Clinical applicability of an artificial intelligence prediction algorithm for early prediction of non-persistent atrial fibrillation

Yeji Kim; Gihun Joo; Bo-Kyung Jeon; Dong-Hyeok Kim; Tae Young Shin; Hyeonseung Im; Junbeom Park

doi:10.3389/fcvm.2023.1168054

Clinical applicability of an artificial intelligence prediction algorithm for early prediction of non-persistent atrial fibrillation

Front Cardiovasc Med. 2023 Sep 13:10:1168054. doi: 10.3389/fcvm.2023.1168054. eCollection 2023.

Authors

Yeji Kim^#¹, Gihun Joo^#², Bo-Kyung Jeon¹, Dong-Hyeok Kim¹, Tae Young Shin³, Hyeonseung Im^#^{2

4}, Junbeom Park^#¹

Affiliations

¹ Cardiovascular Center, Department of Internal Medicine, College of Medicine, Ewha Womans University Medical Center, Seoul, Republic of Korea.
² Interdisciplinary Graduate Program in Medical Bigdata Convergence, Kangwon National University, Chuncheon, Republic of Korea.
³ Department of Urology, College of Medicine, Ewha Womans University Medical Center, SYNERGY AI, Ewha Womans University Mokdong Hospital, Seoul, Republic of Korea.
⁴ Department of Computer Science and Engineering, Kangwon National University, Chuncheon, Republic of Korea.

^# Contributed equally.

Abstract

Background and aims: It is difficult to document atrial fibrillation (AF) on ECG in patients with non-persistent atrial fibrillation (non-PeAF). There is limited understanding of whether an AI prediction algorithm could predict the occurrence of non-PeAF from the information of normal sinus rhythm (SR) of a 12-lead ECG. This study aimed to derive a precise predictive AI model for screening non-PeAF using SR ECG within 4 weeks.

Methods: This retrospective cohort study included patients aged 18 to 99 with SR ECG on 12-lead standard ECG (10 seconds) in Ewha Womans University Medical Center for 3 years. Data were preprocessed into three window periods (which are defined with the duration from SR to non-PeAF detection) - 1 week, 2 weeks, and 4 weeks from the AF detection prospectively. For experiments, we adopted a Residual Neural Network model based on 1D-CNN proposed in a previous study. We used 7,595 SR ECGs (extracted from 215,875 ECGs) with window periods of 1 week, 2 weeks, and 4 weeks for analysis.

Results: The prediction algorithm showed an AUC of 0.862 and an F1-score of 0.84 in the 1:4 matched group of a 1-week window period. For the 1:4 matched group of a 2-week window period, it showed an AUC of 0.864 and an F1-score of 0.85. Finally, for the 1:4 matched group of a 4-week window period, it showed an AUC of 0.842 and an F1-score of 0.83.

Conclusion: The AI prediction algorithm showed the possibility of risk stratification for early detection of non-PeAF. Moreover, this study showed that a short window period is also sufficient to detect non-PeAF.

Keywords: artificial intelligence; convolutional neural network; electrocardiogram; non-persistent atrial fibrillation; normal sinus rhythm.

Grants and funding

This research was supported by Basic Science Research Program through the National Research Foundation of Korea, which is funded by the Ministry of Science, ICT & Future Planning (NRF-2022R1A2C1093352) and by the Korea Medical Device Development Fund grant, which is funded by the Korean 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). This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. RS-2022-00155966, Artificial Intelligence Convergence Innovation Human Resources Development (Ewha Womans University) and No. 2022-0-01196, Regional strategic Industry convergence security core talent training business). This research was also supported by “Regional Innovation Strategy (RIS)” through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2022RIS-005).