Explainable paroxysmal atrial fibrillation diagnosis using an artificial intelligence-enabled electrocardiogram

• Published in: The Korean journal of internal medicine Vol. 40; no. 2; pp. 251 - 261
• Main Authors: Jin, Yeongbong, Ko, Bonggyun, Chang, Woojin, Choi, Kang-Ho, Lee, Ki Hong
• Format: Journal Article
• Language: English
• Published: Korea (South) Korean Association of Internal Medicine 01.03.2025; The Korean Association of Internal Medicine; 대한내과학회
• Subjects: Action Potentials; Aged; Artificial Intelligence; atrial fibrillation; Atrial Fibrillation - diagnosis; Atrial Fibrillation - physiopathology; Deep Learning; Early Diagnosis; Electrocardiography; Female; Humans; Male; Middle Aged; Neural Networks, Computer; Original; paroxysmal atrial fibrillation; Predictive Value of Tests; 내과학; Deep learning; Electrocardiography; Paroxysmal atrial fibrillation; Artificial intelligence; Atrial fibrillation
• ISSN: 1226-3303; 2005-6648; 2005-6648
• DOI: 10.3904/kjim.2024.130

Background/Aims: Atrial fibrillation (AF) significantly contributes to global morbidity and mortality. Paroxysmal atrial fibrillation (PAF) is particularly common among patients with cryptogenic strokes or transient ischemic attacks and has a silent nature. This study aims to develop reliable artificial intelligence (AI) algorithms to detect early signs of AF in patients with normal sinus rhythm (NSR) using a 12-lead electrocardiogram (ECG).Methods: Between 2013 and 2020, 552,372 ECG traces from 318,321 patients were collected and split into training (n = 331,422), validation (n = 110,475), and test sets (n = 110,475). Deep neural networks were then trained to predict AF onset within one month of NSR. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). An explainable AI technique was employed to identify the inference evidence underlying the predictions of deep learning models.Results: The AUROC for early diagnosis of PAF was 0.905 ± 0.007. The findings reveal that the vicinity of the T wave, including the ST segment and S-peak, significantly influences the ability of the trained neural network to diagnose PAF. Additionally, comparing the summarized ECG in NSR with those in PAF revealed that nonspecific ST-T abnormalities and inverted T waves were associated with PAF.Conclusions: Deep learning can predict AF onset from NSR while detecting key features that influence decisions. This suggests that identifying undetected AF may serve as a predictive tool for PAF screening, offering valuable insights into cardiac dysfunction and stroke risk.