The efficacy of convolutional neural network (CNN)-enhanced electrocardiography (ECG) in detecting hypertrophic cardiomyopathy (HCM) and dilated HCM (dHCM) remains uncertain in real-world applications. This retrospective study analyzed data from 19,170 patients (including 140 HCM or dHCM) in the Shinken Database (2010–2017). We evaluated the sensitivity, positive predictive rate (PPR), and F1 score of CNN-enhanced ECG in a ‘‘basic diagnosis’’ model (total disease label) and a ‘‘comprehensive diagnosis’’ model (including disease subtypes). Using all-lead ECG in the "basic diagnosis" model, we observed a sensitivity of 76%, PPR of 2.9%, and F1 score of 0.056. These metrics improved in cases with a diagnostic probability of ≥ 0.9 and left ventricular hypertrophy (LVH) on ECG: 100% sensitivity, 8.6% PPR, and 0.158 F1 score. The ‘‘comprehensive diagnosis’’ model further enhanced these figures to 100%, 13.0%, and 0.230, respectively. Performance was broadly consistent across CNN models using different lead configurations, particularly when including leads viewing the lateral walls. While the precision of CNN models in detecting HCM or dHCM in real-world settings is initially low, it improves by targeting specific patient groups and integrating disease subtype models. The use of ECGs with fewer leads, especially those involving the lateral walls, appears comparably effective.

卷积神经网络 (CNN) 增强心电图 (ECG) 在检测肥厚型心肌病 (HCM) 和扩张型 HCM (dHCM) 方面的功效在实际应用中仍不确定。这项回顾性研究分析了 Shinken 数据库（2010-2017 年）中 19,170 名患者（包括 140 名 HCM 或 dHCM）的数据。我们在“基本诊断”模型（总疾病标签）和“综合诊断”模型（包括疾病亚型）中评估了 CNN 增强心电图的敏感性、阳性预测率 (PPR) 和 F1 评分。在“基本诊断”模型中使用全导联心电图，我们观察到敏感性为 76%，PPR 为 2.9%，F1 评分为 0.056。在 ECG 诊断概率≥ 0.9 且左心室肥厚 (LVH) 的病例中，这些指标有所改善：敏感性 100%，PPR 8.6%，F1 评分 0.158。 “综合诊断”模型进一步将这些数字进一步提高到100%、13.0%和0.230。使用不同引线配置的 CNN 模型的性能基本一致，特别是当包括观察侧壁的引线时。虽然 CNN 模型在现实环境中检测 HCM 或 dHCM 的精度最初较低，但通过针对特定患者群体和整合疾病亚型模型，精度有所提高。使用较少导联的心电图，尤其是涉及侧壁的心电图，似乎相当有效。