BACKGROUND:Artificial intelligence–enhanced ECG analysis shows promise to detect ventricular dysfunction and remodeling in adult populations. However, its application to pediatric populations remains underexplored.METHODS:A convolutional neural network was trained on paired ECG–echocardiograms (≤2 days apart) from patients ≤18 years of age without major congenital heart disease to detect human expert–classified greater than mild left ventricular (LV) dysfunction, hypertrophy, and dilation (individually and as a composite outcome). Model performance was evaluated on single ECG–echocardiogram pairs per patient at Boston Children’s Hospital and externally at Mount Sinai Hospital using area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC).RESULTS:The training cohort comprised 92 377 ECG–echocardiogram pairs (46 261 patients; median age, 8.2 years). Test groups included internal testing (12 631 patients; median age, 8.8 years; 4.6% composite outcomes), emergency department (2830 patients; median age, 7.7 years; 10.0% composite outcomes), and external validation (5088 patients; median age, 4.3 years; 6.1% composite outcomes) cohorts. Model performance was similar on internal test and emergency department cohorts, with model predictions of LV hypertrophy outperforming the pediatric cardiologist expert benchmark. Adding age and sex to the model added no benefit to model performance. When using quantitative outcome cutoffs, model performance was similar between internal testing (composite outcome: AUROC, 0.88, AUPRC, 0.43; LV dysfunction: AUROC, 0.92, AUPRC, 0.23; LV hypertrophy: AUROC, 0.88, AUPRC, 0.28; LV dilation: AUROC, 0.91, AUPRC, 0.47) and external validation (composite outcome: AUROC, 0.86, AUPRC, 0.39; LV dysfunction: AUROC, 0.94, AUPRC, 0.32; LV hypertrophy: AUROC, 0.84, AUPRC, 0.25; LV dilation: AUROC, 0.87, AUPRC, 0.33), with composite outcome negative predictive values of 99.0% and 99.2%, respectively. Saliency mapping highlighted ECG components that influenced model predictions (precordial QRS complexes for all outcomes; T waves for LV dysfunction). High-risk ECG features include lateral T-wave inversion (LV dysfunction), deep S waves in V1 and V2 and tall R waves in V6 (LV hypertrophy), and tall R waves in V4 through V6 (LV dilation).CONCLUSIONS:This externally validated algorithm shows promise to inexpensively screen for LV dysfunction and remodeling in children, which may facilitate improved access to care by democratizing the expertise of pediatric cardiologists.

中文翻译：

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基于儿科心电图的深度学习预测左心室功能障碍和重构

背景：人工智能增强心电图分析有望检测成年人群的心室功能障碍和重构。然而，其在儿科人群中的应用仍未得到充分探索。 方法：对年龄≤18岁、无严重先天性心脏病患者的配对心电图-超声心动图（相隔≤2天）训练卷积神经网络，以检测人类专家分类的大于轻度的先天性心脏病左心室 (LV) 功能障碍、肥厚和扩张（单独和作为复合结果）。使用接受者操作特征曲线下面积 (AUROC) 和精确回忆曲线下面积 (AUPRC) 对波士顿儿童医院和西奈山医院外部每位患者的单个心电图-超声心动图对进行模型性能评估。 结果：训练队列包括 92 377 组心电图-超声心动图对（46 261 名患者；中位年龄 8.2 岁）。测试组包括内部测试（12631 名患者；中位年龄 8.8 岁；4.6% 综合结果）、急诊科（2830 名患者；中位年龄 7.7 岁；10.0% 综合结果）和外部验证（5088 名患者；中位年龄4.3 年；6.1% 复合结果）队列。模型在内部测试和急诊科队列中的表现相似，模型对左室肥厚的预测优于儿科心脏病专家的基准。在模型中添加年龄和性别对模型性能没有任何好处。当使用定量结果截止值时，内部测试之间的模型性能相似（综合结果：AUROC，0.88，AUPRC，0.43；左室功能障碍：AUROC，0.92，AUPRC，0.23；左室肥厚：AUROC，0.88，AUPRC，0.28；左室扩张： AUROC，0.91，AUPRC，0.47）和外部验证（综合结果：AUROC，0.86，AUPRC，0.39；左室功能障碍：AUROC，0.94，AUPRC，0.32；左室肥厚：AUROC，0.84，AUPRC，0.25；左室扩张：AUROC， 0.87，AUPRC，0.33），复合结果阴性预测值分别为 99.0% 和 99.2%。显着性映射突出显示了影响模型预测的心电图成分（心前区 QRS 波群代表所有结果；T 波代表左心室功能障碍）。高危心电图特征包括横向 T 波倒置（左室功能障碍）、V1 和 V2 中的深 S 波、V6 中的高 R 波（左室肥厚）以及 V4 至 V6 中的高 R 波（左室扩张）。结论：外部验证的算法有望以低廉的成本筛查儿童的左心室功能障碍和重塑，这可能通过使儿科心脏病专家的专业知识民主化来促进改善获得护理的机会。