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开发和验证深度学习管道,以测量心心回声学中的心周溢出

Chi-Yung Cheng^1,2, Cheng-Ching Wu^3,4,5, Huang-Chung Chen⁶

¹Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan.

Frontiers in cardiovascular medicine

|August 21, 2023

概括

一个新的深度学习管道自动测量心周溢出 (PE) 从心声回声片段. 这种自动化方法提供了标准化和客观的方法,减少了运营商的依赖,提高了PE评估的准确性.

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:

目前测量心周溢出 (PE) 的方法取决于操作者,并且可能具有挑战性.
对PE的客观测量对于准确的诊断和治疗至关重要.

研究的目的:

开发一个深度学习管道,从原始心声回声学剪辑中自动测量心周溢出 (PE).
克服当前依赖操作者的PE测量技术的局限性.

主要方法:

开发了一个三步管道:移动窗口视图选择 (MWVS),自动细分和宽度计算.
MWVS模型使用ResNet进行框架分类,然后对PE区域进行自动细分.
计算机视觉技术从细分面具计算出最大的PE宽度,应用于995次心声回声检查.

主要成果:

深度学习管道实现了高性能,课内相关系数 (ICC) 为0.867 (内部验证) 和0.801 (外部验证).
该管道在PE检测方面表现出高精度,接收操作特征曲线 (AUC) 下的面积为0.926 (内部) 和0.842 (外部).

结论:

开发的机器学习管道自动从超声波剪辑计算PE宽度.
像MWVS和计算机视觉等新技术用于最大PE宽度计算,可以增强超声波分析.
该管道为PE测量提供了一种标准化,客观和潜在的更准确的方法,减少了运营商的依赖.

关键词:

自动化细分的自动化细分.深度学习人工智能超声心电图 (Echocardiography) 是一种心声回声仪.移动窗口 (MW) 的移动窗口心周溢出 (PE) 是一种心周溢出.宽度测量宽度测量宽度测量

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