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Deep Learning-Enabled Assessment of Right Ventricular Function Improves Prognostication After Transcatheter Edge-to-Edge Repair for Mitral Regurgitation.

Circulation. Cardiovascular imaging
BACKGROUND: Right ventricular (RV) function has a well-established prognostic role in patients with severe mitral regurgitation (MR) undergoing transcatheter edge-to-edge repair (TEER) and is typically assessed using echocardiography-measured tricusp...

ECGEFNet: A two-branch deep learning model for calculating left ventricular ejection fraction using electrocardiogram.

Artificial intelligence in medicine
Left ventricular systolic dysfunction (LVSD) and its severity are correlated with the prognosis of cardiovascular diseases. Early detection and monitoring of LVSD are of utmost importance. Left ventricular ejection fraction (LVEF) is an essential ind...

Electrocardiographic-Driven artificial intelligence Model: A new approach to predicting One-Year mortality in heart failure with reduced ejection fraction patients.

International journal of medical informatics
BACKGROUND: Despite the proliferation of heart failure (HF) mortality prediction models, their practical utility is limited. Addressing this, we utilized a significant dataset to develop and validate a deep learning artificial intelligence (AI) model...

Diagnostic accuracy of artificial-intelligence-based electrocardiogram algorithm to estimate heart failure with reduced ejection fraction: A systematic review and meta-analysis.

Current problems in cardiology
INTRODUCTION: AI-based ECG has shown good accuracy in diagnosing heart failure. However, due to the heterogeneity of studies regarding cutoff points, its precision for specifically detecting heart failure with left ventricle reduced ejection fraction...

External validation of artificial intelligence for detection of heart failure with preserved ejection fraction.

Nature communications
Artificial intelligence (AI) models to identify heart failure (HF) with preserved ejection fraction (HFpEF) based on deep-learning of echocardiograms could help address under-recognition in clinical practice, but they require extensive validation, pa...

AI analysis for ejection fraction estimation from 12-lead ECG.

Scientific reports
Heart failure (HF) remains a leading global cause of cardiovascular deaths, with its prevalence expected to rise in the upcoming decade. Measuring the heart ejection fraction (EF) is crucial for diagnosing and monitoring HF. Although echocardiography...

Electrocardiogram-based deep learning to predict left ventricular systolic dysfunction in paediatric and adult congenital heart disease in the USA: a multicentre modelling study.

The Lancet. Digital health
BACKGROUND: Left ventricular systolic dysfunction (LVSD) is independently associated with cardiovascular events in patients with congenital heart disease. Although artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis is predictive of ...

Snapshot artificial intelligence-determination of ejection fraction from a single frame still image: a multi-institutional, retrospective model development and validation study.

The Lancet. Digital health
BACKGROUND: Artificial intelligence (AI) is poised to transform point-of-care practice by providing rapid snapshots of cardiac functioning. Although previous AI models have been developed to estimate left ventricular ejection fraction (LVEF), they ha...