Associations between echocardiographic traits and AI-ECG predictions of heart failure
A recent study explored the relationship between echocardiographic traits and AI-ECG predictions of heart failure. The research analyzed data from over 8,000 patients to assess how well AI-ECG predictions align with established echocardiographic measures. Findings indicate that AI-ECG primarily correlates with measures of systolic function, particularly global longitudinal strain.
- ▪The study involved a retrospective analysis of ECG and echocardiography data from 8147 patients.
- ▪Global longitudinal strain showed the strongest correlation with AI-ECG predictions of heart failure risk.
- ▪Correlations varied by sex, with diastolic indices showing stronger correlations in women.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.24576 (cs) [Submitted on 23 May 2026] Title:Associations between echocardiographic traits and AI-ECG predictions of heart failure Authors:Elias Stenhede, Eivind Bjørkan Orstad, Torbjørn Omland, Henrik Schirmer, Arian Ranjbar View a PDF of the paper titled Associations between echocardiographic traits and AI-ECG predictions of heart failure, by Elias Stenhede and Eivind Bj{\o}rkan Orstad and Torbj{\o}rn Omland and Henrik Schirmer and Arian Ranjbar View PDF Abstract:Artificial intelligence-enabled electrocardiography (AI-ECG) can detect heart failure (HF), including disease not captured by left ventricular ejection fraction (LVEF), but the cardiac phenotypes underlying model predictions remain unclear.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
