Use of artificial intelligence-powered ECG to differentiate between cardiac and pulmonary pathologies in patients with acute dyspnoea in the emergency department.

ABSTRACT:

Background

Acute dyspnoea is common in acute care settings. However, identifying the origin of dyspnoea in the emergency department (ED) is often challenging. We aimed to investigate whether our artificial intelligence (AI)-powered ECG analysis reliably distinguishes between the causes of dyspnoea and evaluate its potential as a clinical triage tool for comparing conventional heart failure diagnostic processes using natriuretic peptides.

Methods

A retrospective analysis was conducted using an AI-based ECG algorithm on patients ≥18 years old presenting with dyspnoea at the ED from February 2006 to September 2023. Patients were categorised into cardiac or pulmonary origin groups based on initial admission. The performance of an AI-ECG using a transformer neural network algorit

SUBMITTER: Jang JH 

PROVIDER: S-EPMC11448159 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature