Avula Vennela, Wu Katherine C, Carrick Richard T
Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Division of Cardiology, Johns Hopkins University Department of Medicine, Baltimore, Maryland, USA.
JACC Adv. 2023 Dec;2(10). doi: 10.1016/j.jacadv.2023.100686. Epub 2023 Nov 8.
The electrocardiogram (ECG) is one of the most common diagnostic tools available to assess cardio-vascular health. The advent of advanced computational techniques such as deep learning has dramatically expanded the breadth of clinical problems that can be addressed using ECG data, leading to increasing popularity of ECG deep-learning models aimed at predicting clinical endpoints.
The purpose of this study was to define the current landscape of clinically relevant ECG deep-learning models and examine practices in the scientific reporting of these studies.
We performed a systematic review of PubMed and EMBASE databases to identify clinically relevant ECG deep-learning models published through July 1, 2022.
We identified 44 manuscripts including 53 unique, clinically relevant ECG deep-learning models. The rate of publication of ECG deep-learning models is increasing rapidly. The most common clinical applications of ECG deep learning were identification of cardiomyopathy (14/53 [26%]), followed by arrhythmia detection (9/53 [17%]). Methodologic reporting varied; while 33/44 (75%) publications included model architecture diagrams, complete information required to reproduce these models was provided in only 10/44 (23%). Saliency analysis was performed in 20/44 (46%) of publications. Only 18/53 (34%) models were tested within external validation cohorts. Model code or resources allowing for model implementation by external groups were available for only 5/44 (11%) publications.
While ECG deep-learning models are increasingly clinically relevant, their reporting is highly variable, and few publications provide sufficient detail for methodologic reproduction or model validation by external groups. The field of ECG deep learning would benefit from adherence to a set of standardized scientific reporting guidelines.
心电图（ECG）是评估心血管健康最常用的诊断工具之一。深度学习等先进计算技术的出现极大地扩展了利用心电图数据可解决的临床问题范围，导致旨在预测临床终点的心电图深度学习模型越来越受欢迎。
本研究的目的是界定临床相关心电图深度学习模型的现状，并审视这些研究在科学报告方面的做法。
我们对PubMed和EMBASE数据库进行了系统综述，以识别截至2022年7月1日发表的临床相关心电图深度学习模型。
我们识别出44篇手稿，其中包括53个独特的、临床相关的心电图深度学习模型。心电图深度学习模型的发表率正在迅速上升。心电图深度学习最常见的临床应用是心肌病的识别（14/53 [26%]），其次是心律失常检测（9/53 [17%]）。方法学报告各不相同；虽然33/44（75%）的出版物包含模型架构图，但只有10/44（23%）提供了重现这些模型所需的完整信息。20/44（46%）的出版物进行了显著性分析。只有18/53（34%）的模型在外部验证队列中进行了测试。只有5/44（11%）的出版物提供了允许外部团队实施模型的代码或资源。
虽然心电图深度学习模型在临床上的相关性越来越高，但其报告差异很大，很少有出版物提供足够的细节以供外部团队进行方法学重现或模型验证。心电图深度学习领域将受益于遵循一套标准化的科学报告指南。
