Validation of a machine learning algorithm to identify pulmonary vein isolation during ablation procedures for the treatment of atrial fibrillation: results of the PVISION study.

Jan De Pooter Liesbeth Timmers Serge Boveda Stephane Combes Sebastian Knecht Alexandre Almorad Carlos De Asmundis Mattias Duytschaever

Published in: Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology (2024)

This study validated an automated algorithm using machine learning to assess the isolation status of pulmonary veins in patients undergoing PVI with different ablation modalities. The algorithm reached an AUC of 92%, with both sensitivity and specificity exceeding the primary study endpoints.

Keyphrases

machine learning
patients undergoing
atrial fibrillation
deep learning
heart failure
venous thromboembolism
left ventricular
neural network
combination therapy
left atrial appendage
direct oral anticoagulants