AI-enabled ECGs have previously been shown to accurately predict patient sex in adults and correlate with sex hormone levels. We aimed to test the ability of AI-enabled ECGs to predict sex in the pediatric population and study the influence of pubertal development. AI-enabled ECG models were created using a convolutional neural network trained on pediatric 10-second, 12-lead ECGs. The first model was trained de novo using pediatric data. The second model used transfer learning from a previously validated adult data-derived algorithm. We analyzed the first ECG from 90,133 unique pediatric patients (aged ≤18 years) recorded between 1987–2022, and divided the cohort into training, validation, and testing datasets. Subgroup analysis was performed on prepubertal (0–7 years), peripubertal (8–14 years), and postpubertal (15–18 years) patients. The cohort was 46.7% male, with 21,678 prepubertal, 26,740 peripubertal, and 41,715 postpubertal children. The de novo pediatric model demonstrated 81% accuracy and an area under the curve (AUC) of 0.91. Model sensitivity was 0.79, specificity was 0.83, positive predicted value was 0.84, and the negative predicted value was 0.78, for the entire test cohort. The model’s discriminatory ability was highest in postpubertal (AUC = 0.98), lower in the peripubertal age group (AUC = 0.91), and poor in the prepubertal age group (AUC = 0.67). There was no significant performance difference observed between the transfer learning and de novo models. AI-enabled interpretation of ECG can estimate sex in peripubertal and postpubertal children with high accuracy.

此前，人工智能心电图已被证明可以准确预测成人患者性别，并与性激素水平相关。我们的目的是测试人工智能心电图预测儿科人群性别的能力，并研究青春期发育的影响。支持 AI 的心电图模型是使用在儿科 10 秒 12 导联心电图上训练的卷积神经网络创建的。第一个模型是使用儿科数据从头训练的。第二个模型使用了先前验证的成人数据派生算法的迁移学习。我们分析了 1987 年至 2022 年间记录的 90,133 名独特儿科患者（年龄≤18 岁）的第一份心电图，并将该队列分为训练、验证和测试数据集。对青春期前（0-7岁）、青春期前后（8-14岁）和青春期后（15-18岁）患者进行亚组分析。该队列中男性占 46.7%，其中包括 21,678 名青春期前儿童、26,740 名青春期前后儿童和 41,715 名青春期后儿童。 de novo 儿科模型的准确度为 81%，曲线下面积 (AUC) 为 0.91。对于整个测试队列，模型敏感性为 0.79，特异性为 0.83，阳性预测值为 0.84，阴性预测值为 0.78。该模型的辨别能力在青春期后最高（AUC = 0.98），在青春期周围年龄组较低（AUC = 0.91），在青春期前年龄组较差（AUC = 0.67）。迁移学习模型和从头模型之间没有观察到显着的性能差异。基于人工智能的心电图解读可以高精度地估计青春期前后和青春期后儿童的性别。