Machine Learning, Artificial Intelligence and Biophysical Modeling in Cardiac Ablation: Optimizing RFA, Cryoablation, and PFA Outcomes

Catheter ablation remains a cornerstone in the rhythm control strategy for cardiac arrhythmias, yet the therapeutic landscape is undergoing an unprecedented evolution. For decades, thermal modalities, Radiofrequency Ablation (RFA), driven by resistive heating, and Cryoablation, utilizing therapeutic freezing, have served as clinical benchmarks. While highly refined, their efficacy is constrained by thermal propagation dynamics, contact-force dependencies, and the perpetual risk of collateral tissue injury.The rapid clinical translation of Pulsed-Field Ablation (PFA), a non-thermal modality leveraging ultra-short, high-voltage electrical fields to induce tissue-selective irreversible electroporation, has introduced a fundamental paradigm shift. However, maximizing the potential of this multi-energy ecosystem introduces highly complex biophysical challenges. Electrophysiologists must now navigate vastly different lesion kinetics, ranging from thermal diffusion and tissue desiccation in RFA to local electric field gradients and dynamic impedance shifts in PFA.To unify these disparate energy modalities and move toward truly personalized interventions, traditional empirical workflows are no longer sufficient. Biophysical and computational science, spanning mechanistic and statistical modeling, signal processing, machine learning, and artificial intelligence, represents the critical bridge needed to transform intraprocedural data into actionable, real-time insights. Much of this progress depends on cardiac imaging, computed tomography (CT), magnetic resonance imaging (MRI), and echocardiography, which provide the structural and functional data that machine learning and AI models rely on for training and validation. By deploying these approaches to model biophysical interactions, analyze high-density electroanatomical mapping, and process intracardiac and imaging-derived signals, the field can shift from a standardized one-size-fits-all approach to highly individualized, predictive lesion engineering. Preclinical and animal model studies remain essential to this effort, providing the controlled conditions needed to validate novel energy delivery strategies and computational models before clinical translation.The objective of this Research Topic is to gather high-impact, translational and preclinical research at the intersection of biophysical science, cardiac imaging, and interventional electrophysiology. This collection aims to showcase how mechanistic modeling, computational simulation, machine learning, artificial intelligence, and multimodal imaging can optimize energy delivery, characterize patient-specific biophysical interactions, and provide precise, predictive modeling of acute and chronic lesion formation across RFA, cryoablation, and PFA. Ultimately, this collection will serve as a definitive guide for leveraging biophysical, computational, and imaging science to achieve safer, more durable, and highly customized arrhythmia treatment outcomes.We welcome Original Research, comprehensive Reviews, and methodological papers exploring biophysical modeling, computational simulation, machine learning, artificial intelligence, and multimodal cardiac imaging applications across all ablation modalities, including preclinical and animal studies validating multi-energy strategies and predictive models. Topic editors: Guglielmo Gallone, Ting-Yung Chang, Shin-Huei Liu Source: https://www.frontiersin.org/research-topics/84378/machine-learning-artificial-intelligence-and-biophysical-modeling-in-cardiac-ablation-optimizing-rfa-cryoablation-and-pfa-outcomes