Chih-Min Liu1,2,3, Shih-Lin Chang1,2,3, Hung-Hsun Chen4,5, Wei-Shiang Chen6, Yenn-Jiang Lin1,2,3, Li-Wei Lo1,2,3, Yu-Feng Hu1,2,3, Fa-Po Chung1,2,3, Tze-Fan Chao1,2,3, Ta-Chuan Tuan1,2,3, Jo-Nan Liao1,2,3, Chin-Yu Lin1,2,3, Ting-Yung Chang1,2,3, Cheng-I Wu1,2, Ling Kuo1,2,3, Mei-Han Wu3,7, Chun-Ku Chen4,2,3, Ying-Yueh Chang4, Yang-Che Shiu1, Henry Horng-Shing Lu6, Shih-Ann Chen1,2. 1. Heart Rhythm Center, Division of Cardiology, Department of Medicine (C.-M.L., S.-L.C., Y.-J.L., L.-W.L., Y.-F.H., F.-P.C., T.-F.C., T.-C.T., J.-N.L., C.-Y.L., T.-Y.C., C.-I.W., L.K., Y.-C.S., S.-A.C.), Taipei Veterans General Hospital, Taiwan. 2. Institute of Clinical Medicine (C.-M.L., S.-L.C., Y.-J.L., L.-W.L., Y.-F.H., F.-P.C., T.-F.C., T.-C.T., J.-N.L., C.-Y.L., T.-Y.C., C.-I.W., L.K., C.-K.C., S.-A.C.), National Yang-Ming University, Taipei, Taiwan. 3. Faculty of Medicine, School of Medicine (C.-M.L., S.-L.C., Y.-J.L., L.-W.L., Y.-F.H., F.-P.C., T.-F.C., T.-C.T., J.-N.L., C.-Y.L., T.-Y.C., L.K., M.-H.W., C.-K.C.), National Yang-Ming University, Taipei, Taiwan. 4. Department of Radiology (M.-H.W., C.-K.C., Y.-Y.C.), Taipei Veterans General Hospital, Taiwan. 5. Center of Teaching and Learning Development (H.-H.C.), National Chiao Tung University, Hsinchu, Taiwan. 6. Institute of Statistics (W.-S.C., H.H.-S.L.), National Chiao Tung University, Hsinchu, Taiwan. 7. Department of Medical Imaging, Diagnostic Radiology, Cheng Hsin General Hospital, Taipei, Taiwan (M.-H.W.).
Abstract
BACKGROUND: Non-pulmonary vein (NPV) trigger has been reported as an important predictor of recurrence post-atrial fibrillation ablation. Elimination of NPV triggers can reduce the recurrence of postablation atrial fibrillation. Deep learning was applied to preablation pulmonary vein computed tomography geometric slices to create a prediction model for NPV triggers in patients with paroxysmal atrial fibrillation. METHODS: We retrospectively analyzed 521 patients with paroxysmal atrial fibrillation who underwent catheter ablation of paroxysmal atrial fibrillation. Among them, pulmonary vein computed tomography geometric slices from 358 patients with nonrecurrent atrial fibrillation (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 23 683 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 triggers and 60 (16.8%) patients with NPV triggers±pulmonary vein triggers. The patients were randomly assigned to either training, validation, or test groups, and their data were allocated according to those sets. The image datasets were split into training (n=17 340), validation (n=3491), and testing (n=2852) groups, which had completely independent sets of patients. RESULTS: The accuracy of prediction in each pulmonary vein computed tomography 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 for each image and patient were 0.82±0.01 and 0.88±0.07, respectively. CONCLUSIONS: The deep learning model using preablation pulmonary vein computed tomography can be applied to predict the trigger origins in patients with paroxysmal atrial fibrillation receiving catheter ablation. The application of this model may identify patients with a high risk of NPV trigger before ablation.
BACKGROUND: Non-pulmonary vein (NPV) trigger has been reported as an important predictor of recurrence post-atrial fibrillation ablation. Elimination of NPV triggers can reduce the recurrence of postablation atrial fibrillation. Deep learning was applied to preablation pulmonary vein computed tomography geometric slices to create a prediction model for NPV triggers in patients with paroxysmal atrial fibrillation. METHODS: We retrospectively analyzed 521 patients with paroxysmal atrial fibrillation who underwent catheter ablation of paroxysmal atrial fibrillation. Among them, pulmonary vein computed tomography geometric slices from 358 patients with nonrecurrent atrial fibrillation (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 23 683 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 triggers and 60 (16.8%) patients with NPV triggers±pulmonary vein triggers. The patients were randomly assigned to either training, validation, or test groups, and their data were allocated according to those sets. The image datasets were split into training (n=17 340), validation (n=3491), and testing (n=2852) groups, which had completely independent sets of patients. RESULTS: The accuracy of prediction in each pulmonary vein computed tomography 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 for each image and patient were 0.82±0.01 and 0.88±0.07, respectively. CONCLUSIONS: The deep learning model using preablation pulmonary vein computed tomography can be applied to predict the trigger origins in patients with paroxysmal atrial fibrillation receiving catheter ablation. The application of this model may identify patients with a high risk of NPV trigger before ablation.
Entities:
Keywords:
ablation; artificial intelligence; atrial fibrillation; deep learning; trigger
Authors: Jonathan James Hyett Bray; Moghees Ahmad Hanif; Mohammad Alradhawi; Jacob Ibbetson; Surinder Singh Dosanjh; Sabrina Lucy Smith; Mahmood Ahmad; Dominic Pimenta Journal: Eur Heart J Open Date: 2022-03-17
Authors: Felix K Wegner; Lucas Plagwitz; Florian Doldi; Christian Ellermann; Kevin Willy; Julian Wolfes; Sarah Sandmann; Julian Varghese; Lars Eckardt Journal: Clin Res Cardiol Date: 2022-03-30 Impact factor: 6.138