Artificial intelligence for ventricular arrhythmia capability using ambulatory electrocardiograms.

IF 3.9 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS
European heart journal. Digital health Pub Date : 2024-01-30 eCollection Date: 2024-05-01 DOI:10.1093/ehjdh/ztae004
Joseph Barker, Xin Li, Ahmed Kotb, Akash Mavilakandy, Ibrahim Antoun, Chokanan Thaitirarot, Ivelin Koev, Sharon Man, Fernando S Schlindwein, Harshil Dhutia, Shui Hao Chin, Ivan Tyukin, William B Nicolson, G Andre Ng
{"title":"Artificial intelligence for ventricular arrhythmia capability using ambulatory electrocardiograms.","authors":"Joseph Barker, Xin Li, Ahmed Kotb, Akash Mavilakandy, Ibrahim Antoun, Chokanan Thaitirarot, Ivelin Koev, Sharon Man, Fernando S Schlindwein, Harshil Dhutia, Shui Hao Chin, Ivan Tyukin, William B Nicolson, G Andre Ng","doi":"10.1093/ehjdh/ztae004","DOIUrl":null,"url":null,"abstract":"<p><strong>Aims: </strong>European and American clinical guidelines for implantable cardioverter defibrillators are insufficiently accurate for ventricular arrhythmia (VA) risk stratification, leading to significant morbidity and mortality. Artificial intelligence offers a novel risk stratification lens through which VA capability can be determined from the electrocardiogram (ECG) in normal cardiac rhythm. The aim of this study was to develop and test a deep neural network for VA risk stratification using routinely collected ambulatory ECGs.</p><p><strong>Methods and results: </strong>A multicentre case-control study was undertaken to assess VA-ResNet-50, our open source ResNet-50-based deep neural network. VA-ResNet-50 was designed to read pyramid samples of three-lead 24 h ambulatory ECGs to decide whether a heart is capable of VA based on the ECG alone. Consecutive adults with VA from East Midlands, UK, who had ambulatory ECGs as part of their NHS care between 2014 and 2022 were recruited and compared with all comer ambulatory electrograms without VA. Of 270 patients, 159 heterogeneous patients had a composite VA outcome. The mean time difference between the ECG and VA was 1.6 years (⅓ ambulatory ECG before VA). The deep neural network was able to classify ECGs for VA capability with an accuracy of 0.76 (95% confidence interval 0.66-0.87), F1 score of 0.79 (0.67-0.90), area under the receiver operator curve of 0.8 (0.67-0.91), and relative risk of 2.87 (1.41-5.81).</p><p><strong>Conclusion: </strong>Ambulatory ECGs confer risk signals for VA risk stratification when analysed using VA-ResNet-50<i>. Pyramid sampling</i> from the ambulatory ECGs is hypothesized to capture autonomic activity. We encourage groups to build on this open-source model.</p><p><strong>Question: </strong>Can artificial intelligence (AI) be used to predict whether a person is at risk of a lethal heart rhythm, based solely on an electrocardiogram (an electrical heart tracing)?</p><p><strong>Findings: </strong>In a study of 270 adults (of which 159 had lethal arrhythmias), the AI was correct in 4 out of every 5 cases. If the AI said a person was at risk, the risk of lethal event was three times higher than normal adults.</p><p><strong>Meaning: </strong>In this study, the AI performed better than current medical guidelines. The AI was able to accurately determine the risk of lethal arrhythmia from standard heart tracings for 80% of cases over a year away-a conceptual shift in what an AI model can see and predict. This method shows promise in better allocating implantable shock box pacemakers (implantable cardioverter defibrillators) that save lives.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":3.9000,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11104464/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"European heart journal. Digital health","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/ehjdh/ztae004","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/5/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

Aims: European and American clinical guidelines for implantable cardioverter defibrillators are insufficiently accurate for ventricular arrhythmia (VA) risk stratification, leading to significant morbidity and mortality. Artificial intelligence offers a novel risk stratification lens through which VA capability can be determined from the electrocardiogram (ECG) in normal cardiac rhythm. The aim of this study was to develop and test a deep neural network for VA risk stratification using routinely collected ambulatory ECGs.

