Haq Ikram U, Liu Kan, Giudicessi John R, Siontis Konstantinos C, Asirvatham Samuel J, Attia Zachi I, Ackerman Michael J, Friedman Paul A, Killu Ammar M
Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
Eur Heart J Digit Health. 2023 Dec 9;5(2):192-194. doi: 10.1093/ehjdh/ztad078. eCollection 2024 Mar.
ECG abnormalities are often the first signs of arrhythmogenic right ventricular cardiomyopathy (ARVC) and we hypothesized that an artificial intelligence (AI)-enhanced ECG could help identify patients with ARVC and serve as a valuable disease-detection tool.
We created a convolutional neural network to detect ARVC using a 12-lead ECG. All patients with ARVC who met the 2010 task force criteria and had disease-causative genetic variants were included. All case ECGs were randomly assigned in an 8:1:1 ratio into training, validation, and testing groups. The case ECGs were age- and sex-matched with control ECGs at our institution in a 1:100 ratio. Seventy-seven patients (51% male; mean age 47.2 ± 19.9), including 56 patients with PKP2, 7 with DSG2, 6 with DSC2, 6 with DSP, and 2 with JUP were included. The model was trained using 61 case ECGs and 5009 control ECGs; validated with 7 case ECGs and 678 control ECGs and tested in 22 case ECGs and 1256 control ECGs. The sensitivity, specificity, positive and negative predictive values of the model were 77.3, 62.9, 3.32, and 99.4%, respectively. The area under the curve for rhythm ECG and median beat ECG was 0.75 and 0.76, respectively.
Our study found that the model performed well in excluding ARVC and supports the concept that the AI ECG can serve as a biomarker for ARVC if a larger cohort were available for network training. A multicentre study including patients with ARVC from other centres would be the next step in refining, testing, and validating this algorithm.
心电图异常通常是致心律失常性右室心肌病（ARVC）的首发症状，我们推测人工智能（AI）增强的心电图有助于识别ARVC患者，并可作为一种有价值的疾病检测工具。
我们创建了一个卷积神经网络，用于使用12导联心电图检测ARVC。纳入所有符合2010年工作组标准且具有致病基因变异的ARVC患者。所有病例心电图以8:1:1的比例随机分配到训练、验证和测试组。病例心电图与我们机构的对照心电图按1:100的比例进行年龄和性别匹配。共纳入77例患者（男性占51%；平均年龄47.2±19.9岁），其中56例携带PKP2基因，7例携带DSG2基因，6例携带DSC2基因，6例携带DSP基因，2例携带JUP基因。该模型使用61例病例心电图和5009例对照心电图进行训练；用7例病例心电图和678例对照心电图进行验证，并在22例病例心电图和1256例对照心电图上进行测试。该模型的敏感性、特异性、阳性预测值和阴性预测值分别为77.3%、62.9%、3.32%和99.4%。节律心电图和中位搏动心电图的曲线下面积分别为0.75和0.76。
我们的研究发现该模型在排除ARVC方面表现良好，并支持这样一种观点即如果有更大的队列用于网络训练，AI心电图可作为ARVC的生物标志物。下一步将开展一项多中心研究，纳入来自其他中心的ARVC患者，以完善、测试和验证该算法。
