AI-BASED REAL-TIME CLOSED-LOOP CATHETER GUIDANCE FRAMEWORK FOR ADAPTIVE RF ABLATION IN ARRHYTHMIA MANAGEMENT

Abstract

Radiofrequency (RF) catheter ablation is a well-established therapy for atrial and ventricular arrhythmias; however, procedural variability, incomplete lesion formation, and recurrence remain major limitations. Although artificial intelligence (AI) has been extensively applied in arrhythmia detection and post-procedural risk prediction, its integration into real-time interventional guidance remains limited. This study proposes a novel AI-based real-time catheter guidance framework designed to optimize lesion formation, catheter stability, and substrate targeting during RF ablation. The system integrates intracardiac electrograms (EGMs), contact force sensing, impedance monitoring, and 3D electro anatomical mapping into a hybrid deep learning architecture combining convolutional neural networks (CNN), long short term memory (LSTM) networks, and reinforcement learning (RL). The model dynamically predicts lesion adequacy, identifies catheter instability, and adjusts energy delivery parameters in real time. Validation using retrospective multi-procedure RF datasets and simulated real-time deployment demonstrated improvements in predicted lesion completeness (23%), catheter stability detection sensitivity (91%), and arrhythmogenic gap reduction compared to conventional guidance. The proposed framework introduces a translational biomedical engineering approach toward intelligent electrophysiology laboratories and semi-autonomous ablation systems.