Screening Tool for Paroxysmal Atrial Fibrillation Based on a Deep-Learning Algorithm Using Printed 12-Lead Electrocardiographic Records during Sinus Rhythm

• Published in: Reviews in cardiovascular medicine Vol. 25; no. 7; p. 242
• Main Authors: Zhou, Yang, Zhang, Deyun, Chen, Yu, Geng, Shijia, Wei, Guodong, Tian, Ying, Shi, Liang, Wang, Yanjiang, Hong, Shenda, Liu, Xingpeng
• Format: Journal Article
• Language: English
• Published: Singapore IMR Press 01.07.2024
• Subjects: artificial intelligence; Original Research; paroxysmal atrial fibrillation; printed electrocardiography; screening tool; sinus rhythm; paroxysmal atrial fibrillation; sinus rhythm; printed electrocardiography; screening tool; artificial intelligence
• ISSN: 1530-6550; 2153-8174; 2153-8174
• DOI: 10.31083/j.rcm2507242

Recent advancements in artificial intelligence (AI) have significantly improved atrial fibrillation (AF) detection using electrocardiography (ECG) data obtained during sinus rhythm (SR). However, the utility of printed ECG (pECG) records for AF detection, particularly in developing countries, remains unexplored. This study aims to assess the efficacy of an AI-based screening tool for paroxysmal AF (PAF) using pECGs during SR. We analyzed 5688 printed 12-lead SR-ECG records from 2192 patients admitted to Beijing Chaoyang Hospital between May 2011 to August 2022. All patients underwent catheter ablation for PAF (AF group) or other electrophysiological procedures (non-AF group). We developed a deep learning model to detect PAF from these printed SR-ECGs. The 2192 patients were randomly assigned to training (1972, 57.3% with PAF), validation (108, 57.4% with PAF), and test datasets (112, 57.1% with PAF). We developed an applet to digitize the printed ECG data and display the results within a few seconds. Our evaluation focused on sensitivity, specificity, accuracy, F1 score, the area under the receiver-operating characteristic curve (AUROC), and precision-recall curves (PRAUC). The PAF detection algorithm demonstrated strong performance: sensitivity 87.5%, specificity 66.7%, accuracy 78.6%, F1 score 0.824, AUROC 0.871 and PRAUC 0.914. A gradient-weighted class activation map (Grad-CAM) revealed the model's tailored focus on different ECG areas for personalized PAF detection. The deep-learning analysis of printed SR-ECG records shows high accuracy in PAF detection, suggesting its potential as a reliable screening tool in real-world clinical practice.