GSTDTAP

浏览/检索结果: 共4条,第1-4条 帮助

已选(0)清除 条数/页:   排序方式:
Video-based AI for beat-to-beat assessment of cardiac function 期刊论文
NATURE, 2020, 580 (7802) : 252-+
作者:  Pleguezuelos-Manzano, Cayetano;  Puschhof, Jens;  Huber, Axel Rosendahl;  van Hoeck, Arne;  Wood, Henry M.;  Nomburg, Jason;  Gurjao, Carino;  Manders, Freek;  Dalmasso, Guillaume;  Stege, Paul B.;  Paganelli, Fernanda L.;  Geurts, Maarten H.;  Beumer, Joep;  Mizutani, Tomohiro;  Miao, Yi;  van der Linden, Reinier;  van der Elst, Stefan;  Garcia, K. Christopher;  Top, Janetta;  Willems, Rob J. L.;  Giannakis, Marios;  Bonnet, Richard;  Quirke, Phil;  Meyerson, Matthew;  Cuppen, Edwin;  van Boxtel, Ruben;  Clevers, Hans
收藏  |  浏览/下载:136/0  |  提交时间:2020/07/03

A video-based deep learning algorithm-EchoNet-Dynamic-accurately identifies subtle changes in ejection fraction and classifies heart failure with reduced ejection fraction using information from multiple cardiac cycles.


Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular disease(1), screening for cardiotoxicity(2) and decisions regarding the clinical management of patients with a critical illness(3). 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.


  
Using Physically Based Synthetic Peak Flows to Assess Local and Regional Flood Frequency Analysis Methods 期刊论文
WATER RESOURCES RESEARCH, 2019
作者:  Perez, Gabriel;  Mantilla, Ricardo;  Krajewski, Witold F.;  Wright, Daniel B.
收藏  |  浏览/下载:21/0  |  提交时间:2019/11/27
peak flow quantiles  at-site flood frequency analysis  regional flood frequency analysis  Monte Carlo simulation  epistemic error  sampling error  
Characteristics of Satellite Sampling Errors in Total Precipitable Water from SSMIS, HIRS, and COSMIC Observations 期刊论文
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2019, 124 (13) : 6966-6981
作者:  Xue, Yunheng;  Li, Jun;  Menzel, W. Paul;  Borbas, Eva;  Ho, Shu-Peng;  Li, Zhenglong;  Li, Jinlong
收藏  |  浏览/下载:11/0  |  提交时间:2019/11/27
TPW variation  remote sensing  sampling error  
From Point to Area: Worldwide Assessment of the Representativeness of Monthly Surface Solar Radiation Records 期刊论文
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2018, 123 (24) : 13857-13874
作者:  Schwarz, M.;  Folini, D.;  Hakuba, M. Z.;  Wild, M.
收藏  |  浏览/下载:27/0  |  提交时间:2019/04/09
surface solar radiation  representativeness  near-global assessment  decorrelation length  spatial sampling error  spatial sampling bias