Methods and results: A multicentre case-control study was undertaken to assess VA-ResNet-50, our open source ResNet-50-based deep neural network. VA-ResNet-50 was designed to read pyramid samples of three-lead 24 h ambulatory ECGs to decide whether a heart is capable of VA based on the ECG alone. Consecutive adults with VA from East Midlands, UK, who had ambulatory ECGs as part of their NHS care between 2014 and 2022 were recruited and compared with all comer ambulatory electrograms without VA. Of 270 patients, 159 heterogeneous patients had a composite VA outcome. The mean time difference between the ECG and VA was 1.6 years (⅓ ambulatory ECG before VA). The deep neural network was able to classify ECGs for VA capability with an accuracy of 0.76 (95% confidence interval 0.66-0.87), F1 score of 0.79 (0.67-0.90), area under the receiver operator curve of 0.8 (0.67-0.91), and relative risk of 2.87 (1.41-5.81).

Conclusion: Ambulatory ECGs confer risk signals for VA risk stratification when analysed using VA-ResNet-50. Pyramid sampling from the ambulatory ECGs is hypothesized to capture autonomic activity. We encourage groups to build on this open-source model.

Question: Can artificial intelligence (AI) be used to predict whether a person is at risk of a lethal heart rhythm, based solely on an electrocardiogram (an electrical heart tracing)?

Findings: In a study of 270 adults (of which 159 had lethal arrhythmias), the AI was correct in 4 out of every 5 cases. If the AI said a person was at risk, the risk of lethal event was three times higher than normal adults.

Meaning: In this study, the AI performed better than current medical guidelines. The AI was able to accurately determine the risk of lethal arrhythmia from standard heart tracings for 80% of cases over a year away-a conceptual shift in what an AI model can see and predict. This method shows promise in better allocating implantable shock box pacemakers (implantable cardioverter defibrillators) that save lives.

人工智能利用动态心电图识别室性心律失常。
目的:欧洲和美国的植入式心脏除颤器临床指南在室性心律失常(VA)风险分层方面不够准确,导致了严重的发病率和死亡率。人工智能提供了一种新的风险分层视角,可通过正常心律的心电图(ECG)确定室性心律失常的能力。本研究的目的是利用日常收集的非卧床心电图,开发并测试用于VA风险分层的深度神经网络:我们开展了一项多中心病例对照研究,以评估我们基于开源 ResNet-50 的深度神经网络 VA-ResNet-50。VA-ResNet-50旨在读取24小时三导联动态心电图的金字塔样本,从而仅根据心电图判断心脏是否能够发生VA。研究人员招募了英国东米德兰兹地区在2014年至2022年期间接受非卧床心电图检查的连续成人VA患者,并将其与所有无VA患者的非卧床心电图进行了比较。在 270 名患者中,有 159 名异质性患者有综合 VA 结果。心电图与 VA 之间的平均时间差为 1.6 年(VA 之前的 ⅓ 动态心电图)。深度神经网络能够对心电图进行VA能力分类,准确率为0.76(95%置信区间为0.66-0.87),F1得分为0.79(0.67-0.90),接收者操作曲线下面积为0.8(0.67-0.91),相对风险为2.87(1.41-5.81):结论:使用 VA-ResNet-50 进行分析时,动态心电图可为 VA 风险分层提供风险信号。从动态心电图中进行金字塔取样可捕捉自律神经活动。我们鼓励各小组在这一开源模型的基础上再接再厉:人工智能(AI)能否仅根据心电图(心电描记图)预测一个人是否有致命心律的风险?在一项针对 270 名成年人(其中 159 人患有致命性心律失常)的研究中,人工智能每 5 个案例中就有 4 个是正确的。如果人工智能认为一个人有危险,那么其发生致死性心律失常的风险是正常成年人的三倍:在这项研究中,人工智能的表现优于现行的医疗指南。在超过一年的病例中,人工智能能够从标准心脏描记图中准确判断出80%的致命性心律失常风险--这是人工智能模型所能看到和预测的概念性转变。这种方法有望更好地分配可挽救生命的植入式电击盒起搏器(植入式心律转复除颤器)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
5.00
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信