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A deep learning model to predict recurrence of atrial fibrillation after pulmonary vein isolation

International Journal of Arrhythmia 2020년 21권 1호 p.19 ~ 19
김주연, 김영훈, 오길환, Kim Sun-Hwa, Choi Young, 황유미, 김태석, 김성환, 김지훈, 장성원, 오용석, 이만영,
소속 상세정보
김주연 ( Kim Ju-Youn ) - Catholic University College of Medicine Uijeongbu St. Mary’s Hospital Department of Internal Medicine
김영훈 ( Kim Young-Hoon ) - Sungkyunkwan University College of Computing Department of Computer Science and Engineering
오길환 ( Oh Gil-Hwan ) - Geotwo Co. Ltd. Technical Research
 ( Kim Sun-Hwa ) - Catholic University College of Medicine Incheon St. Mary’s Hospital Department of Internal Medicine
 ( Choi Young ) - Catholic University College of Medicine Seoul St. Mary’s Hospital Department of Internal Medicine
황유미 ( Hwang You-Mi ) - Catholic University College of Medicine St. Vincent’s Hospital Department of Internal Medicine
김태석 ( Kim Tae-Seok ) - Catholic University College of Medicine Daejeon St. Mary’s Hospital Department of Internal Medicine
김성환 ( Kim Sung-Hwan ) - Catholic University College of Medicine Seoul St. Mary’s Hospital Department of Internal Medicine
김지훈 ( Kim Ji-Hoon ) - Catholic University College of Medicine St. Vincent’s Hospital Department of Internal Medicine
장성원 ( Jang Sung-Won ) - Catholic University College of Medicine Eunpyeong St. Mary’s Hospital Department of Internal Medicine
오용석 ( Oh Yong-Seog ) - Catholic University College of Medicine Seoul St. Mary’s Hospital Department of Internal Medicine
이만영 ( Lee Man-Young ) - Catholic University College of Medicine Yeouido St. Mary’s Hospital Department of Internal Medicine

Abstract


Background and Objectives: The efficacy of radiofrequency catheter ablation (RFCA) in atrial fibrillation (AF) is well established. The standard approach to RFCA in AF is pulmonary vein isolation (PVI). However, a large proportion of patients experiences recurrence of atrial tachyarrhythmia. The purpose of this study is to find out whether the AI model can assess AF recurrence in patients who underwent PVI.

Materials and methods: This study was a retrospective cohort study that enrolled consecutive patients who underwent catheter ablation for symptomatic, drug-refractory AF and PVI. We developed an AI algorithm to predict recurrence of AF after PVI using patient demographics and three-dimensional (3D) reconstructed left atrium (LA) images.

Results: We included 527 consecutive patients in the study. The overall mean LA diameter was 42.0?±?6.8 mm, and the mean LA volume calculated using 3D reconstructed images was 151.1?±?46.7 ml. During the follow-up period, atrial tachyarrhythmia recurred in 158 patients. The area under the curve (AUC) of the AI model based on a convolutional neural network (including 3D reconstruction images) was 0.61 (95% confidence interval [CI] 0.53?0.74) using the test dataset. The total test accuracy was 66.3% (57.0?75.6), and the sensitivity was 53.3% (34.8?71.9). The specificity was 73.2% (51.8?75.0), and the F1 score was 52.5% 34.5?66.7).

Conclusion: In this study, we developed an AI algorithm to predict recurrence of AF after catheter ablation of PVI using individual reconstructed LA images. This AI model was unable to predict recurrence of AF overwhelmingly; therefore, further large-scale study is needed.

키워드

Left atrium; Atrial fibrillation; Pulmonary vein isolation; Catheter ablation

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