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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.

BackgroundThe most-commonly used multi-slice Simpson's method employed with routine two-dimensional segmented cine images makes it difficult to evaluate left ventricular (LV) volume and function due to endocardial border blurring and beat-to-beat variation during atrial fibrillation (AF) status.ObjectivesTo assess the feasibility of compressed sensing real-time (CSRT) cine imaging combined with an area-length method for quantification of LV systolic function in normal sinus rhythm (NSR) and AF.MethodsThe CSRT cine sequence and routine segmented balanced Steady-State-Free-Precession cine sequence were performed in 71 patients with NSR (n = 36) or AF (n = 35). Image quality and edge sharpness for both sequences were assessed. The LV functional measurements in patients with NSR included end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), ejection fraction (EF), cardiac output (CO), cardiac index (CI), and LV mass (LVM); all were assessed using segmented cine with Simpson's rule in short axis (SegSA_Simpson, as a reference standard) and area-length (AL) method in the two chamber (Seg2CH_AL) or four chamber (Seg4CH_AL) and CSRT cine with AL method in the two chamber (CSRT2CH_AL) or four chamber (CSRT4CH_AL). Finally, the mean, maximum, and minimum values of each LV functional parameter [EDV/ESV/SV/EF/CO/CI/LVM/heart rate (HR)] from 4~5 consecutive heartbeats were measured using CSRT2CH_AL in patients with AF.ResultsIn patients with NSR, measurements of EDV (p > 0.05), ESV (p > 0.05), SV (p > 0.05), EF (p > 0.05), and LVM (p > 0.05) assessed with CSRT2CH_AL did not differ significantly from those obtained with SegSA_Simpson. In patients with AF, CSRT image quality score (p < 0.001) and edge sharpness (p < 0.001) both were significantly higher than those obtained from segmented cine. The CSRT2CH_AL provided significantly different results among mean, maximum, and minimum values of each LV parameter from 4~5 consecutive heartbeats (all p < 0.001) with strong inter- and intra-observer agreement in AF.ConclusionsThe CSRT cine sequence combined with two chamber area-length analysis accurately assessed LV systolic function in NSR. This approach is expected to permit the assessment of multiple parameters in consecutive heartbeats with good inter- and intra-observer reproducibility for beat-to-beat analysis of LV function in AF.

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Kim, Joo, Jeon, Kim, Shin, Im and Park.)

Background: The most-commonly used multi-slice Simpson's method employed with routine two-dimensional segmented cine images makes it difficult to evaluate left ventricular (LV) volume and function due to endocardial border blurring and beat-to-beat variation during atrial fibrillation (AF) status., Objectives: To assess the feasibility of compressed sensing real-time (CSRT) cine imaging combined with an area-length method for quantification of LV systolic function in normal sinus rhythm (NSR) and AF., Methods: The CSRT cine sequence and routine segmented balanced Steady-State-Free-Precession cine sequence were performed in 71 patients with NSR ( n = 36) or AF ( n = 35). Image quality and edge sharpness for both sequences were assessed. The LV functional measurements in patients with NSR included end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), ejection fraction (EF), cardiac output (CO), cardiac index (CI), and LV mass (LVM); all were assessed using segmented cine with Simpson's rule in short axis (SegSA&#95;Simpson, as a reference standard) and area-length (AL) method in the two chamber (Seg2CH&#95;AL) or four chamber (Seg4CH&#95;AL) and CSRT cine with AL method in the two chamber (CSRT2CH&#95;AL) or four chamber (CSRT4CH&#95;AL). Finally, the mean, maximum, and minimum values of each LV functional parameter [EDV/ESV/SV/EF/CO/CI/LVM/heart rate (HR)] from 4~5 consecutive heartbeats were measured using CSRT2CH&#95;AL in patients with AF., Results: In patients with NSR, measurements of EDV ( p > 0.05), ESV ( p > 0.05), SV ( p > 0.05), EF ( p > 0.05), and LVM ( p > 0.05) assessed with CSRT2CH&#95;AL did not differ significantly from those obtained with SegSA&#95;Simpson. In patients with AF, CSRT image quality score ( p < 0.001) and edge sharpness ( p < 0.001) both were significantly higher than those obtained from segmented cine. The CSRT2CH&#95;AL provided significantly different results among mean, maximum, and minimum values of each LV parameter from 4~5 consecutive heartbeats (all p < 0.001) with strong inter- and intra-observer agreement in AF., Conclusions: The CSRT cine sequence combined with two chamber area-length analysis accurately assessed LV systolic function in NSR. This approach is expected to permit the assessment of multiple parameters in consecutive heartbeats with good inter- and intra-observer reproducibility for beat-to-beat analysis of LV function in AF., Competing Interests: JA was employed by Siemens Shenzhen Magnetic Resonance Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Yin, Cui, An, Zhao, Yang, Li, Yang, Wang, Dong, Yu, He, Chen, Lu and Zhao.)