Detection of Hypertrophic Cardiomyopathy Using a Convolutional Neural Network-Enabled Electrocardiogram
Detection of Hypertrophic Cardiomyopathy Using a Convolutional Neural Network-Enabled Electrocardiogram
复制标题
使用卷积神经网络的心电图检测肥厚性心肌病
DOI：
10.1016/j.jacc.2019.12.030
复制
发表时间：
2020-02-25
影响因子：
24
通讯作者：
Noseworthy, Peter A.
中科院分区：
文献类型：
--
作者：
Ko, Wei-Yin;Siontis, Konstantinos C.;Noseworthy, Peter A.
肥厚型心肌病（HCM）是心脏性猝死的一种罕见但重要的原因。本研究旨在开发一种基于12导联心电图（ECG）检测HCM的人工智能方法。使用数字12导联心电图对卷积神经网络（CNN）进行训练和验证，这些心电图来自2，448名经验证的HCM诊断患者和51，153名年龄和性别匹配的非HCM对照受试者。CNN检测HCM的能力随后在612名HCM和12，788名对照受试者的不同数据集上进行了测试。结果：在合并数据集中，HCM组的平均年龄为54.8 ± 15.9岁，对照组为57.5 ± 15.5岁。经过训练和验证后，验证数据集中CNN的曲线下面积（AUC）为0.95（95%置信区间[CI]：0.94至0.97），最佳概率阈值为11%。当将此概率阈值应用于测试数据集时，CNN的AUC为0.96（95% CI：0.95至0.96），灵敏度为87%，特异性为90%。在亚组分析中，根据ECG标准，左心室肥大患者的AUC为0. 95（95% CI：0. 94 - 0. 97），ECG正常患者的AUC为0. 95（95% CI：0. 90 - 1. 00）。该模型在年轻患者中表现特别好（灵敏度95%，特异性92%）。在有和没有肌节突变的HCM患者中，模型推导的HCM中位概率分别为97%和96%。结论：通过人工智能算法进行基于ECG的HCM检测可以实现高诊断性能，特别是在年轻患者中。该模型需要进一步完善和外部验证，但它可能对HCM筛查有希望。(c)2020年由美国心脏病学会基金会。
BACKGROUND Hypertrophic cardiomyopathy (HCM) is an uncommon but important cause of sudden cardiac death. OBJECTIVES This study sought to develop an artificial intelligence approach for the detection of HCM based on 12-lead electrocardiography (ECG). METHODS A convolutional neural network (CNN) was trained and validated using digital 12-lead ECG from 2,448 patients with a verified HCM diagnosis and 51,153 non-HCM age- and sex-matched control subjects. The ability of the CNN to detect HCM was then tested on a different dataset of 612 HCM and 12,788 control subjects. RESULTS In the combined datasets, mean age was 54.8 +/- 15.9 years for the HCM group and 57.5 +/- 15.5 years for the control group. After training and validation, the area under the curve (AUC) of the CNN in the validation dataset was 0.95 (95% confidence interval [CI]: 0.94 to 0.97) at the optimal probability threshold of 11% for having HCM. When applying this probability threshold to the testing dataset, the CNN's AUC was 0.96 (95% CI: 0.95 to 0.96) with sensitivity 87% and specificity 90%. In subgroup analyses, the AUC was 0.95 (95% CI: 0.94 to 0.97) among patients with left ventricular hypertrophy by ECG criteria and 0.95 (95% CI: 0.90 to 1.00) among patients with a normal ECG. The model performed particularly well in younger patients (sensitivity 95%, specificity 92%). In patients with HCM with and without sarcomeric mutations, the model-derived median probabilities for having HCM were 97% and 96%, respectively. CONCLUSIONS ECG-based detection of HCM by an artificial intelligence algorithm can be achieved with high diagnostic performance, particularly in younger patients. This model requires further refinement and external validation, but it may hold promise for HCM screening. (c) 2020 by the American College of Cardiology Foundation.