Abstract：
Malposition of a nasogastric tube (NGT) can lead to severe complications. We aimed to develop a computer-aided detection (CAD) system to localize NGTs and detect NGT malposition on portable chest X-rays (CXRs). A total of 7378 portable CXRs were retrospectively retrieved from two hospitals between 2015 and 2020. All CXRs were annotated with pixel-level labels for NGT localization and image-level labels for NGT presence and malposition. In the CAD system, DeepLabv3 + with backbone ResNeSt50 and DenseNet121 served as the model architecture for segmentation and classification models, respectively. The CAD system was tested on images from chronologically different datasets (National Taiwan University Hospital (National Taiwan University Hospital)-20), geographically different datasets (National Taiwan University Hospital-Yunlin Branch (YB)), and the public CLiP dataset. For the segmentation model, the Dice coefficients indicated accurate delineation of the NGT course (National Taiwan University Hospital-20: 0.665, 95% confidence interval (CI) 0.630-0.696; National Taiwan University Hospital-Yunlin Branch: 0.646, 95% CI 0.614-0.678). The distance between the predicted and ground-truth NGT tips suggested accurate tip localization (National Taiwan University Hospital-20: 1.64 cm, 95% CI 0.99-2.41; National Taiwan University Hospital-Yunlin Branch: 2.83 cm, 95% CI 1.94-3.76). For the classification model, NGT presence was detected with high accuracy (area under the receiver operating characteristic curve (AUC): National Taiwan University Hospital-20: 0.998, 95% CI 0.995-1.000; National Taiwan University Hospital-Yunlin Branch: 0.998, 95% CI 0.995-1.000; CLiP dataset: 0.991, 95% CI 0.990-0.992). The CAD system also detected NGT malposition with high accuracy (AUC: National Taiwan University Hospital-20: 0.964, 95% CI 0.917-1.000; National Taiwan University Hospital-Yunlin Branch: 0.991, 95% CI 0.970-1.000) and detected abnormal nasoenteric tube positions with favorable performance (AUC: 0.839, 95% CI 0.807-0.869). The CAD system accurately localized NGTs and detected NGT malposition, demonstrating excellent potential for external generalizability.
摘要：
鼻胃管（NGT）的错位可导致严重的并发症。我们旨在开发一种计算机辅助检测（CAD）系统，以定位NGT并检测便携式胸部X射线（CXR）上的NGT错位。2015年至2020年期间，从两家医院回顾性检索了7378台便携式CXR。所有CXR都用NGT定位的像素级标签和NGT存在和错位的图像级标签注释。在CAD系统中,DeepLabv3+具有骨干ResNeSt50和DenseNet121作为分割和分类模型的模型架构，分别。CAD系统在时间顺序不同的数据集（国立台湾大学医院（国立台湾大学医院）-20）的图像上进行了测试，地理上不同的数据集(国立台湾大学医院云林分院(YB))，和公共CLiP数据集。对于分割模型，Dice系数表明NGT课程的准确划分（国立台湾大学医院-20：0.665，95％置信区间（CI）0.630-0.696；国立台湾大学医院-云林分院：0.646，95％CI0.614-0.678）。预测和地面实况NGT尖端之间的距离表明了精确的尖端定位（国立台湾大学医院-20：1.64厘米，95%CI0.99-2.41;台大医院云林分院:2.83cm,95%CI1.94-3.76）。对于分类模型，以高精度检测NGT的存在（受试者工作特征曲线下面积（AUC）：国立台湾大学医院-20：0.998，95％CI0.995-1.000；国立台湾大学医院-云林分院：0.998，95％CI0.995-1.000；CLiP数据集：0.991，95％CI0.990-0.992）。CAD系统还以高精度检测了NGT错位（AUC：国立台湾大学医院-20：0.964，95％CI0.917-1.000；国立台湾大学医院-云林分院：0.991，95％CI0.970-1.000），并检测出性能良好的异常鼻肠管位置（AUC：0.839，95％CI0.807-0.869）。CAD系统准确定位NGT并检测NGT错位，展示了优秀的外部泛化潜力。
