Background

Early and accurate detection of ankle fractures are crucial for optimizing treatment and thus reducing future complications. Radiographs are the most abundant imaging techniques for assessing fractures. Deep learning (DL) methods, through adequately trained deep convolutional neural networks (DCNNs), have been previously shown to faster and accurately analyze radiographic images without human intervention. Herein, we aimed to assess the performance of two different DCNNs in detecting ankle fractures using radiographs compared to the ground truth.

Methods

In this retrospective case-control study, our DCNNs were trained using radiographs obtained from 1050 patients with ankle fracture and the same number of individuals with otherwise healthy ankles. Inception V3 and Renet-50 pretrained models were used in our algorithms. Danis-Weber classification method was used. Out of 1050, 72 individuals were labeled as occult fractures as they were not detected in the primary radiographic assessment. Single-view (anteroposterior) radiographs was compared with 3-views (anteroposterior, mortise, lateral) for training the DCNNs.

Results

Our DCNNs showed a better performance using 3-views images versus single-view based on greater values for accuracy, F-score, and area under the curve (AUC). The highest sensitivity was 98.7 % and specificity was 98.6 % in detection of ankle fractures using 3-views using inception V3. This model missed only one fracture on radiographs.

Conclusion

The performance of our DCNNs showed that it can be used for developing the currently used image interpretation programs or as a separate assistant solution for the clinicians to detect ankle fractures faster and more precisely.

Level of evidence

III

中文翻译：

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使用深度学习算法检测脚踝骨折

背景

踝关节骨折的早期和准确检测对于优化治疗和减少未来并发症至关重要。X 光片是评估骨折最丰富的成像技术。深度学习 (DL) 方法，通过经过充分训练的深度卷积神经网络 (DCNN)，先前已被证明可以在无需人工干预的情况下更快、更准确地分析射线照相图像。在此，我们的目的是评估两种不同的 DCNN 在使用 X 光片检测踝关节骨折方面与地面实况相比的性能。

方法

在这项回顾性病例对照研究中，我们的 DCNN 使用从 1050 名踝关节骨折患者和相同数量的其他方面健康的脚踝患者获得的 X 光片进行训练。我们的算法中使用了 Inception V3 和 Renet-50 预训练模型。使用 Danis-Weber 分类方法。在 1050 人中，有 72 人被标记为隐匿性骨折，因为他们在初级放射影像学评估中未被发现。将单视图（前后）射线照片与 3 视图（前后、榫眼、侧面）进行比较，以训练 DCNN。

结果

我们的 DCNN 使用 3 视图图像比单视图显示更好的性能，基于更高的准确性、F 分数和曲线下面积 (AUC) 值。使用 inception V3 的 3 视图检测踝关节骨折的最高灵敏度为 98.7%，特异性为 98.6%。该模型在射线照片上仅遗漏了一处骨折。

结论

我们的 DCNN 的性能表明，它可用于开发当前使用的图像判读程序，或作为临床医生更快、更准确地检测踝关节骨折的单独辅助解决方案。

证据等级

三