A machine learning model using multiple radiofrequency parameters accurately predicted lesion depth (r²=0.87), length (r²=0.82), and volume (r²=0.86), outperforming conventional metrics.
Does a machine learning model improve the accuracy of lesion metric estimation in radiofrequency catheter ablation compared to conventional parameters?
Machine learning models incorporating multiple ablation parameters provide more accurate estimation of radiofrequency ablation lesion dimensions than conventional single metrics.
Abstract Background Conventional parameters for estimating lesions in radiofrequency catheter ablation (RFCA), such as ablation energy (AE), contact force, and impedance variation, often yield suboptimal results. This study aimed to develop a machine learning model to improve the accuracy of lesion metric estimation in RFCA. Methods RF energies (30-50W) were applied to excised ventricular myocardium using RFCA with contact forces of 10 g or 20 g for durations between 10 and 180 seconds, with various orientations. Correlations between total AE, force-time integral, impedance-drop, and lesion metrics were evaluated and compared to machine learning model predictions, using eXtreme Gradient Boosting (XGBoost). The dataset was split for training (75%) and validation (25%). Feature importance for each lesion metric was also assessed. Results A total of 1,142 ablations were analyzed. Total AE had the strongest correlation with max depth, max length, and volume (r² = 0.63, 0.50, 0.69), followed by force-time integral (r² = 0.54, 0.45, 0.62) and impedance-drop (r² = 0.33, 0.45, 0.31). Impedance drop was most strongly associated with surface area (r² = 0.48). The machine learning model accurately predicted lesion metrics: r² = 0.87 for depth, 0.82 for length, 0.86 for volume, and 0.69 for surface area, with low root mean square error values. Total AE and ablation duration were key predictors, with impedance drop contributing more to surface area and length predictions. Conclusions Machine learning using multiple RFCA parameters improves lesion metric predictions, enhancing lesion estimation beyond conventional metrics, potentially improving procedural guidance and safety.
Takigawa et al. (Thu,) conducted a other in Radiofrequency catheter ablation (n=1,142). Machine learning model (XGBoost) vs. Conventional parameters (total ablation energy, force-time integral, impedance-drop) was evaluated on Accuracy of lesion metric estimation (depth, length, volume, surface area). A machine learning model using multiple radiofrequency parameters accurately predicted lesion depth (r²=0.87), length (r²=0.82), and volume (r²=0.86), outperforming conventional metrics.