Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.
Department of Signal Processing, Tampere University of Technology, Tampere, Finland.
Sci Rep. 2018 Jan 29;8(1):1727. doi: 10.1038/s41598-018-20132-7.
Knee osteoarthritis (OA) is the most common musculoskeletal disorder. OA diagnosis is currently conducted by assessing symptoms and evaluating plain radiographs, but this process suffers from subjectivity. In this study, we present a new transparent computer-aided diagnosis method based on the Deep Siamese Convolutional Neural Network to automatically score knee OA severity according to the Kellgren-Lawrence grading scale. We trained our method using the data solely from the Multicenter Osteoarthritis Study and validated it on randomly selected 3,000 subjects (5,960 knees) from Osteoarthritis Initiative dataset. Our method yielded a quadratic Kappa coefficient of 0.83 and average multiclass accuracy of 66.71% compared to the annotations given by a committee of clinical experts. Here, we also report a radiological OA diagnosis area under the ROC curve of 0.93. Besides this, we present attention maps highlighting the radiological features affecting the network decision. Such information makes the decision process transparent for the practitioner, which builds better trust toward automatic methods. We believe that our model is useful for clinical decision making and for OA research; therefore, we openly release our training codes and the data set created in this study.
膝关节骨关节炎（OA）是最常见的肌肉骨骼疾病。OA 的诊断目前是通过评估症状和评估普通 X 光片来进行的，但这个过程存在主观性。在这项研究中，我们提出了一种新的基于深度暹罗卷积神经网络的透明计算机辅助诊断方法，根据 Kellgren-Lawrence 分级标准自动对膝关节 OA 的严重程度进行评分。我们仅使用来自多中心骨关节炎研究的数据来训练我们的方法，并在 Osteoarthritis Initiative 数据集上对随机选择的 3000 名受试者（5960 个膝关节）进行验证。与临床专家委员会给出的注释相比，我们的方法得到了二次 Kappa 系数为 0.83 和平均多类准确率为 66.71%。此外，我们还报告了 ROC 曲线下的放射学 OA 诊断面积为 0.93。除此之外，我们还展示了重点突出影响网络决策的放射学特征的注意力图。这些信息使医生能够了解决策过程，从而对自动方法建立更好的信任。我们相信我们的模型对临床决策和 OA 研究是有用的；因此，我们公开发布我们在这项研究中创建的培训代码和数据集。
