AI-Driven Atrial Fibrillation Management: From Signal to Strategy

Figure 1. AI applications across atrial fibrillation management. Structured overview of five principal domains of AI integration in AF care, organized around a central cardiac anatomical reference. (1) Detection and screening: CNN-based ECG analysis, PPG wearables, and EHR-derived models enable scalable AF identification across clinical and non-clinical settings. (2) Risk stratification: multimodal AI models integrating clinical, electrocardiographic, and laboratory data improve stroke and outcome prediction beyond conventional scoring systems. (3) Cardiac imaging: deep learning architectures automate left atrial segmentation and fibrosis quantification from cardiac MRI, improving reproducibility over manual interpretation. (4) Catheter ablation: AI-guided electroanatomical mapping and real-time signal classification enhance procedural efficiency and post-ablation recurrence prediction. (5) Clinical decision support and follow-up: AI tools support anticoagulation management, rhythm control strategy selection, and wearable-integrated long-term surveillance. CNN, convolutional neural network; DL, deep learning; ECG, electrocardiogram; EGM, electrogram; EHR, electronic health record; LA, left…

Figure 2. Traditional machine learning vs. deep learning pipelines for multimodal AF analysis. Schematic comparison of two computational approaches to multimodal cardiovascular data processing. The upper panel depicts the traditional machine learning pipeline, in which ECG, imaging, and clinical text inputs undergo manual feature engineering followed by feature selection and conventional classification algorithms. The lower panel illustrates the end-to-end deep learning pipeline, in which convolutional spatial encoders and hierarchical sequential modules jointly extract features and perform classification via a multimodal fusion layer and deep decision layers, eliminating the need for manual intermediate steps. CNN, convolutional neural network; ECG, electrocardiogram; KNN, k-nearest neighbors; NLP, natural language processing; SVM, support vector machine.