SOFA: Deep Learning Framework for Simulating and Optimizing Atrial Fibrillation Ablation

Atrial fibrillation (AF) is the most prevalent sustained cardiac arrhythmia, affecting millions globally. Its management has evolved from pharmacological therapy to invasive procedures like catheter ablation, which aims to isolate arrhythmogenic foci within the atria. The success of ablation depends on precise lesion formation, which is influenced by procedural parameters such as ablation site, duration, temperature, power, and contact force. Post-procedure scar assessment via late gadolinium enhancement MRI (LGE-MRI) provides insights into lesion formation, but predicting long-term recurrence remains difficult due to individual anatomical differences and tissue heterogeneity.

Recent developments in deep learning have demonstrated remarkable capabilities in medical image synthesis, segmentation, and disease outcome prediction. Convolutional neural networks (CNNs) have been employed to simulate ablation strategies, while reinforcement learning approaches optimize procedural paths. However, these efforts are often isolated, focusing on either simulation or prediction, without a unified framework that can simulate tissue response, predict recurrence, and optimize parameters simultaneously. This gap hampers the development of personalized, predictive surgical planning tools.

The current work aims to bridge this gap by introducing SOFA, a comprehensive AI framework that integrates image synthesis, outcome prediction, and procedural optimization. This approach leverages recent advances in multi-modal data fusion, generative modeling, and gradient-based optimization to provide a holistic tool for personalized AF ablation planning. The ultimate goal is to improve procedural success rates, reduce recurrence, and facilitate clinical decision-making through a data-driven, automated system.

Despite the widespread use of catheter ablation for AF, its outcomes are highly variable, with recurrence rates up to 30-50%. Traditional approaches rely on operator experience and post-procedural scar assessment, which are insufficient for precise prediction and optimization. The core challenge lies in understanding how procedural parameters influence tissue response and long-term success. Manual selection of parameters is often heuristic, lacking a systematic basis, and current predictive models do not incorporate the direct effects of procedural choices. This results in suboptimal outcomes and high variability.

Furthermore, existing simulation tools are limited in their ability to generate realistic post-ablation images conditioned on specific parameters, and predictive models often ignore procedural context. The need for a unified framework that can simulate tissue response, predict recurrence risk, and automatically optimize parameters tailored to individual patients is urgent. Addressing this problem requires integrating high-fidelity image synthesis, predictive modeling, and differentiable optimization within a single system, enabling preoperative planning and intraoperative guidance.

The primary innovation of SOFA lies in its holistic integration of three key functionalities: 1) high-fidelity simulation of post-ablation scars conditioned on preoperative images and procedural parameters using a cross-attention fusion GAN; 2) accurate prediction of AF recurrence risk from simulated images and multi-view features via a CNN classifier; 3) automated, gradient-based optimization of ablation parameters constrained by region masks, to minimize predicted recurrence.

This approach differs fundamentally from prior work that either focuses solely on image synthesis or outcome prediction. The use of multi-modal fusion with cross-attention mechanisms allows for nuanced integration of imaging and procedural data, capturing complex tissue responses. Multi-view feature aggregation enhances robustness and generalization. The parameter optimization module employs a differentiable loss function with regularization and mask constraints, enabling fine-grained adjustment of continuous variables. This end-to-end pipeline provides a novel, clinically actionable tool for personalized surgical planning, representing a significant step forward in AI-assisted electrophysiology.

• �� Inputs: Pre-ablation LGE-MRI images (Ipre) and procedural parameters (location, duration, temperature, power, force).
• �� Phase 1: Image generation
• Encode Ipre and procedural parameters separately using CNN encoders.
• Compute queries, keys, and values via learnable matrices (Wq, Wk, Wv).
• Apply cross-attention to fuse features, producing a combined feature map.
• Decode fused features to generate a post-ablation scar image (ˆIpost) and scar mask (ˆM) using a convolutional decoder.
• Loss functions include L1 image loss and Dice loss for scar segmentation.
• �� Phase 2: Recurrence prediction
• Extract multi-view features from the fixed fusion module.
• Aggregate features across views via averaging.
• Input aggregated features into a classifier (f(·;ϕ)) trained with binary cross-entropy loss to predict recurrence probability.
• �� Phase 3: Parameter optimization
• Fix the pre-ablation image encoder, input Ipre and candidate Iabl.
• Process combined data through the fusion module to extract features.
• Use gradient descent to update Iabl, minimizing the combined loss: predicted recurrence probability plus regularization.
• Incorporate pre-generated ablation masks to constrain updates to relevant regions.
• Iterate until convergence, producing optimized ablation parameters that lower predicted recurrence risk.

The experimental setup utilized the DECAAF-II dataset, comprising 235 patients with detailed pre- and post-ablation MRI, along with procedural parameters. Data preprocessing involved rigid registration and extraction of six views per patient, resized to 256×256 pixels. Model evaluation employed metrics such as MSE, SSIM, Dice coefficient for image synthesis, and ROC-AUC, accuracy for recurrence prediction. Results showed that SOFA achieved an MSE of 0.018, SSIM of 0.826, and Dice of 0.131, outperforming baseline models. The recurrence prediction AUC was 0.671, with an accuracy of 62.4%. Ablation studies confirmed the effectiveness of multi-modal fusion and multi-view aggregation. The parameter optimization process reduced the predicted recurrence risk from 0.487 to 0.379, demonstrating practical utility. Visual assessments indicated high fidelity in scar simulation across multiple views, with optimized parameters aligning with clinical intuition—longer durations and higher temperatures correlated with lower recurrence risk.

Quantitative results demonstrate the model’s ability to generate realistic post-ablation images with high structural similarity and accurate scar delineation. The recurrence prediction model achieved an AUC of 0.671, surpassing demographic-only models, and correctly identified high-risk patients. The optimization process effectively reduced the predicted recurrence probability by 22.18%, indicating its potential to guide personalized procedures. Ablation studies showed that multi-view fusion and cross-attention mechanisms significantly improved performance, validating the design choices. The visual comparison of pre- and post-optimization parameters revealed clinically plausible adjustments, such as increased ablation duration and temperature in key regions, supporting the model’s interpretability and practical relevance.

This framework can be integrated into preoperative planning workflows, allowing clinicians to simulate various ablation strategies and select optimal parameters tailored to individual patient anatomy and tissue response. It can serve as a decision-support tool for personalized therapy, reducing trial-and-error approaches. Additionally, the model can be used for training purposes, simulating realistic scenarios for electrophysiologists. In the future, real-time intraoperative adaptation could be enabled by embedding the system into surgical navigation platforms, providing dynamic guidance. Long-term, this approach could facilitate fully automated, AI-driven electrophysiology procedures, significantly improving success rates and reducing recurrence in AF patients.

The current model is primarily validated on 2D views, lacking full 3D spatial modeling of the atrium, which may limit the accuracy of scar boundary delineation. The dataset size, although substantial, remains limited for diverse populations, necessitating larger, multi-center datasets for robust generalization. The optimization relies on predefined masks, which may oversimplify complex tissue responses. Clinical validation through prospective trials is still pending, and real-time deployment requires further optimization for computational efficiency. Additionally, interpretability of the deep learning model remains a challenge, requiring future efforts to enhance transparency and clinician trust.