Real-Time Global Longitudinal Strain During Echocardiography: A Deep Learning Platform for Improved Workflow.

IF 6 2区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS
Vegard Holmstrøm, Erik Smistad, Stian Stølen, Espen Holte, Lasse Løvstakken, Håvard Dalen, Andreas Østvik, Bjørnar Grenne
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引用次数: 0

Abstract

Background: Left ventricular (LV) global longitudinal strain (GLS) offers advantages over LV ejection fraction, including improved diagnostic sensitivity, reproducibility, and prognostic value. However, current semiautomatic analyses are time-consuming and operator dependent, impeding widespread adoption of GLS in routine clinical practice.

Objectives: We aimed to assess the feasibility, precision, and time efficiency of GLS measurements using a deep learning (DL) platform that performs real-time GLS analysis during image acquisition and incorporates DL tools to support standardization, to evaluate whether DL-assisted acquisitions can enhance image quality metrics relevant to strain analyses.

Methods: A DL platform was developed for fully automated real-time GLS analysis, including tools that detect and alert the operator to foreshortening or baseline drift. In this controlled prospective study, 50 patients (mean age, 56 years; 64% male) were included. Two image sets were acquired by different operators using the DL platform and a conventional workflow, and GLS and image quality were compared.

Results: Overall feasibility of DL-based GLS measurements was 94%. Absolute GLS was 14.8 ± 3.2 using the DL platform workflow and 16.2 ± 3.3 with manual reference measurements, with a bias of -1.3 and limits of agreement ranging from -3.5 to 0.8. Correlation was excellent (r = 0.94). Images acquired with the DL platform showed significantly less baseline drift and borderline improved territorial strain agreement than the reference acquisition. The median time obtaining GLS with the DL platform was reduced by 57% compared to the conventional workflow, from 4 minutes and 48 seconds to 2 minutes and 4 seconds.

Conclusion: The DL platform for fully automated real-time GLS measurements was feasible, precise, and time efficient. Real-time DL-based feedback allows operators to optimize images during acquisition, thus improving quality metrics relevant to GLS analyses. Implementing this method in clinical practice could streamline workflow and improve efficiency in the echocardiographic laboratory.

超声心动图过程中的实时全局纵向应变:改进工作流程的深度学习平台。
背景:左室(LV)整体纵向应变(GLS)比左室射血分数具有优势,包括更高的诊断敏感性、可重复性和预后价值。然而,目前的半自动分析耗时且依赖于操作人员,阻碍了GLS在常规临床实践中的广泛采用。目的:我们旨在利用深度学习(DL)平台评估GLS测量的可行性、精度和时间效率,该平台在图像采集过程中执行实时GLS分析,并结合DL工具来支持标准化,以评估DL辅助采集是否可以提高与应变分析相关的图像质量指标。方法:开发了全自动实时GLS分析的DL平台,包括检测和提醒操作人员预见缩短或基线漂移的工具。在这项前瞻性对照研究中,纳入了50例患者(平均年龄56岁,男性占64%)。利用深度学习平台和传统的工作流程,由不同的操作员获取两组图像集,并对GLS和图像质量进行比较。结果:基于dl的GLS测量总体可行性为94%。使用DL平台工作流程的绝对GLS为14.8±3.2,手动参考测量的绝对GLS为16.2±3.3,偏差为-1.3,一致性范围为-3.5至0.8。相关性极好(r = 0.94)。与参考图像相比,DL平台获得的图像显示基线漂移明显减少,边界区域应变一致性得到改善。与传统工作流程相比,使用DL平台获得GLS的中位数时间减少了57%,从4分48秒减少到2分4秒。结论:全自动实时测量GLS的DL平台是可行的、精确的、省时的。基于dl的实时反馈使作业者能够在采集过程中优化图像,从而提高与GLS分析相关的质量指标。将该方法应用于临床,可简化超声心动图实验室的工作流程,提高工作效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
9.50
自引率
12.30%
发文量
257
审稿时长
66 days
期刊介绍: The Journal of the American Society of Echocardiography(JASE) brings physicians and sonographers peer-reviewed original investigations and state-of-the-art review articles that cover conventional clinical applications of cardiovascular ultrasound, as well as newer techniques with emerging clinical applications. These include three-dimensional echocardiography, strain and strain rate methods for evaluating cardiac mechanics and interventional applications.
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