Purpose: Congenital heart defect (CHD) is the most common birth defect. Thoracic echocardiography (TTE) can provide sufficient cardiac structure information, evaluate hemodynamics and cardiac function, and is an effective method for atrial septal defect (ASD) examination. This paper aims to study a deep learning method based on cardiac ultrasound video to assist in ASD diagnosis. Materials and methods: We select two standard views of the atrial septum (subAS) and low parasternal four-compartment view (LPS4C) as the two views to identify ASD. We enlist data from 300 children patients as part of a double-blind experiment for five-fold cross-validation to verify the performance of our model. In addition, data from 30 children patients (15 positives and 15 negatives) are collected for clinician testing and compared to our model test results (these 30 samples do not participate in model training). We propose an echocardiography video-based atrial septal defect diagnosis system. In our model, we present a block random selection, maximal agreement decision and frame sampling strategy for training and testing respectively, resNet18 and r3D networks are used to extract the frame features and aggregate them to build a rich video-level representation. Results: We validate our model using our private dataset by five-cross validation. For ASD detection, we achieve 89.33 AUC, 84.95 accuracy, 85.70 sensitivity, 81.51 specificity and 81.99 F1 score. Conclusion: The proposed model is multiple instances learning-based deep learning model for video atrial septal defect detection which effectively improves ASD detection accuracy when compared to the performances of previous networks and clinical doctors.

翻译：目的：先天性心脏病是最常见的出生缺陷。经胸超声心动图能够提供充足的心脏结构信息，评估血流动力学和心功能，是房间隔缺损检查的有效方法。本文旨在研究一种基于心脏超声视频的深度学习方法辅助房间隔缺损诊断。材料与方法：我们选取房间隔的两个标准切面——剑突下心房切面和低位胸骨旁四腔心切面作为识别房间隔缺损的两个视图。我们招募了300例儿童患者的数据进行双盲实验，采用五折交叉验证评估模型性能。此外，另收集30例儿童患者数据（阳性15例、阴性15例）用于临床医生测试，并与模型测试结果进行对比（这30例样本不参与模型训练）。我们提出了一种基于超声心动图视频的房间隔缺损诊断系统。在模型中，分别采用块随机选择、最大一致性决策和帧采样策略进行训练和测试，使用ResNet18和R3D网络提取帧特征并进行聚合，构建丰富的视频级表示。结果：我们通过五折交叉验证在私有数据集上验证模型。在房间隔缺损检测中，我们获得了89.33的AUC值、84.95%的准确率、85.70%的灵敏度、81.51%的特异度和81.99%的F1分数。结论：所提出的模型是基于多实例学习的视频房间隔缺损检测深度学习模型，与既往网络和临床医生的检测结果相比，能有效提升房间隔缺损检测准确率。