Artificial Intelligence in Atrial Fibrillation: From Early Detection to Precision Therapy
Abstract
:1. Introduction
2. AI-Powered Electrocardiographic Screening for the Early Detection of AF
3. AI in Wearable Photoplethysmography for Real-Time AF Detection
4. AI-Based Detection of AF in Patients with Implantable Devices
5. AI-Based Prediction of AF Utilizing Clinical Characteristics
6. AI for Risk Stratification in Atrial Fibrillation
7. AI for Personalized AF Management
7.1. Medical Therapy
7.2. Catheter Ablation Therapy
8. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Study (Year) | Patient Population | Sample Size | AI Technique | Findings | Clinical Implications |
---|---|---|---|---|---|
Bahlke et al. (2024) [20] | Patients with long-standing persistent AF | 50 | Volta VX1 software for AI-guided ablation of spatio-temporal dispersions | 82% of patients remained in stable sinus rhythm after an average of 1.46 procedures; 52% experienced arrhythmia recurrence; AF cycle length prolonged significantly; low complication rate. | AI-guided ablation of spatio-temporal dispersions may improve outcomes for persistent AF patients, but randomized trials are needed to confirm long-term efficacy. |
Zou et al. (2024) [76] | Patients undergoing radiofrequency catheter ablation for AF | 118 | CARTOSOUND FAM AI-based ICE module for 3D LA reconstruction | 98% acute ablation success rate; no immediate complications; mean procedure time: 136.9 min; mean RF time: 29.6 min; 92% received PVI, 68% received posterior wall isolation. | AI-integrated ICE module enables accurate LA reconstruction without a multipolar mapping catheter, demonstrating high acute success and safety; long-term AF recurrence needs further study. |
Ogbomo-Harmitt et al. (2022) [77] | Persistent AF patients undergoing simulated RF catheter ablation | 122 | Deep learning (CNN) for predicting success of fibrosis-based and rotor-based ablation | For fibrosis-based ablation: AUC 0.92, recall 0.89, precision 0.82; for rotor-based ablation: AUC 0.77, recall 0.93, precision 0.76; saliency maps identified ablation lesions in 62–71% of cases. | DL-based prediction models can identify proarrhythmogenic regions and improve AI interpretability for AF ablation, potentially aiding clinical decision making. |
Park et al. (2024) [78] | Patients undergoing de novo AF catheter ablation | 5466 | AI-estimated electrocardiographic age (AI-ECG) using ResNet-based model | AI-ECG age gap (≥10 years) associated with higher AF recurrence risk; 5-year recurrence HR 1.44 (95% CI 1.31–1.59); each year increase in AI-ECG age gap increased recurrence risk by 1%. | AI-ECG age gap is a potential predictor of AF recurrence after ablation, offering a simple and interpretable risk marker for clinical use. |
TAILORED-AF Trial (2025) [17] | Patients with drug-refractory persistent or long-standing persistent AF | 374 | AI algorithm detecting spatio-temporal dispersion for tailored ablation | Freedom from AF at 12 months: 88% (tailored arm) vs. 70% (PVI-only arm); freedom from any arrhythmia: no significant difference after one procedure in the entire population, becoming significant after one or two procedures. Significant difference after a single procedure in the >6 months’ persistent AF pre-specified subgroup; tailored ablation had longer procedure and ablation times but similar safety outcomes. | AI-guided ablation targeting spatio-temporal dispersion improves AF elimination compared to PVI alone; long-term efficacy and need for additional AT ablation require further study. |
Gruwez et al. (2024) [79] | Patients undergoing AF ablation with pre-procedure sinus rhythm ECG | 53 | Deep neural network (DNN)-based AI-enabled ECG algorithm | AI-ECG predicted AF recurrence with AUC 0.65; patients classified as high-risk had a 2.6-fold higher risk of AF recurrence (HR 2.6, p = 0.037). | AI-enabled ECG analysis may help predict AF recurrence risk post-ablation, potentially improving patient selection and risk stratification. |
Fox et al. (2024) [80] | Patients undergoing AI-guided arrhythmia mapping for catheter ablation | 28 | Forward-solution AI ECG mapping system | Reduced time to first ablation by 19% (133 vs. 165 min, p = 0.02); reduced procedure duration by 22.6% (233 vs. 301 min, p < 0.001); reduced fluoroscopy time by 43.7% (18.7 vs. 33.2 min, p < 0.001); 6-month arrhythmia-free survival: 73.5% (AI) vs. 63.3% (control, p = 0.56). | AI-guided ECG mapping improves procedural efficiency by reducing mapping time, procedure duration, and radiation exposure without negatively affecting outcomes. |
Asaeikheybari et al. (2024) [81] | Patients undergoing AF catheter ablation with pre-procedure CT scans | 809 | AI-based segmentation and radiomic analysis of pulmonary vein morphology | Primary PV morphology associated with AF recurrence (AUC 0.73, 0.71, 0.70 across datasets); AF+ cases exhibited greater surface complexity; secondary PV features had weaker association (AUC ~0.61). | AI-extracted pulmonary vein features may serve as predictors of AF recurrence post-ablation; potential for improved patient selection and ablation strategies. |
Sato et al. (2024) [82] | Patients with persistent AF undergoing catheter ablation | 497 | Uplift modeling with adaptive boosting to predict benefit from extensive ablation | Uplift score ≥ 0.0124 identified patients who benefited from extensive ablation (HR 0.40, p = 0.015); no benefit observed in patients with uplift score < 0.0124 (HR 1.17, p = 0.661); creatinine, LVEF, BNP, hemoglobin among top predictors. | AI-based uplift modeling can stratify patients who require extensive catheter ablation, offering a precision medicine approach to AF ablation strategies. |
Liu et al. (2020) [83] | Patients with paroxysmal AF undergoing catheter ablation | 521 | Deep learning (ResNet34) for predicting non-pulmonary vein (NPV) triggers | Prediction accuracy for NPV triggers: 88.6%; sensitivity: 75.0%, specificity: 95.7%; AUC for individual images: 0.82, for patients: 0.88. | AI-based prediction of NPV triggers before ablation may enhance procedural planning, helping to reduce AF recurrence and improve ablation strategies. |
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Karakasis, P.; Theofilis, P.; Sagris, M.; Pamporis, K.; Stachteas, P.; Sidiropoulos, G.; Vlachakis, P.K.; Patoulias, D.; Antoniadis, A.P.; Fragakis, N. Artificial Intelligence in Atrial Fibrillation: From Early Detection to Precision Therapy. J. Clin. Med. 2025, 14, 2627. https://doi.org/10.3390/jcm14082627
Karakasis P, Theofilis P, Sagris M, Pamporis K, Stachteas P, Sidiropoulos G, Vlachakis PK, Patoulias D, Antoniadis AP, Fragakis N. Artificial Intelligence in Atrial Fibrillation: From Early Detection to Precision Therapy. Journal of Clinical Medicine. 2025; 14(8):2627. https://doi.org/10.3390/jcm14082627
Chicago/Turabian StyleKarakasis, Paschalis, Panagiotis Theofilis, Marios Sagris, Konstantinos Pamporis, Panagiotis Stachteas, Georgios Sidiropoulos, Panayotis K. Vlachakis, Dimitrios Patoulias, Antonios P. Antoniadis, and Nikolaos Fragakis. 2025. "Artificial Intelligence in Atrial Fibrillation: From Early Detection to Precision Therapy" Journal of Clinical Medicine 14, no. 8: 2627. https://doi.org/10.3390/jcm14082627
APA StyleKarakasis, P., Theofilis, P., Sagris, M., Pamporis, K., Stachteas, P., Sidiropoulos, G., Vlachakis, P. K., Patoulias, D., Antoniadis, A. P., & Fragakis, N. (2025). Artificial Intelligence in Atrial Fibrillation: From Early Detection to Precision Therapy. Journal of Clinical Medicine, 14(8), 2627. https://doi.org/10.3390/jcm14082627