AIMC Topic: Stroke Volume

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Automated ejection fraction and risk stratification in cardiomyopathy patients with diverse LV geometry using 2D echocardiography.

Scientific reports
Cardiomyopathy often alters left ventricular geometry (LVG), impairing cardiac function. We developed a deep learning (DL) model to estimate left ventricular ejection fraction (LVEF) from echocardiographic images while accounting for LVG variability ...

Artificial intelligence-assisted diagnosis and prognostication in low ejection fraction using electrocardiograms in inpatient department: a pragmatic randomized controlled trial.

BMC medicine
BACKGROUND: Early diagnosis of low ejection fraction (EF) remains challenging despite being a treatable condition. This study aimed to evaluate the effectiveness of an electrocardiogram (ECG)-based artificial intelligence (AI)-assisted clinical decis...

SEISMIC-HF 1: key findings from AHA24 and implications for remote cardiac monitoring.

Heart failure reviews
While there is continued progress in developing therapies for patients with heart failure, the condition results in significant morbidity and a sizeable economic impact on our society. Recent advances in wearable sensors combined with machine learnin...

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...

Comprehensive Analysis of Heart Failure Subtypes Presenting at Emergency Department for Acute Heart Failure Management.

Journal of emergency nursing
INTRODUCTION: Despite advances in echocardiography and biomarkers, the pathophysiological complexities among heart failure categories remain incompletely understood. This study analyzed patients' characteristics across heart failure with reduced ejec...

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...