AI Article Synopsis
- - We created a convolutional neural network (CNN) to detect atrial fibrillation (AF) using sinus rhythm ECGs (SR-ECG), analyzing data from 616 AF cases and 3,412 sinus rhythm cases among over 19,000 patients.
- - The CNN model showed strong performance with an area under the curve (AUC) of 0.872 for detecting AF with eight-lead ECGs, indicating high accuracy in distinguishing AF from sinus rhythm.
- - Notably, the double-lead ECG using leads I and V1 had a similar AUC of 0.871, suggesting that this simpler approach could be a viable alternative for AF screening in clinical settings.
Article Abstract
We developed a convolutional neural network (CNN) model to detect atrial fibrillation (AF) using the sinus rhythm ECG (SR-ECG). However, the diagnostic performance of the CNN model based on different ECG leads remains unclear. In this retrospective analysis of a single-center, prospective cohort study, we identified 616 AF cases and 3,412 SR cases for the modeling dataset among new patients (n=19,170). The modeling dataset included SR-ECGs obtained within 31 days from AF-ECGs in AF cases and SR cases with follow-up ≥1,095 days. We evaluated the CNN model's performance for AF detection using 8-lead (I, II, and V1-6), single-lead, and double-lead ECGs through 5-fold cross-validation. The CNN model achieved an area under the curve (AUC) of 0.872 (95% confidence interval (CI): 0.856-0.888) and an odds ratio of 15.24 (95% CI: 12.42-18.72) for AF detection using the eight-lead ECG. Among the single-lead and double-lead ECGs, the double-lead ECG using leads I and V1 yielded an AUC of 0.871 (95% CI: 0.856-0.886) with an odds ratio of 14.34 (95% CI: 11.64-17.67). We assessed the performance of a CNN model for detecting AF using eight-lead, single-lead, and double-lead SR-ECGs. The model's performance with a double-lead (I, V1) ECG was comparable to that of the 8-lead ECG, suggesting its potential as an alternative for AF screening using SR-ECG.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10920024 | PMC |
| http://dx.doi.org/10.1253/circrep.CR-23-0068 | DOI Listing |
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