Clinical applicability of an artificial intelligence prediction algorithm for early prediction of non-persistent atrial fibrillation
- 주제(키워드) non-persistent atrial fibrillation , artificial intelligence , convolutional neural network , electrocardiogram , normal sinus rhythm
- 주제(기타) Cardiac & Cardiovascular Systems
- 설명문(일반) [Kim, Yeji; Jeon, Bo-Kyung; Kim, Dong-Hyeok; Park, Junbeom] Ewha Womans Univ, Coll Med, Cardiovasc Ctr, Med Ctr,Dept Internal Med, Seoul, South Korea; [Joo, Gihun; Im, Hyeonseung] Kangwon Natl Univ, Interdisciplinary Grad Program Med Bigdata Converg, Chunchon, South Korea; [Shin, Tae Young] Ewha Womans Univ, Mokdong Hosp, Coll Med, Med Ctr,SYNERGY AI,Dept Urol, Seoul, South Korea; [Im, Hyeonseung] Kangwon Natl Univ, Dept Comp Sci & Engn, Chunchon, South Korea
- 등재 SCIE, SCOPUS
- OA유형 gold
- 발행기관 FRONTIERS MEDIA SA
- 발행년도 2023
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000211626
- 본문언어 영어
- Published As https://doi.org/10.3389/fcvm.2023.1168054
- PubMed 37781313
초록/요약
Background and aimsIt 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.MethodsThis 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.ResultsThe 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.ConclusionThe 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.
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