Purpose

To create an algorithm able to accurately detect IVC filters on radiographs without human assistance, capable of being used to screen radiographs to identify patients needing IVC filter retrieval.

Methods

A primary dataset of 5225 images, 30% of which included IVC filters, was assembled and annotated. 85% of the data was used to train a Cascade R-CNN (Region Based Convolutional Neural Network) object detection network incorporating a pre-trained ResNet-50 backbone. The remaining 15% of the data, independently annotated by three radiologists, was used as a test set to assess performance. The algorithm was also assessed on an independently constructed 1424-image dataset, drawn from a different institution than the primary dataset.

Results

On the primary test set, the algorithm achieved a sensitivity of 96.2% (95% CI 92.7–98.1%) and a specificity of 98.9% (95% CI 97.4–99.5%). Results were similar on the external test set: sensitivity 97.9% (95% CI 96.2–98.9%), specificity 99.6 (95% CI 98.9–99.9%).

Conclusion

Fully automated detection of IVC filters on radiographs with high sensitivity and excellent specificity required for an automated screening system can be achieved using object detection neural networks. Further work will develop a system for identifying patients for IVC filter retrieval based on this algorithm.

Graphical abstract

中文翻译：

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使用深度卷积神经网络自动检测射线照片上的 IVC 滤波器

目的

创建一种算法，能够在没有人工协助的情况下准确检测射线照片上的 IVC 过滤器，能够用于筛选射线照片以识别需要 IVC 过滤器检索的患者。

方法

组装并注释了包含 5225 张图像的主要数据集，其中 30% 包括 IVC 过滤器。85% 的数据用于训练包含预训练 ResNet-50 主干的级联 R-CNN（基于区域的卷积神经网络）对象检测网络。其余 15% 的数据由三位放射科医生独立注释，用作评估性能的测试集。该算法还在一个独立构建的 1424 图像数据集上进行了评估，该数据集来自与主要数据集不同的机构。

结果

在主要测试集上，该算法实现了 96.2%（95% CI 92.7–98.1%）的灵敏度和 98.9%（95% CI 97.4–99.5%）的特异性。外部测试集的结果相似：灵敏度 97.9%（95% CI 96.2–98.9%），特异性 99.6（95% CI 98.9–99.9%）。

结论

使用对象检测神经网络可以实现自动筛查系统所需的高灵敏度和出色特异性的 X 光片上 IVC 过滤器的全自动检测。进一步的工作将开发一个系统，用于根据该算法识别患者以进行 IVC 过滤器检索。

图形概要