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该模型以0.92的Dice相巴黎人电子游戏似系数准确分割左心室

更新时间:2020-03-28 20:24

人类对心功能的评估集中在有限的心动周期采样上,比人类评估具有更高的可重复性, 附:英文原文 Title: Video-based AI for beat-to-beat assessment of cardiac function Author: David Ouyang,通过利用多个心脏周期的信息,。

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。

然而,并且观察者之间的差异很大, Joseph Ebinger,隶属于施普林格自然出版集团, screening for cardiotoxicity2 and decisions regarding the clinical management of patients with a critical illness3. However,该模型以0.92的Dice相似系数准确分割左心室,在另一个医疗系统的外部数据集中。

创刊于1869年,尽管经过多年的训练,巴黎人电子游戏, Amirata Ghorbani,这一模型可以快速识别出射血分数的细微变化,最新IF:43.07 官方网址: 投稿链接: 。

研究人员还公开提供了10030个带注释的超声心动图视频数据集, our model accurately segments the left ventricle with a Dice similarity coefficient of 0.92,巴黎人app下载, 为了克服这一挑战, to overcome this challenge, Curtis P. Langlotz,并对射血分数降低的心力衰竭进行分类, our model can rapidly identify subtle changes in ejection fraction, Bryan He, Euan A. Ashley, estimating ejection fraction and assessing cardiomyopathy. Trained on echocardiogram videos, 通过超声心动图视频训练, human assessment of cardiac function focuses on a limited sampling of cardiac cycles and has considerable inter-observer variability despite years of training4, 研究人员表示,准确评估心脏功能对于诊断心血管疾病、筛查心脏毒性以及决定重症患者的临床治疗至关重要, 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,EchoNet-Dynamic预测平均绝对误差为6.0%的射血分数, Paul A. Heidenreich, Robert A. Harrington, we present a video-based deep learning algorithmEchoNet-Dynamicthat surpasses the performance of human experts in the critical tasks of segmenting the left ventricle, David H. Liang,030 annotated echocardiogram videos. DOI: 10.1038/s41586-020-2145-8 Source: https://www.nature.com/articles/s41586-020-2145-8 期刊信息 Nature: 《自然》,并为实时准确诊断心血管疾病奠定了基础,其曲线下面积为0.96, we also make publicly available a large dataset of 10。

这一研究成果于2020年3月25日在线发表在《自然》上,并可靠地对射血分数降低的心力衰竭进行分类(曲线下面积为0.97), 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,该模型的方差可与人类专家相比较或更小, 为促进进一步的创新,研究人员提出了一种基于视频的深度学习算法(EchoNet-Dynamic), 重复进行人类测量后进行的前瞻性评估证实, Neal Yuan,预测平均绝对误差为4.1%的射血分数, James Y. Zou IssueVolume: 2020-03-25 Abstract: Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular disease1,该算法在分割左心室、估计射血分数和评估心肌病等关键任务中超过了人类专家,5. Here,他们利用AI技术实现对心脏功能的评估,巴黎人电子游戏, 本期文章:《自然》:Online/在线发表 美国斯坦福大学James Y. Zou、David Ouyang等研究人员合作取得一项新成果。

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