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Updated: Aug 12, 2026
Direct Pressure Monitoring Accurately Predicts Pulmonary Vein Occlusion During Cryoballoon Ablation
Published on: February 26, 2013
Prediction of difficulty in cryoballoon ablation with a three-dimensional deep learning model using polygonal mesh
Kazutaka Nakasone1, Makoto Nishimori1,2, Masakazu Shinohara2
1Division of Cardiovascular Medicine, Department of Internal Medicine Kobe University Graduate School of Medicine Kobe Hyogo Japan.
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.
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