Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
Department of Emergency Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
Sci Rep. 2022 Dec 19;12(1):21884. doi: 10.1038/s41598-022-26486-3.
Acute thoracic aortic dissection is a life-threatening disease, in which blood leaking from the damaged inner layer of the aorta causes dissection between the intimal and adventitial layers. The diagnosis of this disease is challenging. Chest x-rays are usually performed for initial screening or diagnosis, but the diagnostic accuracy of this method is not high. Recently, deep learning has been successfully applied in multiple medical image analysis tasks. In this paper, we attempt to increase the accuracy of diagnosis of acute thoracic aortic dissection based on chest x-rays by applying deep learning techniques. In aggregate, 3,331 images, comprising 716 positive images and 2615 negative images, were collected from 3,331 patients. Residual neural network 18 was used to detect acute thoracic aortic dissection. The diagnostic accuracy of the ResNet18 was observed to be 90.20% with a precision of 75.00%, recall of 94.44%, and F1-score of 83.61%. Further research is required to improve diagnostic accuracy based on aorta segmentation.
急性胸主动脉夹层是一种危及生命的疾病，其特征是血液从主动脉受损的内膜层漏出，导致内膜和外膜之间发生夹层。这种疾病的诊断具有挑战性。胸部 X 射线通常用于初始筛查或诊断，但这种方法的诊断准确性不高。最近，深度学习已成功应用于多种医学图像分析任务。在本文中，我们尝试通过应用深度学习技术，基于胸部 X 射线提高急性胸主动脉夹层的诊断准确性。总共从 3331 名患者中收集了 3331 张图像，包括 716 张阳性图像和 2615 张阴性图像。使用残差神经网络 18 来检测急性胸主动脉夹层。ResNet18 的诊断准确性为 90.20%，精度为 75.00%，召回率为 94.44%，F1 得分为 83.61%。需要进一步研究基于主动脉分割来提高诊断准确性。
