Arrhythmias & electrophysiologyCohort study
Identifying the Presence and Characteristics of Mid-Myocardial and Epicardial Fibrosis From Intracardiac Electrograms in Patients Undergoing Ventricular Arrhythmia Ablation Using a Transformer-Based Self-Supervised Classifier
- retrospective study
- stratified patient-wise split
Authors' conclusion
Our study demonstrates that routine intracardiac electrograms enable the identification of endocardial, mid-myocardial, and epicardial scar using a transformer-based self-supervised deep learning model. Ultimately, our model has the potential to provide magnetic resonance imaging-like fibrosis maps during EP procedures without the need for external imaging and to increase the data set size for patient-specific models in ventricular arrhythmia research.
Study context
- Population
- Patients undergoing ventricular arrhythmia ablationpatients, 131,584 cardiac contact electrogram (EGM) signals · training/validation set 37 · testing set 9
- Participants
- 46 patients
- Exposure
- EGM2Scar-AI vs basic convolutional neural network models, with scar labels from coregistered cardiac magnetic resonance imaging and electroanatomic mapping surface meshes
Main finding
As reported in the abstract
basic convolutional neural network
0.729 (0.725-0.732)
average area under the receiver operating characteristic curve
short-time Fourier transform-based convolutional neural network
0.729 (0.726-0.733)
average area under the receiver operating characteristic curve
EGM2Scar-AI
0.822 (0.819-0.825)
average area under the receiver operating characteristic curve
Observational data: associations, not effects.
Further findings
Performance by scar type
- All models performed better on endocardial and mid-myocardial fibrosis identification, with a modest reduction in performance for epicardial fibrosis
Sensitivity and specificity
- Sensitivity improved significantly with the transformer-based architecture without appreciable changes in specificity
Original publication
Summarised from the published abstract; every number is checked against it