Background

MRI is frequently used for early diagnosis of axial spondyloarthritis (axSpA). However, evaluation is time-consuming and requires profound expertise because noninflammatory degenerative changes can mimic axSpA, and early signs may therefore be missed. Deep neural networks could function as assistance for axSpA detection.

Purpose

To create a deep neural network to detect MRI changes in sacroiliac joints indicative of axSpA.

Materials and Methods

This retrospective multicenter study included MRI examinations of five cohorts of patients with clinical suspicion of axSpA collected at university and community hospitals between January 2006 and September 2020. Data from four cohorts were used as the training set, and data from one cohort as the external test set. Each MRI examination in the training and test sets was scored by six and seven raters, respectively, for inflammatory changes (bone marrow edema, enthesitis) and structural changes (erosions, sclerosis). A deep learning tool to detect changes indicative of axSpA was developed. First, a neural network to homogenize the images, then a classification network were trained. Performance was evaluated with use of area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. P < .05 was considered indicative of statistically significant difference.

Results

Overall, 593 patients (mean age, 37 years ± 11 [SD]; 302 women) were studied. Inflammatory and structural changes were found in 197 of 477 patients (41%) and 244 of 477 (51%), respectively, in the training set and 25 of 116 patients (22%) and 26 of 116 (22%) in the test set. The AUCs were 0.94 (95% CI: 0.84, 0.97) for all inflammatory changes, 0.88 (95% CI: 0.80, 0.95) for inflammatory changes fulfilling the Assessment of SpondyloArthritis international Society definition, and 0.89 (95% CI: 0.81, 0.96) for structural changes indicative of axSpA. Sensitivity and specificity on the external test set were 22 of 25 patients (88%) and 65 of 91 patients (71%), respectively, for inflammatory changes and 22 of 26 patients (85%) and 70 of 90 patients (78%) for structural changes.

Conclusion

Deep neural networks can detect inflammatory or structural changes to the sacroiliac joint indicative of axial spondyloarthritis at MRI.

© RSNA, 2022

Online supplemental material is available for this article.

中文翻译：

---

深度学习检测骶髂关节 MRI 中轴性脊柱关节炎的变化指标

背景

MRI 常用于中轴型脊柱关节炎 (axSpA) 的早期诊断。然而，评估非常耗时并且需要深厚的专业知识，因为非炎症性退行性变化可以模仿 axSpA，因此可能会错过早期征兆。深度神经网络可以辅助 axSpA 检测。

目的

创建一个深度神经网络来检测骶髂关节中指示 axSpA 的 MRI 变化。

材料和方法

这项回顾性多中心研究包括 2006 年 1 月至 2020 年 9 月期间在大学和社区医院收集的五个临床疑似 axSpA 患者队列的 MRI 检查。四个队列的数据用作训练集，一个队列的数据用作外部测试放。训练集和测试集中的每项 MRI 检查分别由 6 名和 7 名评分者针对炎症变化（骨髓水肿、附着点炎）和结构变化（糜烂、硬化）进行评分。开发了一种深度学习工具来检测指示 axSpA 的变化。首先，神经网络对图像进行均质化，然后训练分类网络。使用接受者操作特征曲线 (AUC) 下的面积、灵敏度和特异性评估性能。P< .05 被认为是统计显着差异的指标。

结果

总体而言，研究了 593 名患者（平均年龄，37 岁 ± 11 [SD]；302 名女性）。在训练组中分别发现 477 名患者中的 197 名 (41%) 和 477 名患者中的 244 名 (51%) 以及测试中的 116 名患者中的 25 名 (22%) 和 116 名患者中的 26 名 (22%) 出现炎症和结构变化放。所有炎症变化的 AUC 为 0.94（95% CI：0.84，0.97），满足国际脊柱关节炎评估协会定义的炎症变化为 0.88（95% CI：0.80，0.95），以及 0.89（95% CI：0.81，0.96） ) 指示 axSpA 的结构变化。对于炎症变化，外部测试集的敏感性和特异性分别为 25 名患者中的 22 名 (88%) 和 91 名患者中的 65 名 (71%)，以及 26 名患者中的 22 名 (85%) 和 90 名患者中的 70 名 (78%)为结构变化。

结论

深度神经网络可以检测到骶髂关节的炎症或结构变化，在 MRI 上指示中轴脊柱关节炎。

©北美放射学会，2022

本文提供了在线补充材料。