克服量化和比较心电图成像方法的障碍:基于社区的方法。

Computing in cardiology Pub Date : 2017-09-01 Epub Date: 2018-04-05 DOI:10.22489/CinC.2017.370-289
Sandesh Ghimire, Jwala Dhamala, Jaume Coll-Font, Jess D Tate, Maria S Guillem, Dana H Brooks, Rob S MacLeod, Linwei Wang
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引用次数: 13

摘要

近年来,随着心电图成像(ECGI)方法的发展,临床应用也有了显著的增加。为了更好地评估最新技术,实现可靠的进展,并促进临床应用,能够全面、科学和临床地比较结果是很重要的。然而,研究在建模选择、计算方法、验证机制和指标以及临床应用方面各不相同,这使得ECGI的统一评估和比较成为一项关键挑战。本文描述了一个项目的初步结果,该项目通过由心电图成像协会(CEI)组织的以社区为基础的方法来解决这一挑战。我们详细介绍了这一集体努力的不同方面,包括数据共享存储库、在相同数据集上比较不同算法和建模方法的平台、几个活跃的工作组以及沿着这些方向取得的进展。我们还总结了参与此次合作的小组的结果,并通过将他们的方法应用于同一数据集进行比较来提供解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Overcoming Barriers to Quantification and Comparison of Electrocardiographic Imaging Methods: A Community-Based Approach.

Overcoming Barriers to Quantification and Comparison of Electrocardiographic Imaging Methods: A Community-Based Approach.

Overcoming Barriers to Quantification and Comparison of Electrocardiographic Imaging Methods: A Community-Based Approach.

There has been a recent upsurge in the development of electrocardiographic imaging (ECGI) methods, along with a significant increase in clinical application. To better assess the state-of-the-art, enable reliable progress, and facilitate clinical adoption, it is important to be able to compare results in a comprehensive manner, scientifically and clinically. However, studies vary in modeling choices, computational methods, validation mechanisms and metrics, and clinical applications, making unified evaluation and comparison of ECGI a critical challenge. This paper describes initial results of a project to address this challenge via a community-based approach organized by the Consortium for Electrocardiographic Imaging (CEI). We detail different aspects of this collective effort including a data sharing repository, a platform for comparison of different algorithms and modeling approaches on the same datasets, several active workgroups and progress made along these directions. We also summarize the results from groups participating in this collaboration and contributing solutions by applying their methods to the same dataset for comparison.

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