Echo(通信协议)
计算机科学
杠杆(统计)
卷积神经网络
人工智能
质量得分
帧(网络)
人工神经网络
操作员(生物学)
模式识别(心理学)
计算机视觉
数据挖掘
电信
工程类
基因
转录因子
生物化学
抑制因子
公制(单位)
化学
计算机网络
运营管理
作者
Ali A. Abdi,Christina Luong,Teresa Tsang,John Jue,Ken Gin,Darwin F. Yeung,Dale Hawley,Robert Rohling,Purang Abolmaesumi
标识
DOI:10.1007/978-3-319-66179-7_35
摘要
Echocardiography (echo) is a clinical imaging technique which is highly dependent on operator experience. We aim to reduce operator variability in data acquisition by automatically computing an echo quality score for real-time feedback. We achieve this with a deep neural network model, with convolutional layers to extract hierarchical features from the input echo cine and recurrent layers to leverage the sequential information in the echo cine loop. Using data from 509 separate patient studies, containing 2,450 echo cines across five standard echo imaging planes, we achieved a mean quality score accuracy of 85 $$\%$$ compared to the gold-standard score assigned by experienced echosonographers. The proposed approach calculates the quality of a given 20 frame echo sequence within 10 ms, sufficient for real-time deployment.
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