The identification of patients with knee osteoarthritis (OA) likely to progress rapidly in terms of structure is critical to facilitate the development of disease-modifying drugs. Using 9280 knee magnetic resonance (MR) images (3268 patients) from the Osteoarthritis Initiative (OAI) database , we implemented a deep learning method to predict, from MR images and clinical variables including body mass index (BMI), further cartilage degradation measured by joint space narrowing at 12 months. Using COR IW TSE images, our classification model achieved a ROC AUC score of 65%. On a similar task, trained radiologists obtained a ROC AUC score of 58.7% highlighting the difficulty of the classification task. Additional analyses conducted in parallel to predict pain grade evaluated by the WOMAC pain index achieved a ROC AUC score of 72%. Attention maps provided evidence for distinct specific areas as being relevant in those two predictive models, including the medial joint space for JSN progression and the intra-articular space for pain prediction. This feasibility study demonstrates the interest of deep learning applied to OA, with a potential to support even trained radiologists in the challenging task of identifying patients with a high-risk of disease progression.

中文翻译：

---

一种从 MRI 预测膝关节骨关节炎放射学进展的深度学习方法

识别可能在结构方面进展迅速的膝关节骨关节炎 (OA) 患者对于促进疾病缓解药物的开发至关重要。使用来自骨关节炎倡议 (OAI) 数据库的 9280 幅膝关节磁共振 (MR) 图像（3268 名患者），我们实施了一种深度学习方法，以根据 MR 图像和包括体重指数 (BMI) 在内的临床变量来预测进一步的软骨退化： 12 个月时关节间隙变窄。使用 COR IW TSE 图像，我们的分类模型实现了 65% 的 ROC AUC 分数。在类似的任务中，训练有素的放射科医生获得了 58.7% 的 ROC AUC 分数，这突出了分类任务的难度。为预测由 WOMAC 疼痛指数评估的疼痛等级而并行进行的其他分析实现了 72% 的 ROC AUC 评分。注意图提供了与这两个预测模型相关的不同特定区域的证据，包括用于 JSN 进展的内侧关节空间和用于预测疼痛的关节内空间。这项可行性研究证明了将深度学习应用于 OA 的兴趣，甚至有可能支持训练有素的放射科医生完成识别疾病进展高风险患者的挑战性任务。