The Clinical Application of the Deep Learning Technique for Predicting Trigger Origins in Paroxysmal Atrial Fibrillation Patients with Catheter Ablation.
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Abstract
Background – Non-pulmonary vein (NPV) trigger has been reported as an important predictor of recurrence post-atrial fibrillation (AF) ablation. Elimination of NPV triggers can reduce the recurrence of post-ablation AF. Deep learning was applied to pre-ablation pulmonary vein computed tomography (PVCT) geometric slices to create a prediction model for NPV triggers in patients with paroxysmal atrial fibrillation (PAF). Methods – We retrospectively analyzed 521 PAF patients who underwent catheter ablation of PAF. Among them, PVCT geometric slices from 358 non-recurrent AF patients (1-3 mm interspace per slice, 20-200 slices for each patient, ranging from the upper border of the left atrium to the bottom of the heart, for a total of 23683 images of slices) were used in the deep learning process, the ResNet34 of the neural network, to create the prediction model of the NPV trigger. There were 298 (83.2%) patients with only pulmonary vein (PV) triggers and 60 (16.8%) patients with NPV triggers +/- PV triggers. The patients were randomly assigned to either training, validation or test groups and their data was allocated according to those sets. The image datasets were split into training (n=17340), validation (n=3491), and testing (n=2852) groups, which had completely independent sets of patients. Results – The accuracy of prediction in each PVCT image for NPV trigger was up to 82.4±2.0%. The sensitivity and specificity were 64.3±5.4% and 88.4±1.9%, respectively. For each patient, the accuracy of prediction for a NPV trigger was 88.6±2.3%. The sensitivity and specificity were 75.0±5.8% and 95.7±1.8%, respectively. The area under the curve (AUC) for each image and patient were 0.82±0.01 and 0.88±0.07, respectively. Conclusions – The deep learning model using pre-ablation PVCT can be applied to predict the trigger origins in PAF patients receiving catheter ablation. The application of this model may identify patients with a high risk of NPV trigger before ablation.