Knee Osteoarthritis (KOA), a leading cause of disability worldwide, is challenging to detect early due to subtle radiographic indicators. Diverse, extensive datasets are needed but are challenging to compile because of privacy, data collection limitations, and the progressive nature of KOA. However, a model capable of projecting genuine radiographs into different OA stages could augment data pools, enhance algorithm training, and offer pre-emptive prognostic insights. In this study, we trained a CycleGAN model to synthesize past and future stages of KOA on any genuine radiograph. The model was validated using a Convolutional Neural Network that was deceived into misclassifying disease stages in transformed images, demonstrating the CycleGAN's ability to effectively transform disease characteristics forward or backward in time. The model was particularly effective in synthesizing future disease states and showed an exceptional ability to retroactively transition late-stage radiographs to earlier stages by eliminating osteophytes and expanding knee joint space, signature characteristics of None or Doubtful KOA. The model's results signify a promising potential for enhancing diagnostic models, data augmentation, and educational and prognostic usage in healthcare. Nevertheless, further refinement, validation, and a broader evaluation process encompassing both CNN-based assessments and expert medical feedback are emphasized for future research and development.

翻译：膝关节骨关节炎是导致全球残疾的主要原因之一，由于放射学指标细微，早期检测颇具挑战。虽然需要多样且广泛的数据集，但由于隐私保护、数据采集限制以及KOA渐进性特征，这类数据集的构建十分困难。然而，能够将真实X光片投射至不同骨关节炎阶段的模型，既可扩充数据池、增强算法训练，又能提供前瞻性预后见解。本研究训练了一个CycleGAN模型，可在任意真实X光片上合成KOA的过去与未来阶段。通过卷积神经网络验证模型效果——该网络在转换后的图像中被诱导错误分类疾病阶段，证明CycleGAN具备有效向前或向后转化疾病特征的能力。该模型在合成未来疾病状态方面尤为有效，并能通过消除骨赘和扩大膝关节间隙（无或可疑KOA的特征标志），将晚期X光片逆向转化为早期阶段。模型成果预示着在增强诊断模型、数据增强及医疗领域的教育与预后应用方面具有显著潜力。但未来研究仍需进一步优化、验证，并开展包含CNN评估与医学专家反馈的综合性评价流程。