论文标题
深度学习可以预测磁共振图像中的总膝盖替代
Deep learning predicts total knee replacement from magnetic resonance images
论文作者
论文摘要
膝盖骨关节炎（OA）是美国常见的肌肉骨骼疾病。当在早期诊断时，诸如运动和体重减轻之类的生活方式干预措施会减慢OA的进展，但在以后的阶段，只有一种侵入性选择：总膝盖置换（TKR）。尽管通常是一个成功的程序，但只有2/3的患者报告了膝盖的膝盖感觉“正常”，并且可能会出现需要修订的并发症。这需要一个模型来识别TKR风险较高的人群，尤其是在OA的高级阶段，因此可以实施适当的治疗方法，从而缓慢OA的进展和延迟TKR。在这里，我们提出了一条深度学习的管道，该管道利用MRI图像以及临床和人口统计学信息以AUC $ 0.834 \ pm 0.036 $（P <0.05）预测TKR。最值得注意的是，该管道可预测无OA患者的AUC $ 0.943 \ pm 0.057 $（p <0.05）。此外，我们在测试数据中开发了病例对照对的遮挡图，并比较两者中模型使用的区域，从而识别TKR成像生物标志物。因此，这项工作迈向了具有临床实用性的管道，生物标志物进一步确定了我们对OA进展和最终TKR发作的理解。
Knee Osteoarthritis (OA) is a common musculoskeletal disorder in the United States. When diagnosed at early stages, lifestyle interventions such as exercise and weight loss can slow OA progression, but at later stages, only an invasive option is available: total knee replacement (TKR). Though a generally successful procedure, only 2/3 of patients who undergo the procedure report their knees feeling ''normal'' post-operation, and complications can arise that require revision. This necessitates a model to identify a population at higher risk of TKR, particularly at less advanced stages of OA, such that appropriate treatments can be implemented that slow OA progression and delay TKR. Here, we present a deep learning pipeline that leverages MRI images and clinical and demographic information to predict TKR with AUC $0.834 \pm 0.036$ (p < 0.05). Most notably, the pipeline predicts TKR with AUC $0.943 \pm 0.057$ (p < 0.05) for patients without OA. Furthermore, we develop occlusion maps for case-control pairs in test data and compare regions used by the model in both, thereby identifying TKR imaging biomarkers. As such, this work takes strides towards a pipeline with clinical utility, and the biomarkers identified further our understanding of OA progression and eventual TKR onset.
