Lin Wen-Yu, Lin Chin, Liu Wen-Cheng, Liu Wei-Ting, Chang Chiao-Hsiang, Chen Hung-Yi, Lee Chiao-Chin, Chen Yu-Cheng, Wu Chen-Shu, Lee Chia-Cheng, Wang Chih-Hung, Liao Chun-Cheng, Lin Chin-Sheng
Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center Taipei, Taiwan, R.O.C.
Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan, R.O.C.
J Med Syst. 2025 Apr 22;49(1):51. doi: 10.1007/s10916-025-02177-0.
Arrhythmias are common and can affect individuals with or without structural heart disease. Deep learning models (DLMs) have shown the ability to recognize arrhythmias using 12-lead electrocardiograms (ECGs). However, the limited types of arrhythmias and dataset robustness have hindered widespread adoption. This study aimed to develop a DLM capable of detecting various arrhythmias across diverse datasets. This algorithm development study utilized 22,130 ECGs, divided into development, tuning, validation, and competition sets. External validation was conducted on three open datasets (CODE-test, PTB-XL, CPSC2018) comprising 32,495 ECGs. The study also assessed the long-term risks of new-onset atrial fibrillation (AF), heart failure (HF), and mortality in individuals with false-positive AF detection by the DLM. In the validation set, the DLM achieved area under the receiver operating characteristic curve above 0.97 and sensitivity/specificity exceeding 90% across most arrhythmia classes. It demonstrated cardiologist-level performance, ranking first in balanced accuracy in a human-machine competition. External validation confirmed comparable performance. Individuals with false-positive AF detection had a significantly higher risk of new-onset AF (hazard ration [HR]: 1.69, 95% confidence interval [CI]: 1.11-2.59), HF (HR: 1.73, 95% CI: 1.20-2.51), and mortality (HR: 1.40, 95% CI: 1.02-1.92) compared to true-negative individuals after adjusting for age and sex. We developed an accurate DLM capable of detecting 23 cardiac arrhythmias across multiple datasets. This DLM serves as a valuable screening tool to aid physicians in identifying high-risk patients, with potential implications for early intervention and risk stratification.
心律失常很常见，可影响有或没有结构性心脏病的个体。深度学习模型（DLM）已显示出使用12导联心电图（ECG）识别心律失常的能力。然而，心律失常类型有限以及数据集的稳健性阻碍了其广泛应用。本研究旨在开发一种能够在不同数据集中检测各种心律失常的DLM。这项算法开发研究使用了22,130份心电图，分为开发集、调整集、验证集和竞赛集。在包含32,495份心电图的三个开放数据集（CODE - test、PTB - XL、CPSC2018）上进行了外部验证。该研究还评估了DLM检测到房颤（AF）假阳性的个体中新发房颤、心力衰竭（HF）和死亡的长期风险。在验证集中，DLM在大多数心律失常类别中，受试者工作特征曲线下面积超过0.97，敏感性/特异性超过90%。它表现出心脏病专家级别的性能，在人机竞赛的平衡准确性方面排名第一。外部验证证实了类似的性能。在调整年龄和性别后，与真阴性个体相比，DLM检测到房颤假阳性的个体中新发房颤（风险比[HR]：1.69，95%置信区间[CI]：1.11 - 2.59）、HF（HR：1.73，95% CI：1.20 - 2.51）和死亡（HR：1.40，95% CI：1.02 - 1.92）的风险显著更高。我们开发了一种准确的DLM，能够在多个数据集中检测23种心律失常。这种DLM作为一种有价值的筛查工具，有助于医生识别高危患者，对早期干预和风险分层具有潜在意义。
