Background

The electrocardiogram (ECG) is one of the most accessible and comprehensive diagnostic tools used to assess cardiac patients at the first point of contact. Despite advances in computerized interpretation of the electrocardiogram (CIE), its accuracy remains inferior to physicians. This study evaluated the diagnostic performance of an artificial intelligence (AI)-powered ECG system and compared its performance to current state-of-the-art CIE.

Methods

An AI-powered system consisting of 6 deep neural networks (DNN) was trained on standard 12‑lead ECGs to detect 20 essential diagnostic patterns (grouped into 6 categories: rhythm, acute coronary syndrome (ACS), conduction abnormalities, ectopy, chamber enlargement and axis). An independent test set of ECGs with diagnostic consensus of two expert cardiologists was used as a reference standard. AI system performance was compared to current state-of-the-art CIE. The key metrics used to compare performances were sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score.

Results

A total of 932,711 standard 12‑lead ECGs from 173,949 patients were used for AI system development. The independent test set pooled 11,932 annotated ECG labels. In all 6 diagnostic categories, the DNNs achieved high F1 scores: Rhythm 0.957, ACS 0.925, Conduction abnormalities 0.893, Ectopy 0.966, Chamber enlargement 0.972, and Axis 0.897. The diagnostic performance of DNNs surpassed state-of-the-art CIE for the 13 out of 20 essential diagnostic patterns and was non-inferior for the remaining individual diagnoses.

Conclusions

Our results demonstrate the AI-powered ECG model's ability to accurately identify electrocardiographic abnormalities from the 12‑lead ECG, highlighting its potential as a clinical tool for healthcare professionals.

中文翻译：

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验证用于 12 导联心电图解读的自动化人工智能系统

背景

心电图 (ECG) 是用于第一时间评估心脏病患者的最方便、最全面的诊断工具之一。尽管心电图计算机化解读 (CIE) 取得了进步，但其准确性仍然不如医生。这项研究评估了人工智能 (AI) 驱动的心电图系统的诊断性能，并将其性能与当前最先进的 CIE 进行了比较。

方法

由 6 个深度神经网络 (DNN) 组成的人工智能系统接受了标准 12 导联心电图的训练，可检测 20 种基本诊断模式（分为 6 类：心律、急性冠状动脉综合征 (ACS)、传导异常、异位、心室扩大和轴）。使用由两位心脏病专家达成诊断共识的独立心电图测试集作为参考标准。AI 系统性能与当前最先进的 CIE 进行了比较。用于比较性能的关键指标是敏感性、特异性、阳性预测值 (PPV)、阴性预测值 (NPV) 和 F1 评分。

结果

来自 173,949 名患者的总共 932,711 个标准 12 导联心电图用于 AI 系统开发。独立测试集汇集了 11,932 个带注释的心电图标签。在所有 6 个诊断类别中，DNN 均取得了较高的 F1 分数：节律 0.957、ACS 0.925、传导异常 0.893、异位 0.966、腔室扩大 0.972 和轴 0.897。DNN 的诊断性能在 20 种基本诊断模式中的 13 种中超过了最先进的 CIE，并且对于其余的个体诊断而言也不逊色。

结论

我们的结果证明，人工智能驱动的心电图模型能够准确识别 12 导联心电图的心电图异常，突显了其作为医疗保健专业人员的临床工具的潜力。