用于Brugada综合征的合成心电图：从数据生成到心脏病专家评估
Synthetic electrocardiograms for Brugada syndrome: from data generation to expert cardiologists evaluation.
作者信息
Zanchi Beatrice, Monachino Giuliana, Faraci Francesca Dalia, Metaldi Matteo, Brugada Pedro, Sarquella-Brugada Georgia, Behr Elijah R, Brugada Josep, Crotti Lia, Belhassen Bernard, Conte Giulio
机构信息
Department of Innovative Technologies, Institute of Digital Technologies for Personalized Healthcare of SUPSI, Lugano, Switzerland.
Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
出版信息
Eur Heart J Digit Health. 2025 Apr 24;6(4):683-687. doi: 10.1093/ehjdh/ztaf039. eCollection 2025 Jul.
AIMS
Synthetic electrocardiograms (ECGs) for inherited cardiac diseases may overcome the issue related to data scarcity for artificial intelligence (AI)-based algorithms. This study aimed to evaluate experienced cardiologists' ability to differentiate synthetic and real Brugada ECGs.
METHODS AND RESULTS
A total of 2244 ECG instances (50% synthetic generated by a generative adversarial network, 50% real Brugada patients' ECGs) were evaluated by 7 cardiologists, each with >15 years of experience. All ECGs were standard 12-lead recordings acquired with identical settings (paper speed 25 mm/s, amplitude 10 mm/mV) and randomly assigned without identifying markers. The examination was blinded and conducted in 2 rounds with at least 2 h gap between rounds to assess potential learning effects and intra-rater reliability. Each physician classified the recordings as 'real' or 'synthetic' without having any additional information. Performance metrics, including accuracy, sensitivity, specificity, and intra-rater reliability (Cohen's Kappa), were analyzed. Brugada syndrome (BrS) specialists' repeated evaluations were characterized by low accuracy (first round 40%, second round 42%), specificity (first round 22%, second round 26%) and sensitivity (first round 58%, second round 58%). Intra-rater reliability varied widely (Cohen's Kappa: -0.12 to 0.80).
CONCLUSION
Synthetic Brugada ECGs cannot be adequately distinguished from real patients' ECGs by BrS specialists.
目的
用于遗传性心脏病的合成心电图（ECG）可能会克服基于人工智能（AI）算法的数据稀缺问题。本研究旨在评估经验丰富的心脏病专家区分合成和真实Brugada心电图的能力。
方法与结果
7位经验超过15年的心脏病专家对总共2244例心电图实例（50%由生成对抗网络生成的合成心电图，50%为真实Brugada患者的心电图）进行了评估。所有心电图均为在相同设置（纸速25 mm/s，幅度10 mm/mV）下采集的标准12导联记录，并在不带有识别标记的情况下随机分配。检查是盲法进行的，分两轮进行，两轮之间至少间隔2小时，以评估潜在的学习效果和评分者内信度。每位医生在没有任何额外信息的情况下将记录分类为“真实”或“合成”。分析了包括准确性、敏感性、特异性和评分者内信度（Cohen's Kappa）在内的性能指标。Brugada综合征（BrS）专家的重复评估表现为准确性较低（第一轮40%，第二轮42%）、特异性较低（第一轮22%，第二轮26%）和敏感性较低（第一轮58%，第二轮58%）。评分者内信度差异很大（Cohen's Kappa：-0.12至0.80）。
结论
BrS专家无法充分区分合成的Brugada心电图和真实患者的心电图。
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