Automated diagnosis of chronic obstructive pulmonary disease using deep learning applied to electrocardiograms.

ABSTRACT:

Background

Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of morbidity and mortality globally. Effective management hinges on early diagnosis, which is often impeded by non-specific symptoms and resource-intensive diagnostic methods. This study assesses the effectiveness of electrocardiograms (ECGs) analysed via deep learning as a tool for early COPD detection.

Methods

We utilised a Convolutional Neural Network model to analyse ECGs for detecting COPD. The primary outcome was the accuracy of a new clinical COPD diagnosis as determined by ICD codes. Performance was evaluated using Area-Under-the-Curve (AUC) metrics derived by testing against ECGs from a set of holdout patients, ECGs from patients from another hospital, and ECGs of patients with COPD within th

SUBMITTER: Vaid A 

PROVIDER: S-EPMC12805298 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature