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Machine Learning for Predicting Atrial Fibrillation Recurrence After Cardioversion: A Modest Leap Forward

  • Junbeom Park (Department of Cardiology, Ewha Womans University Medical Center, Ewha Womans University College of Medicine)
  • Received : 2023.07.14
  • Accepted : 2023.07.18
  • Published : 2023.10.01

Abstract

Keywords

Acknowledgement

This work was supported by the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (project number: 9991006899, RS-2020-KD000234).

References

  1. Kuo L, Chan YH, Liao JN, Chen SA, Chao TF. Stroke and bleeding risk assessment in atrial fibrillation: where are we now? Korean Circ J 2021;51:668-80. https://doi.org/10.4070/kcj.2021.0170
  2. Kim D, Yang PS, Joung B. Optimal rhythm control strategy in patients with atrial fibrillation. Korean Circ J 2022;52:496-512. https://doi.org/10.4070/kcj.2022.0078
  3. Bamford P, Rogers J. Where's the bleed? A response to Piccini et al.'s: management of major bleeding events in patients treated with rivaroxaban vs. warfarin: results from the ROCKET AF trial. Eur Heart J 2019;40:1567.
  4. Sanchez-Somonte P, Gul EE, Verma A. The importance of arrhythmia burden for outcomes and management related to catheter ablation of atrial fibrillation. Korean Circ J 2021;51:477-86. https://doi.org/10.4070/kcj.2021.0077
  5. Park J. Can artificial intelligence prediction algorithms exceed statistical predictions? Korean Circ J 2019;49:640-1. https://doi.org/10.4070/kcj.2019.0110
  6. Kwon S, Lee E, Ju H, et al. Machine learning prediction for the recurrence after electrical cardioversion of patients with persistent atrial fibrillation. Korean Circ J 2023;53:677-89. https://doi.org/10.4070/kcj.2023.0012
  7. Kwon S, Hong J, Choi EK, et al. Detection of atrial fibrillation using a ring-type wearable device (CardioTracker) and deep learning analysis of photoplethysmography signals: prospective observational proof-of-concept study. J Med Internet Res 2020;22:e16443.
  8. Joo G, Song Y, Im H, Park J. Clinical implication of machine learning in predicting the occurrence of cardiovascular disease using big data (nationwide cohort data in Korea). IEEE Access 2020;8:157643-53. https://doi.org/10.1109/ACCESS.2020.3015757
  9. Al'Aref SJ, Anchouche K, Singh G, et al. Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. Eur Heart J 2019;40:1975-86. https://doi.org/10.1093/eurheartj/ehy404
  10. Park J, Lee C, Leshem E, et al. Early differentiation of long-standing persistent atrial fibrillation using the characteristics of fibrillatory waves in surface ECG multi-leads. Sci Rep 2019;9:2746.