论文标题
使用人工智能在便携式仰卧胸部X光片上开发自动气管管和Carina检测
Development of Automatic Endotracheal Tube and Carina Detection on Portable Supine Chest Radiographs using Artificial Intelligence
论文作者
论文摘要
便携式仰卧胸部X光片的图像质量由于对比度低和高噪声而固有地很差。气管插管检测需要气管导管（ETT）尖端和Carina的位置。目的是在胸部射线照相中找到ETT尖端和Carina之间的距离。为了克服此类问题，我们提出了使用Mask R-CNN的特征提取方法。蒙版R-CNN预测图像中的管子和气管分叉。然后，使用特征提取方法来找到ETT尖端和Carina的特征点。因此，可以获得ETT-Carina距离。在我们的实验中，我们的结果在召回和精度方面可能超过96 \％。此外，对象错误小于$ 4.7751 \ pm 5.3420 $毫米，ETT-Carina距离错误小于$ 5.5432 \ pm 6.3100 $ mm。外部验证表明，所提出的方法是高舒适性系统。根据Pearson相关系数，我们在董事会认证的强化主义者与ETT-Carine距离方面具有很强的相关性。
The image quality of portable supine chest radiographs is inherently poor due to low contrast and high noise. The endotracheal intubation detection requires the locations of the endotracheal tube (ETT) tip and carina. The goal is to find the distance between the ETT tip and the carina in chest radiography. To overcome such a problem, we propose a feature extraction method with Mask R-CNN. The Mask R-CNN predicts a tube and a tracheal bifurcation in an image. Then, the feature extraction method is used to find the feature point of the ETT tip and that of the carina. Therefore, the ETT-carina distance can be obtained. In our experiments, our results can exceed 96\% in terms of recall and precision. Moreover, the object error is less than $4.7751\pm 5.3420$ mm, and the ETT-carina distance errors are less than $5.5432\pm 6.3100$ mm. The external validation shows that the proposed method is a high-robustness system. According to the Pearson correlation coefficient, we have a strong correlation between the board-certified intensivists and our result in terms of ETT-carina distance.
