论文标题
可解释的深度学习算法，用于通过超声心动图成像区分冠状动脉病变不完整的川崎疾病
Explainable Deep Learning Algorithm for Distinguishing Incomplete Kawasaki Disease by Coronary Artery Lesions on Echocardiographic Imaging
论文作者
论文摘要
背景和客观：由于缺乏经典KD的临床表现，川崎疾病（KD）不完整。然而，它与冠状动脉病变的患病率显着相关。通过超声心动图识别冠状动脉病变对于及时诊断和有利的结果很重要。此外，类似于KD，2019年冠状病毒病，目前引起全球大流行，也表现出发烧。因此，目前应该在儿童的高热疾病中明确区分KD至关重要。在这项研究中，我们旨在验证一种深入学习算法，以分类KD和其他急性发热疾病。 方法：我们通过儿童的超声心动图获得了冠状动脉图像（KD = 88；肺炎n = 65）。我们使用收集的数据培训了六个深度学习网络（VGG19，Xception，Resnet50，Resnext50，Se-Resnet50和Se-Resnext50）。 结果：Se-Resnext50在分类中的准确性，特异性和精度方面表现出最佳性能。 Se-Resnext50的精度为76.35％，灵敏度为82.64％，特异性为58.12％。 结论：我们的研究结果表明，深度学习算法在检测冠状动脉病变方面具有相似的性能，以促进KD的诊断。
Background and Objective: Incomplete Kawasaki disease (KD) has often been misdiagnosed due to a lack of the clinical manifestations of classic KD. However, it is associated with a markedly higher prevalence of coronary artery lesions. Identifying coronary artery lesions by echocardiography is important for the timely diagnosis of and favorable outcomes in KD. Moreover, similar to KD, coronavirus disease 2019, currently causing a worldwide pandemic, also manifests with fever; therefore, it is crucial at this moment that KD should be distinguished clearly among the febrile diseases in children. In this study, we aimed to validate a deep learning algorithm for classification of KD and other acute febrile diseases. Methods: We obtained coronary artery images by echocardiography of children (n = 88 for KD; n = 65 for pneumonia). We trained six deep learning networks (VGG19, Xception, ResNet50, ResNext50, SE-ResNet50, and SE-ResNext50) using the collected data. Results: SE-ResNext50 showed the best performance in terms of accuracy, specificity, and precision in the classification. SE-ResNext50 offered a precision of 76.35%, a sensitivity of 82.64%, and a specificity of 58.12%. Conclusions: The results of our study suggested that deep learning algorithms have similar performance to an experienced cardiologist in detecting coronary artery lesions to facilitate the diagnosis of KD.
