Handra Julia, James Hannah, Mbilinyi Ashery, Moller-Hansen Ashley, O'Riley Callum, Andrade Jason, Deyell Marc, Hague Cameron, Hawkins Nathaniel, Ho Kendall, Hu Ricky, Leipsic Jonathon, Tam Roger
Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.
School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada.
JMIR Cardio. 2024 Dec 30;8:e60697. doi: 10.2196/60697.
Cardiovascular disease remains the leading cause of mortality worldwide. Cardiac fibrosis impacts the underlying pathophysiology of many cardiovascular diseases by altering structural integrity and impairing electrical conduction. Identifying cardiac fibrosis is essential for the prognosis and management of cardiovascular disease; however, current diagnostic methods face challenges due to invasiveness, cost, and inaccessibility. Electrocardiograms (ECGs) are widely available and cost-effective for monitoring cardiac electrical activity. While ECG-based methods for inferring fibrosis exist, they are not commonly used due to accuracy limitations and the need for cardiac expertise. However, the ECG shows promise as a target for machine learning (ML) applications in fibrosis detection.
This study aims to synthesize and critically evaluate the current state of ECG-based ML approaches for cardiac fibrosis detection.
We conducted a scoping review of research in ECG-based ML applications to identify cardiac fibrosis. Comprehensive searches were performed in PubMed, IEEE Xplore, Scopus, Web of Science, and DBLP databases, including publications up to October 2024. Studies were included if they applied ML techniques to detect cardiac fibrosis using ECG or vectorcardiogram data and provided sufficient methodological details and outcome metrics. Two reviewers independently assessed eligibility and extracted data on the ML models used, their performance metrics, study designs, and limitations.
We identified 11 studies evaluating ML approaches for detecting cardiac fibrosis using ECG data. These studies used various ML techniques, including classical (8/11, 73%), ensemble (3/11, 27%), and deep learning models (4/11, 36%). Support vector machines were the most used classical model (6/11, 55%), with the best-performing models of each study achieving accuracies of 77% to 93%. Among deep learning approaches, convolutional neural networks showed promising results, with one study reporting an area under the receiver operating characteristic curve (AUC) of 0.89 when combined with clinical features. Notably, a large-scale convolutional neural network study (n=14,052) achieved an AUC of 0.84 for detecting cardiac fibrosis, outperforming cardiologists (AUC 0.63-0.66). However, many studies had limited sample sizes and lacked external validation, potentially impacting the generalizability of the findings. Variability in reporting methods may affect the reproducibility and applicability of these ML-based approaches.
ML-augmented ECG analysis shows promise for accessible and cost-effective detection of cardiac fibrosis. However, there are common limitations with respect to study design and insufficient external validation, raising concerns about the generalizability and clinical applicability of the findings. Inconsistencies in methodologies and incomplete reporting further impede cross-study comparisons. Future work may benefit from using prospective study designs, larger and more clinically and demographically diverse datasets, advanced ML models, and rigorous external validation. Addressing these challenges could pave the way for the clinical implementation of ML-based ECG detection of cardiac fibrosis to improve patient outcomes and health care resource allocation.
心血管疾病仍然是全球范围内的主要死因。心脏纤维化通过改变结构完整性和损害电传导，影响许多心血管疾病的潜在病理生理学。识别心脏纤维化对于心血管疾病的预后和管理至关重要；然而，当前的诊断方法由于具有侵入性、成本高以及难以实施等问题而面临挑战。心电图（ECG）广泛可用且成本效益高，可用于监测心脏电活动。虽然存在基于心电图推断纤维化的方法，但由于准确性有限以及需要心脏专业知识，这些方法并不常用。然而，心电图有望成为机器学习（ML）在纤维化检测中的应用目标。
本研究旨在综合并批判性地评估基于心电图的ML方法在心脏纤维化检测中的现状。
我们对基于心电图的ML应用于识别心脏纤维化的研究进行了范围综述。在PubMed、IEEE Xplore、Scopus、Web of Science和DBLP数据库中进行了全面检索，包括截至2024年10月的出版物。如果研究应用ML技术使用心电图或向量心电图数据检测心脏纤维化，并提供足够的方法学细节和结果指标，则纳入研究。两名评审员独立评估研究的 eligibility 并提取有关所使用的ML模型、其性能指标、研究设计和局限性的数据。
我们确定了(11)项评估使用心电图数据检测心脏纤维化的ML方法的研究。这些研究使用了各种ML技术，包括经典模型（(8/11)，(73%)）、集成模型（(3/11)，(27%)）和深度学习模型（(4/11)，(36%)）。支持向量机是最常用的经典模型（(6/11)，(55%)），每项研究中表现最佳的模型准确率达到(77%)至(93%)。在深度学习方法中，卷积神经网络显示出有前景的结果，一项研究报告称，与临床特征相结合时，受试者工作特征曲线（AUC）下面积为(0.89)。值得注意的是，一项大规模卷积神经网络研究（(n = 14,052)）在检测心脏纤维化方面的AUC为(0.84)，优于心脏病专家（AUC为(0.63 - 0.66)）。然而，许多研究样本量有限且缺乏外部验证，这可能会影响研究结果的普遍性。报告方法的差异可能会影响这些基于ML的方法的可重复性和适用性。
ML增强的心电图分析有望实现对心脏纤维化的可及且经济高效的检测。然而，在研究设计和外部验证不足方面存在共同局限性，这引发了对研究结果的普遍性和临床适用性的担忧。方法学上的不一致和报告不完整进一步阻碍了跨研究比较。未来的工作可能受益于使用前瞻性研究设计、更大且临床和人口统计学上更多样化的数据集、先进的ML模型以及严格的外部验证。应对这些挑战可为基于ML的心电图检测心脏纤维化的临床应用铺平道路，以改善患者预后和医疗资源分配。
