Prediction of difficulty in cryoballoon ablation with a three-dimensional deep learning model using polygonal mesh representation

Background

Cryoballoon ablation (CBA) is useful for pulmonary vein (PV) isolation. However, some cases are challenging, requiring multiple applications and/or touch-up ablations. Although several predictors of CBA difficulty have been reported, none have assessed the spatial location and morphology of the left atrium and PVs. This study aimed to develop a three-dimensional (3D) deep learning (DL) model to predict CBA difficulty and compare its accuracy with conventional manual measurement.

Methods

A 28-mm cryoballoon (Arctic Front Advance, Medtronic) was used in all cases. CBA difficulty was defined as requiring touch-up ablation and/or more than three applications per PV. We developed a DL model that can learn polygonal meshes and predict CBA difficulty. In the conventional method, predictors included a thinner left lateral ridge, higher left superior PV (LSPV) ovality index, longer LSPV ostium-bifurcation distance, and shorter right inferior PV ostium-bifurcation distance.

Results

A total of 189 patients who underwent CBA for drug-resistant atrial fibrillation between January 2015 and January 2022 were included. The DL model was superior to the conventional method in accuracy (0.793 vs. 0.630, p = .042) and specificity (0.796 vs. 0.609, p = .022), with the AUC-ROC of 0.821.

Conclusions

We developed a 3D DL model that can detect CBA difficulty using a polygonal mesh representation. By predicting difficult cases in advance, strategies can be developed to increase success rates.