Ischaemic heart disease is among the most frequent causes of death. Early detection of myocardial pathologies can increase the benefit of therapy and reduce the number of lethal cases. Presence of myocardial scar is an indicator for developing ischaemic heart disease and can be detected with high diagnostic precision by magnetic resonance imaging. However, magnetic resonance imaging scanners are expensive and of limited availability. It is known that presence of myocardial scar has an impact on the well-established, reasonably low cost, and almost ubiquitously available electrocardiogram. However, this impact is non-specific and often hard to detect by a physician. We present an artificial intelligence based approach - namely a deep learning model - for the prediction of myocardial scar based on an electrocardiogram and additional clinical parameters. The model was trained and evaluated by applying 6-fold cross-validation to a dataset of 12-lead electrocardiogram time series together with clinical parameters. The proposed model for predicting the presence of scar tissue achieved an area under the curve score, sensitivity, specificity, and accuracy of 0.89, 70.0, 84.3, and 78.0%, respectively. This promisingly high diagnostic precision of our electrocardiogram-based deep learning models for myocardial scar detection may support a novel, comprehensible screening method.

中文翻译：

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使用心电图数据和深度神经网络检测心肌疤痕


缺血性心脏病是最常见的死亡原因之一。早期发现心肌病变可以增加治疗效果并减少致命病例的数量。心肌疤痕的存在是发生缺血性心脏病的指标，并且可以通过磁共振成像以高诊断精度检测到。然而，磁共振成像扫描仪价格昂贵且可用性有限。众所周知，心肌疤痕的存在会对完善的、相当低的成本且几乎无处不在的心电图产生影响。然而，这种影响是非特异性的，并且通常很难被医生发现。我们提出了一种基于人工智能的方法，即深度学习模型，用于根据心电图和其他临床参数预测心肌疤痕。通过对 12 导联心电图时间序列和临床参数的数据集应用 6 倍交叉验证来训练和评估模型。所提出的预测疤痕组织存在的模型的曲线下面积评分、敏感性、特异性和准确性分别为 0.89、70.0、84.3 和 78.0%。我们基于心电图的深度学习模型用于心肌疤痕检测的这种令人鼓舞的高诊断精度可能支持一种新颖的、易于理解的筛查方法。