Artificial intelligence for ventricular arrhythmia capability using ambulatory electrocardiograms
Artificial intelligence for ventricular arrhythmia capability using ambulatory electrocardiograms
复制标题
使用动态心电图治疗室性心律失常的人工智能能力
DOI：
10.1101/2023.12.18.23300017
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Barker J
中科院分区：
文献类型：
--
作者：
Barker J
欧洲和美国的植入式心律转复除颤器临床指南对室性心律失常（VA）风险分层不够准确，导致显著的发病率和死亡率。人工智能提供了一种新的风险分层透镜，通过它可以从正常心律的心电图（ECG）中确定VA能力。本研究的目的是开发和测试使用常规收集的动态心电图进行室性心动过速风险分层的深度神经网络。方法与结果采用多中心病例对照研究对我们基于开源resnet -50的深度神经网络VA-ResNet-50进行评估。VA- resnet -50被设计用于读取三导联24小时动态心电图的金字塔样本，以确定心脏是否能够仅根据心电图进行VA。研究人员招募了来自英国东米德兰兹（East Midlands）的连续成年VA患者，这些患者在2014年至2022年期间接受了门诊心电图作为其NHS护理的一部分，并将其与所有没有VA的患者的门诊心电图进行了比较。在270名患者中，159名异质患者具有复合VA结果。心电图与VA的平均时差为1.6年（VA前1 / 3动态心电图）。深度神经网络对心电图的VA能力分类准确率为0.76(95%置信区间为0.66 ~ 0.87)，F1评分为0.79(0.67 ~ 0.90)，接收算子曲线下面积为0.8(0.67 ~ 0.91)，相对危险度为2.87（1.41 ~ 5.81）。结论使用VA- resnet -50对动态心电图进行分析，可为室性心律失常风险分层提供风险信号。从动态心电图的金字塔取样被假设为捕获自主神经活动。我们鼓励团队在这个开源模型上进行构建。人工智能（AI）能否仅根据心电图（一种心脏电追踪）来预测一个人是否有致命的心律风险？在一项对270名成年人（其中159人患有致命性心律失常）的研究中，人工智能在每5个病例中有4个是正确的。如果人工智能说一个人有危险，致命事件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目前的医疗指南。人工智能能够在一年多的时间里，通过标准心脏追踪准确地确定80%的病例发生致命性心律失常的风险——这是人工智能模型所能看到和预测的概念上的转变。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）。
AimsEuropean 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 resultsA 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).ConclusionAmbulatory ECGs confer risk signals for VA risk stratification when analysed using VA-ResNet-50. Pyramid samplingfrom the ambulatory ECGs is hypothesized to capture autonomic activity. We encourage groups to build on this open-source model.QuestionCan 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)?FindingsIn 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.MeaningIn 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.
DOI：
10.1093/europace/euab021
发表时间：
2021-08-06
期刊：
Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology
影响因子：
--
作者：
Dhutia H;Malhotra A;Finocchiaro G;Parpia S;Bhatia R;D'Silva A;Gati S;Mellor G;Narain R;Chandra N;Behr E;Tome M;Papadakis M;Sharma S
通讯作者：
Sharma S
影响因子：
20.1
作者：
Hayashi M;Shimizu W;Albert CM
通讯作者：
Albert CM
LB-456090-4 使用人工智能单导联动态心电图对危及生命的室性心律失常进行近期预测
影响因子：
5.5
作者：
L. Fiorina;Tanner Carbonati;K. Narayanan;Jia Li;C. Henry;J. Singh;É. Marijon
通讯作者：
É. Marijon