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开发和验证一个深度学习系统,用于对16段LV壁厚的心声评估

Guijuan Peng¹, Wufeng Xue², Yuanyuan Sheng¹

¹Department of Ultrasound, Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, China.

Ultrasound in medicine & biology

|March 14, 2026

概括

这项研究提出了一种深度学习方法,用于自动测量16段左心室壁厚度,提高诊断左心室缩的准确性和减少变异性.

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:

精确测量16段左心室壁厚度对于诊断左心室缩至关重要.
传统的方法耗时,容易变化.

研究的目的:

开发一种自动化的深度学习算法,用于测量16段左心室壁厚度.
为了验证该算法的 in vivo 和 in vitro 的性能.

主要方法:

在92,984个心声回声图上训练了一种深度学习算法.
对正常和增加壁厚的数据集和体外数据集进行了性能评估.
关键结果包括细分精度和壁厚测量精度.

主要成果:

该算法在左心室细分 (迪斯系数0.87) 和墙壁厚度测量 (平均绝对误差<1mm) 中实现了高精度.
在增加墙壁厚度的患者中,测量值处于临床可容忍值 (<2mm) 之内.
在体外验证显示,细分壁厚度的平均绝对误差为1.0mm.

结论:

深度学习使左心室心肌的可行和准确的细分成为可能.
开发的方法提供了精确的16段墙壁厚度测量,在体内和体外验证.
这种方法为改善左心室缩的诊断和临床应用提供了潜力.

关键词:

自动测量自动测量自动测量深度学习是一种深度学习.心声回声扫描 (Echocardiography) 是一种心声回声扫描.在左心室的左心室.墙壁厚度墙壁厚度

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