据集存在显著障碍。通过增强数据多样性的加法数据增强技术应运而生，成为有前景的解决方案。但目前尚不清楚哪些增强技术对KOA最有效。本研究探索了多种数据增强方法（包括对抗性增强）及其对KOA分类模型性能的影响。虽然部分技术提升了性能，但一些常用方法表现不佳。通过对抗性增强，我们识别出图像中潜在混杂区域。证据在于：即便在剔除膝关节后，模型仍能准确分类KL0和KL4等级。这一观察表明模型存在偏差，可能利用了X射线中当前存在的非相关特征进行分类。值得注意的是，剔除膝关节还意外提升了KL1分类准确率。为更直观地可视化这些反常效应，我们采用Grad-CAM突出显示相关区域。本研究强调：需谨慎选择增强技术以提升模型性能，并识别和管理放射学KOA深度学习中的潜在混杂区域。