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Artificial Intelligence-Enabled Electrocardiograms: Do 15-Lead Studies Improve Model Performance?

作者信息

Mayourian Joshua, Ghelani Sunil, Triedman John K

机构信息

Department of Cardiology, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA.

Department of Cardiology, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA.

出版信息

JACC Adv. 2025 Jul;4(7):101866. doi: 10.1016/j.jacadv.2025.101866. Epub 2025 Jun 25.

DOI:10.1016/j.jacadv.2025.101866
PMID:40570608
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12246699/
Abstract
摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f9c4/12246699/bacff137c884/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f9c4/12246699/bacff137c884/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f9c4/12246699/bacff137c884/gr1.jpg

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本文引用的文献

1
Electrocardiogram-based deep learning to predict mortality in paediatric and adult congenital heart disease.基于心电图的深度学习预测小儿及成人先天性心脏病死亡率
Eur Heart J. 2025 Mar 3;46(9):856-868. doi: 10.1093/eurheartj/ehae651.
2
Deep Learning-Based Electrocardiogram Analysis Predicts Biventricular Dysfunction and Dilation in Congenital Heart Disease.基于深度学习的心电图分析预测先天性心脏病的双心室功能障碍和扩张。
J Am Coll Cardiol. 2024 Aug 27;84(9):815-828. doi: 10.1016/j.jacc.2024.05.062.
3
A novel multi-scale 2D CNN with weighted focal loss for arrhythmias detection on varying-dimensional ECGs.
一种新颖的多尺度 2D CNN 结合加权焦点损失，用于在不同维度的 ECG 上进行心律失常检测。
Physiol Meas. 2022 Oct 31;43(10). doi: 10.1088/1361-6579/ac7695.
4
Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram.使用人工智能心电图筛查心脏收缩功能障碍。
Nat Med. 2019 Jan;25(1):70-74. doi: 10.1038/s41591-018-0240-2. Epub 2019 Jan 7.