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从左心房内径到曲线M型散斑追踪图像:超声心动图在评估心房颤动患者中的作用

From Left Atrial Dimension to Curved M-Mode Speckle-Tracking Images: Role of Echocardiography in Evaluating Patients with Atrial Fibrillation.

作者信息

Liu Hao-Tien, Lee Hui-Ling, Chou Chung-Chuan

机构信息

Division of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital, Linkou branch, 33304 Taoyuan, Taiwan.

Department of Anesthesia, Chang Gung Memorial Hospital, Taipei branch, 10507 Taipei, Taiwan.

出版信息

Rev Cardiovasc Med. 2022 May 11;23(5):171. doi: 10.31083/j.rcm2305171. eCollection 2022 May.

Abstract

Left atrial (LA) enlargement and dysfunction increase the risk of atrial fibrillation (AF). Traditional echocardiographic evaluation of the left atrium has been limited to dimensional and semi-quantification measurement of the atrial component of ventricular filling, with routine measurement of LA function not yet implemented. However, functional parameters, such as LA emptying fraction (LAEF), may be more sensitive markers for detecting AF-related changes than LA enlargement. Speckle-tracking echocardiography has proven to be a feasible and reproducible technology for the direct evaluation of LA function. The clinical application, advantages, and limitations of LA strain and strain rate need to be fully understood. Furthermore, the prognostic value and utility of this technique in making therapeutic decisions for patients with AF need further elucidation. Deep learning neural networks have been successfully adapted to specific tasks in echocardiographic image analysis, and fully automated measurements based on artificial intelligence could facilitate the clinical diagnostic use of LA speckle-tracking images for classification of AF ablation outcome. This review describes the fundamental concepts and a brief overview of the prognostic utility of LA size, LAEF, LA strain and strain rate analyses, and the clinical implications of the use of these measures.

摘要

左心房(LA)扩大和功能障碍会增加心房颤动(AF)的风险。传统超声心动图对左心房的评估仅限于心室充盈时心房部分的尺寸和半定量测量,尚未实施左心房功能的常规测量。然而,诸如左心房排空分数(LAEF)等功能参数可能是比左心房扩大更敏感的检测房颤相关变化的指标。斑点追踪超声心动图已被证明是直接评估左心房功能的一种可行且可重复的技术。需要充分了解左心房应变和应变率的临床应用、优势和局限性。此外,该技术在房颤患者治疗决策中的预后价值和实用性需要进一步阐明。深度学习神经网络已成功应用于超声心动图图像分析的特定任务,基于人工智能的全自动测量有助于临床将左心房斑点追踪图像用于房颤消融结果的分类诊断。本综述描述了左心房大小、左心房排空分数、左心房应变和应变率分析的基本概念及其预后效用的简要概述,以及使用这些测量方法的临床意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/42c0/11273969/1ee68575cdbe/2153-8174-23-5-171-g1.jpg

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