Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular disease¹, screening for cardiotoxicity² and decisions regarding the clinical management of patients with a critical illness³. However, human assessment of cardiac function focuses on a limited sampling of cardiac cycles and has considerable inter-observer variability despite years of training^4,5. Here, to overcome this challenge, we present a video-based deep learning algorithm—EchoNet-Dynamic—that surpasses the performance of human experts in the critical tasks of segmenting the left ventricle, estimating ejection fraction and assessing cardiomyopathy. Trained on echocardiogram videos, our model accurately segments the left ventricle with a Dice similarity coefficient of 0.92, predicts ejection fraction with a mean absolute error of 4.1% and reliably classifies heart failure with reduced ejection fraction (area under the curve of 0.97). In an external dataset from another healthcare system, EchoNet-Dynamic predicts the ejection fraction with a mean absolute error of 6.0% and classifies heart failure with reduced ejection fraction with an area under the curve of 0.96. Prospective evaluation with repeated human measurements confirms that the model has variance that is comparable to or less than that of human experts. By leveraging information across multiple cardiac cycles, our model can rapidly identify subtle changes in ejection fraction, is more reproducible than human evaluation and lays the foundation for precise diagnosis of cardiovascular disease in real time. As a resource to promote further innovation, we also make publicly available a large dataset of 10,030 annotated echocardiogram videos.

准确评估心功能对于心血管疾病的诊断¹ 、心脏毒性的筛查²以及危重患者的临床管理决策³至关重要。然而，人类对心脏功能的评估主要集中在有限的心动周期样本上，尽管经过多年的训练，观察者之间仍存在相当大的差异^4,5 。为了克服这一挑战，我们提出了一种基于视频的深度学习算法——EchoNet-Dynamic——在分割左心室、估计射血分数和评估心肌病等关键任务中，该算法超越了人类专家的表现。经过超声心动图视频训练，我们的模型以 Dice 相似系数 0.92 准确分割左心室，以平均绝对误差 4.1% 预测射血分数，并可靠地对射血分数降低的心力衰竭（曲线下面积 0.97）进行分类。在另一个医疗保健系统的外部数据集中，EchoNet-Dynamic 预测射血分数的平均绝对误差为 6.0%，并将射血分数降低的心力衰竭分类为曲线下面积 0.96。通过重复的人类测量进行的前瞻性评估证实，该模型的方差与人类专家的方差相当或更低。通过利用多个心动周期的信息，我们的模型可以快速识别射血分数的细微变化，比人类评估更具可重复性，并为心血管疾病的实时精确诊断奠定了基础。作为促进进一步创新的资源，我们还公开了包含 10,030 个带注释的超声心动图视频的大型数据集。