Identifying patients with acute aortic dissection using an electrocardiogram with convolutional neural network.

ABSTRACT:

Background

The potential of utilizing artificial intelligence with electrocardiography (ECG) for initial screening of aortic dissection (AD) is promising. However, achieving a high positive predictive rate (PPR) remains challenging.

Methods and results

This retrospective analysis of a single-center, prospective cohort study (Shinken Database 2010-2017, N = 19,170) used digital 12-lead ECGs from initial patient visits. We assessed a convolutional neural network (CNN) model's performance for AD detection with eight-lead (I, II, and V1-6), single-lead, and double-lead (I, II) ECGs via five-fold cross-validation. The mean age was 63.5 ± 12.5 years for the AD group (n = 147) and 58.1 ± 15.7 years for the non-AD group (n = 19,023). The CNN model achieved an area under the curve (A

SUBMITTER: Arita T 

PROVIDER: S-EPMC10973583 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature