Deep learning-based model detects atrial septal defects from electrocardiography: a cross-sectional multicenter hospital-based study.

ABSTRACT:

Background

Atrial septal defect (ASD) increases the risk of adverse cardiovascular outcomes. Despite the potential for risk mitigation through minimally invasive percutaneous closure, ASD remains underdiagnosed due to subtle symptoms and examination findings. To bridge this diagnostic gap, we propose a novel screening strategy aimed at early detection and enhanced diagnosis through the implementation of a convolutional neural network (CNN) to identify ASD from 12-lead electrocardiography (ECG).

Methods

ECGs were collected from patients with at least one recorded echocardiogram at 3 hospitals from 2 continents (Keio University Hospital from July 2011 to December 2020, Brigham and Women's Hospital from January 2015 to December 2020, and Dokkyo Medical University Saitama Medica

SUBMITTER: Miura K 

PROVIDER: S-EPMC10518511 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature