Utilising Artificial Intelligence to Identify Ventricular Tachycardia Ablation Targets in Sinus Rhythm

Background and Aims

Machine learning has shown potential in predicting ablation targets for ventricular tachycardia (VT) in an animal model. This study progresses to externally validating deep learning approaches for human data.

Methods

The development and external validation dataset included 21 and 13 patients, respectively, with structural VT undergoing catheter ablation. In the development datasets, electrophysiological studies were conducted using the Advisor ^TM HD grid (Ensite ^TM X), while both CARTO and Ensite Precision were used in the validation dataset.

In each patient, VT ablation targets were defined as mapping points within 8 mm of VT isthmuses. Three advanced machine learning models were trained using cardiac mapping data acquired in both omnipolar and unipolar configurations during sinus rhythm and ventricular pacing. Discrimination was evaluated using nested leave-one-out cross-validation at patient level.

Results

Overall, graph convolutional networks (GCNs), which integrate intracardiac signal waveforms with three-dimensional electroanatomical geometries, achieved the highest performance, with optimal results obtained from unipolar electrograms acquired in sinus rhythm (median AUC 0.793, sensitivity 83.6%, specificity 69.0%). This may be partly explained by the inclusion of repolarization dynamics in unipolar electrograms and the higher point density of sinus rhythm maps. Comparable performance was observed in the external dataset.

Conclusion

This study demonstrates that graph convolutional networks applied to sinus rhythm EGM waveforms collected during substrate mapping can localise critical components of VT re-entry circuits. This approach has potential to provide fast and accurate ablation guidance without the need to induce and map VT, improving safety and efficacy of VT catheter ablation.

Structured Graphical Abstract

Utilising artificial intelligence approaches to identify ventricular tachycardia ablation targets through electrophysiological maps in sinus rhythm.