Echocardiography uses ultrasound technology to capture high temporal and spatial resolution images of the heart and surrounding structures, and is the most common imaging modality in cardiovascular medicine. Using convolutional neural networks on a large new dataset, we show that deep learning applied to echocardiography can identify local cardiac structures, estimate cardiac function, and predict systemic phenotypes that modify cardiovascular risk but not readily identifiable to human interpretation. Our deep learning model, EchoNet, accurately identified the presence of pacemaker leads (AUC = 0.89), enlarged left atrium (AUC = 0.86), left ventricular hypertrophy (AUC = 0.75), left ventricular end systolic and diastolic volumes ( R 2  = 0.74 and R 2  = 0.70), and ejection fraction ( R 2  = 0.50), as well as predicted systemic phenotypes of age ( R 2  = 0.46), sex (AUC = 0.88), weight ( R 2  = 0.56), and height ( R 2  = 0.33). Interpretation analysis validates that EchoNet shows appropriate attention to key cardiac structures when performing human-explainable tasks and highlights hypothesis-generating regions of interest when predicting systemic phenotypes difficult for human interpretation. Machine learning on echocardiography images can streamline repetitive tasks in the clinical workflow, provide preliminary interpretation in areas with insufficient qualified cardiologists, and predict phenotypes challenging for human evaluation.

中文翻译：

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超声心动图的深度学习解释。

超声心动图使用超声技术捕获心脏和周围结构的高时间和空间分辨率图像，是心血管医学中最常见的成像方式。在大型新数据集上使用卷积神经网络，我们表明应用于超声心动图的深度学习可以识别局部心脏结构，估计心脏功能，并预测改变心血管风险但不易被人类解释识别的系统表型。我们的深度学习模型 EchoNet 准确识别了起搏器导联的存在 (AUC = 0.89)、左心房扩大 (AUC = 0.86)、左心室肥大 (AUC = 0.75)、左心室收缩末期和舒张期容积 (R 2 = 0.74)和 R 2 = 0.70）、射血分数（R 2 = 0.50），以及年龄（R 2 = 0.46）、性别（AUC = 0.88）、体重（R 2 = 0.56）和身高（R 2 = 0.56）的预测全身表型（ R 2 = 0.33）。解释分析验证了 EchoNet 在执行人类可解释的任务时对关键心脏结构表现出适当的关注，并在预测人类难以解释的系统表型时突出显示假设生成的感兴趣区域。超声心动图图像的机器学习可以简化临床工作流程中的重复性任务，在合格心脏病专家不足的领域提供初步解释，并预测对人类评估具有挑战性的表型。