Radiofrequency Ablation Size Prediction Model Based On Deep Learning

Atrial fibrillation is the most common arrhythmia in clinical practice,severely affecting patients’ quality of life and even endangering their health.Catheter ablation has become the main treatment method for atrial fibrillation due to its advantages of minimal trauma,short recovery time,and low cost.However,to ensure good treatment outcomes,the ablation lesion must cover the entire target area and maintain a certain safety margin.Therefore,selecting appropriate ablation energy parameters and strategies,and achieving real-time monitoring of the ablation size have become urgent issues in the development of precision medicine for catheter radiofrequency ablation.To achieve real-time and accurate monitoring of radiofrequency ablation lesions,this study constructs a multi-physics coupling tissue simulation model for cardiac radiofrequency ablation,proposes a deep learning-based model for predicting cardiac radiofrequency ablation lesions,and establishes an experimental monitoring system for radiofrequency ablation.The specific research contents are as follows:(1)Theoretical analysis and modeling of the physical fields involved in cardiac radiofrequency ablation are conducted.A cardiac radiofrequency ablation simulation model is constructed using the COMSOL finite element simulation software.Detailed analysis is performed on the ablation parameters and characteristic variables that influence the efficacy of radiofrequency ablation,based on the simulation model’s calculation results.(2)A prediction model for cardiac radiofrequency ablation lesions,based on Long Short-Term Memory(LSTM)neural networks,is proposed.This model can provide real-time predictions of ablation lesion volume based on key parameters such as ablation time,ablation voltage,tissue impedance,contact force,electrode size,and electrode temperature.The model is trained using data from the COMSOL simulation model and tested for prediction accuracy at various time points within a 30-second window to validate its effectiveness.(3)An experimental monitoring system for radiofrequency ablation is established to collect realtime data on electrode temperature,tissue impedance,and contact force,enabling constant force contact between the catheter and myocardial tissue.The radiofrequency ablation lesion prediction model is deployed on an embedded device to achieve real-time prediction of lesion size based on the collected data.In vitro experiments are conducted to validate the collaborative capabilities of the monitoring system and to test the predictive ability of the model during actual ablation processes.Through Transfer learning,the prediction effect of the prediction model is optimized.The research work aims to provide accurate lesion monitoring methods for catheter radiofrequency ablation and further promote the development of precision medicine in this field.