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通过心声回声学进行深度学习实时指导,以减少左心室缩短和测量变化

Sigbjorn Sabo¹, Hakon Neergaard Pettersen^1,2, Erik Smistad^1,3

¹Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, NTNU, Box 8905, 7491 Trondheim, Norway.

European heart journal. Imaging methods and practice

|July 24, 2024

概括

实时深度学习 (DL) 引导心声回声学减少左心室 (LV) 缩短. 虽然DL指导改善了图像标准化,但对于经验丰富的用户来说,它对观察者之间的变异性的影响是最小的.

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:

在心声回声学中,角缩短导致左心室 (LV) 体积和射出分数测量不准确.
深度学习 (DL) 指导提供了一个潜在的解决方案,以减少预先缩短和改善心声回声学中的标准化.

研究的目的:

为了评估实时DL指导对LV缩短在心声回声学期间的影响.
评估DL指导对LV测量的观察者间变异性的影响.

主要方法:

88名患者接受了三次心声回声图:两次由超声波仪 (第1期:标准,第2期:有DL指导) 和一次由心脏病学家 (参考) 进行.
测量了LV的长度,并计算了在终端-透气缩短的顶端缩短.
类内相关性 (ICC) 用于评估观察者之间的变异性.

主要成果:

与标准成像相比,实时DL指导显著减少了超声波器中的LV预缩短 (第1期与第2期,P <0.05).
使用DL指导的超声波检查人员与心脏病学家相比,没有显著的缩短差异 (P ≥0.409).
DL指导并没有显著改善LV终端-透析体积的类内相关性 (DL指导:0.87与没有指导:0.92).

结论:

实时DL指导有效地减少了LV的缩短,并显示了改进心声图像标准化的潜力.
虽然经验丰富的用户对操作员之间的变异性的影响很小,但DL指导可能会提高经验较少的操作员的测试-重新测试可靠性.

关键词:

角的预先缩短.人工智能的人工智能是人工智能.超声心电图 (Echocardiography) 是一种心声回声仪.喷射分数的喷射分数是什么全球纵向菌株全球纵向菌株

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