ArticleOpen heart2024
Use of artificial intelligence-powered ECG to differentiate between cardiac and pulmonary pathologies in patients with acute dyspnoea in the emergency department.
Article in Open heart, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- An Artificial Intelligence-Enabled Electrocardiogram to Evaluate Patients With Dyspnea in the Emergency Department.Mayo Clinic proceedings. Innovations, quality & outcomes · 2025Article
- Mapping artificial intelligence models in emergency medicine: A scoping review on artificial intelligence performance in emergency care and education.Turkish journal of emergency medicineReview
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAcute 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.
methodsA 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 algorithm was assessed to analyse standard 12-lead ECGs for accuracy, sensitivity, specificity and area under the receiver operating characteristic curve (AUC). Additionally, we compared the diagnostic efficacy of AI-ECG models with N-terminal probrain natriuretic peptide (NT-proBNP) levels to identify cardiac origins.
resultsAmong the 3105 patients included in the study, 1197 had cardiac-origin dyspnoea. The AI-ECG model demonstrated an AUC of 0.938 and 88.1% accuracy for cardiac-origin dyspnoea. The sensitivity, specificity and positive and negative predictive values were 93.0%, 79.5%, 89.0% and 86.4%, respectively. The F1 score was 0.828. AI-ECG demonstrated superior diagnostic performance in identifying cardiac-origin dyspnoea compared with NT-proBNP. True cardiac origin was confirmed in 96 patients in a sensitivity analysis of 129 patients with a high probability of cardiac origin initially misdiagnosed as pulmonary origin predicted by AI-ECG.
conclusionsAI-ECG demonstrated superior diagnostic accuracy over NT-proBNP and showed promise as a clinical triage tool. It is a potentially valuable tool for identifying the origin of dyspnoea in emergency settings and supporting decision-making.
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