Back

Predicting the When: Multimodal AI for Time-to-Recurrence Analysis After Atrial Fibrillation Ablation

Yin, M.; lai, c.; Yadav, R.; Milstein, J. A.; Thi My Tran, L.; O'Donnell, C.; Schumacher, S.; Cronin, C.; Weinstein, R.; Yamamoto, C.; Ahmad, Z.; Chen, S.; Lefebvre, A.; Ryu, J.; Lacy, A.; Thi Yee, A.; Noh, J.; Kholmovski, E.; Maggioni, M.; Calkins, H.; Spragg, D.; Trayanova, N.

2026-05-15 cardiovascular medicine
10.64898/2026.05.12.26353055 medRxiv
Show abstract

Background: Catheter ablation is the most effective rhythm control strategy for atrial fibrillation (AF); however, recurrence remains common. Current post-ablation management follows largely population-level protocols, constrained by the absence of tools that can anticipate not merely whether, but when, an individual patient will experience recurrence. The emergence of multimodal artificial intelligence (AI) presents a new opportunity to address this unmet clinical need. Objective: To develop a predictive model for time-to-AF-recurrence post-ablation using pre-procedural bi-atrial imaging, clinical covariates, and procedural characteristics, within a novel multimodal AI and survival analysis framework. Methods: We analyzed a retrospective cohort of 437 AF patients who underwent catheter ablation with follow-up censored at 36 months. MARTA-AF (Multimodal AI Recurrence and Time-to-event Analysis post-Ablation in AF) was trained on pre-procedural bi-atrial images, and covariates/procedural characteristics, and integrated into a survival model to generate time-varying recurrence probability estimates. Model interpretability was achieved by quantifying contribution of covariates/procedural characteristics to predicted survival probabilities. Results: MARTA-AF successfully predicted time-varying recurrence risk up to three years post-ablation. Patients were effectively stratified into low- and high-risk groups, with statistically significant discrimination sustained over the follow-up period. The model demonstrated consistent performance across clinically relevant subgroups, including sex, age, and AF type. Incorporation of right atrial shape features improved time-to-AF-recurrence prediction. Interpretability analyses identified key recurrence predictors. Conclusions: MARTA-AF delivers individualized, time-varying AF recurrence risk forecasts and enables stratification into clinically meaningful risk groups. This framework has the potential to transform post- ablation management into a proactive paradigm and to support informed clinical decision-making prior to ablation.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
JACC: Clinical Electrophysiology
11 papers in training set
Top 0.1%
33.7%
2
European Heart Journal - Digital Health
15 papers in training set
Top 0.1%
7.3%
3
Frontiers in Physiology
93 papers in training set
Top 0.5%
6.5%
4
Circulation
66 papers in training set
Top 0.7%
5.0%
50% of probability mass above
5
Scientific Reports
3102 papers in training set
Top 26%
4.4%
6
npj Digital Medicine
97 papers in training set
Top 1%
4.4%
7
PLOS ONE
4510 papers in training set
Top 35%
4.1%
8
Journal of the American Heart Association
119 papers in training set
Top 2%
3.1%
9
Heart Rhythm
22 papers in training set
Top 0.2%
3.1%
10
Nature Communications
4913 papers in training set
Top 48%
1.9%
11
Circulation: Genomic and Precision Medicine
42 papers in training set
Top 0.7%
1.9%
12
Computers in Biology and Medicine
120 papers in training set
Top 2%
1.5%
13
iScience
1063 papers in training set
Top 21%
1.3%
14
eLife
5422 papers in training set
Top 48%
1.3%
15
Journal of the American Medical Informatics Association
61 papers in training set
Top 2%
1.0%
16
The American Journal of Cardiology
15 papers in training set
Top 1%
0.9%
17
Biology Methods and Protocols
53 papers in training set
Top 2%
0.8%
18
PLOS Computational Biology
1633 papers in training set
Top 23%
0.8%
19
Nature Cardiovascular Research
28 papers in training set
Top 0.6%
0.8%
20
Journal of NeuroEngineering and Rehabilitation
28 papers in training set
Top 0.9%
0.8%
21
Clinical Pharmacology & Therapeutics
25 papers in training set
Top 0.7%
0.8%
22
Nature Medicine
117 papers in training set
Top 5%
0.7%
23
Physiological Measurement
12 papers in training set
Top 0.5%
0.7%
24
Heart
10 papers in training set
Top 0.9%
0.7%
25
BMC Cardiovascular Disorders
14 papers in training set
Top 2%
0.7%
26
Computer Methods and Programs in Biomedicine
27 papers in training set
Top 1%
0.7%
27
Medical Image Analysis
33 papers in training set
Top 1%
0.5%
28
Genome Medicine
154 papers in training set
Top 10%
0.5%
29
BMC Medicine
163 papers in training set
Top 9%
0.5%