Kim Darae, Lee Eunjung, Kim Jihoon, Kim Eun Kyoung, Chang Sung-A, Park Sung-Ji, Choi Jin-Oh, On Young Keun, Attia Zachi, Friedman Paul, Park Kyoung-Min, Oh Jae K
Division of Cardiology, Department of Internal Medicine, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
Eur Heart J Digit Health. 2025 Jul 1;6(4):656-664. doi: 10.1093/ehjdh/ztaf067. eCollection 2025 Jul.
To assess the performance of an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm in identifying patients with moderate to severe aortic stenosis (AS) in an Asian cohort from a tertiary care centre.
We identified a randomly selected patients ≥60 years old who underwent echocardiography and ECG within in 31 days between 2012 and 2021 at the Samsung Medical Center in Korea. Patients with previous cardiac surgery, prosthetic valves, or pacemakers were excluded. The AI-ECG model, originally developed and validated by Mayo Clinic in the USA, was applied without fine-tuning. Performance metrics, including the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy, were calculated to compare AI-ECG predictions with TTE-confirmed AS status. Among 5425 patients, 1095 had moderate to severe AS, and 4330 age- and sex-matched patients without AS were included as controls. The AI-ECG model achieved an AUC of 0.85 (95% CI: 0.84-0.87) in detecting moderate to severe AS. Sensitivity, specificity, PPV, NPV, and accuracy were 0.83, 0.65, 0.37, 0.94, and 68.29%, respectively. The model's performance was consistent across various age and sex subgroups, with sensitivity increasing in older patients.
The AI-ECG model developed in the USA demonstrated comparable performance in detecting moderate to severe AS in an Asian cohort compared with its original validation population. These findings highlight the potential utility of AI-ECG as a non-invasive screening tool for AS across diverse patient populations.
评估一种基于人工智能的心电图（AI-ECG）算法在一家三级医疗中心的亚洲队列中识别中重度主动脉瓣狭窄（AS）患者的性能。
我们从韩国三星医疗中心随机选取了2012年至2021年期间在31天内接受过超声心动图和心电图检查的60岁及以上患者。排除既往有心脏手术、人工瓣膜或起搏器植入史的患者。应用美国梅奥诊所最初开发并验证的AI-ECG模型，未进行微调。计算曲线下面积（AUC）、敏感性、特异性、阳性预测值（PPV）、阴性预测值（NPV）和准确性等性能指标，以比较AI-ECG预测结果与经经胸超声心动图（TTE）确认的AS状态。在5425例患者中，1095例患有中重度AS，4330例年龄和性别匹配的无AS患者作为对照。AI-ECG模型检测中重度AS的AUC为0.85（95%CI：0.84-0.87）。敏感性、特异性、PPV、NPV和准确性分别为0.83、0.65、0.37、0.94和68.29%。该模型在不同年龄和性别亚组中的性能一致，老年患者的敏感性增加。
在美国开发的AI-ECG模型在亚洲队列中检测中重度AS的性能与其原始验证人群相当。这些发现凸显了AI-ECG作为一种针对不同患者群体进行AS无创筛查工具的潜在效用。
