PDF(Pubmed)
Abstract：
Biological fingerprints extracted from clinical images can be used for patient identity verification to determine misfiled clinical images in picture archiving and communication systems. However, such methods have not been incorporated into clinical use, and their performance can degrade with variability in the clinical images. Deep learning can be used to improve the performance of these methods. A novel method is proposed to automatically identify individuals among examined patients using posteroanterior (PA) and anteroposterior (AP) chest X-ray images. The proposed method uses deep metric learning based on a deep convolutional neural network (DCNN) to overcome the extreme classification requirements for patient validation and identification. It was trained on the NIH chest X-ray dataset (ChestX-ray8) in three steps: preprocessing, DCNN feature extraction with an EfficientNetV2-S backbone, and classification with deep metric learning. The proposed method was evaluated using two public datasets and two clinical chest X-ray image datasets containing data from patients undergoing screening and hospital care. A 1280-dimensional feature extractor pretrained for 300 epochs performed the best with an area under the receiver operating characteristic curve of 0.9894, an equal error rate of 0.0269, and a top-1 accuracy of 0.839 on the PadChest dataset containing both PA and AP view positions. The findings of this study provide considerable insights into the development of automated patient identification to reduce the possibility of medical malpractice due to human errors.
摘要：
从临床图像中提取的生物指纹可用于患者身份验证，以确定图片存档和通信系统中的错误归档的临床图像。然而,这些方法尚未纳入临床使用，并且它们的性能会随着临床图像的变化而降低。深度学习可以用来提高这些方法的性能。提出了一种新颖的方法，可以使用后前（PA）和前后（AP）胸部X射线图像在被检查的患者中自动识别个体。所提出的方法使用基于深度卷积神经网络(DCNN)的深度度量学习来克服患者验证和识别的极端分类要求。它在NIH胸部X射线数据集（ChestX-ray8）上进行了三个步骤的训练：预处理，具有EfficientNetV2-S骨干的DCNN特征提取，和深度度量学习的分类。所提出的方法是使用两个公共数据集和两个临床胸部X射线图像数据集进行评估，这些数据集包含来自接受筛查和医院护理的患者的数据。在包含PA和AP视图位置的PadChest数据集上，预训练了300个时期的1280维特征提取器在接收器工作特性曲线下的面积为0.9894，误差率为0.0269，并且前1精度为0.839时表现最佳。这项研究的结果为自动患者识别的发展提供了相当多的见解，以减少由于人为错误而导致医疗事故的可能性。
