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深度学习辅助的左心回声图参数测量:改善了观察者之间的变化和工作流效率

Victor Mor-Avi¹, Alexandra Blitz², Marcus Schreckenberg²

¹University of Chicago Medicine, 5758 S. Maryland Ave., MC 9067, DCAM 5509, Chicago, IL, 60637, USA.

The international journal of cardiovascular imaging

|October 23, 2023

概括

深度学习 (DL) 算法自动化了心声回声测量,提高了准确性并减少了分析时间. 与传统方法相比,这种AI方法提高了左心评估的可重现性.

科学领域:

心脏病学心脏病学
医疗成像医学成像
人工智能的人工智能

背景情况:

声心成像对于评估左心功能至关重要.
对心声回声图的手动解释是耗时的,容易变化.
需要自动化来提高心声回声分析的效率和一致性.

研究的目的:

评估深度学习 (DL) 算法用于自动回声心脏学测量.
将DL方法的可复制性和分析时间与传统方法进行比较.
评估手动修订对DL辅助测量的影响.

主要方法:

用一个经过验证的DL算法对20个心脏左侧参数进行自动测量.
DL测量与12个心声回声研究中的传统手动测量进行了比较.
量化了读者间的变化和分析时间,并将方法进行了比较.

主要成果:

完全自动化的DL测量显示与参考标准有很好的一致性 (Bland-Altman偏差0-14%).
手动修订只提供了微小的准确性改进 (偏差为0-11%).
DL辅助方法减少了43%的分析时间,并减少了读者间的变化 (变化系数比较小2-3倍).

结论:

深度学习辅助的回声心脏图像分析提供了准确的左心脏测量.
与传统方法相比,DL方法显著提高了可重现性,并节省了时间.
自动化心声回声分析有望改善临床工作流程和诊断一致性.

关键词:

人工智能的人工智能是人工智能.机器学习机器学习两个维的心声回声图 (Echocardiography) 是一个二维的心声回声图.

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