Congestive Heart Failure

Reappraising cardiac function with myocardial contraction fraction: normal values, disease detection, and prognostication.

AIMS: Assessing cardiac function is critical for managing cardiovascular disease, guiding treatment, monitoring progression, and risk stratification. While left ventricular (LV) ejection fraction (LVEF) is firmly established, it has limitations. Myocardial contraction fraction (MCF)-the ratio of stroke volume to myocardial volume, is simple to compute without additional analysis and offers a promi...

A machine learning approach using echocardiographic and hematological parameters to classify cerebral small vessel disease burden.

BACKGROUND: Cerebral small vessel disease (CSVD) is a leading cause of stroke and dementia and is associated with cardiac and hematological biomarkers. Although brain magnetic resonance imaging (MRI) remains the reference standard for assessing CSVD burden, its cost and contraindications limit widespread clinical application. AIM: To develop machine learning (ML) models based on routine echocardio...

Addressing disparities in transthyretin amyloid cardiomyopathy: A systematic review of artificial intelligence in the early identification to improve patient outcomes.

BACKGROUND: Transthyretin amyloid cardiomyopathy (ATTR-CM) remains substantially underdiagnosed among Black patient populations. When applied to non-i...

Effect of SnapShot Freeze 2 on Reducing Pulsation Artifacts in Coronary Artery Imaging of Patients With High Heart Rates.

BACKGROUND This study aimed to evaluate the effect of SnapShot Freeze 2 (SSF2) on reducing pulsation artifacts in coronary artery imaging of patients ...

Machine-learning approach on echocardiography to improve the detection of transthyretin amyloid cardiomyopathy: GRAAL algorithm.

AIMS: Transthyretin amyloid cardiomyopathy (ATTR-CM) is an increasingly recognized cause of heart failure, yet detection remains challenging due to it...

A generalizable deep learning system for cardiac MRI.

Cardiac MRI allows for a comprehensive assessment of myocardial structure, function and tissue characteristics. Here we describe a foundational vision...

Enhancing few-shot personalized cuffless blood pressure estimation with self-supervised learning.

Objective.Individual differences across subjects reduce the accuracy of physiological signal-based cuffless blood pressure (BP) estimation. However, t...

Chronic heart failure detection based on long-term RR interval dynamics.

Chronic heart failure (CHF) is a condition affecting millions worldwide, characterized by the heart's reduced ability to pump blood efficiently. Conve...

Imaging biomarkers of post-COVID dyspnea: insights from machine learning CT patterns and parametric response mapping.

Cardiopulmonary exercise testing in contemporary cardiology: physiological insights, novel applications and evolving algorithms.

Cardiopulmonary exercise testing (CPET) allows for the study of the pathophysiology of exercise intolerance through assessment of exercise integrative...

Chronic phase of Chikungunya: understanding the impact of joint pain using data science and artificial intelligence.

BACKGROUND: Chikungunya is an arbovirus capable of affecting the musculoskeletal system of infected individuals. Furthermore, it has the potential to ...

Artificial Intelligence as a Catalyst for Value-Based Health Insurance in the United States: Narrative Review and Policy Perspective.

The United States health insurance system is at a critical crossroads. Inflating costs, fragmented care, and administrative inefficiencies have reveal...

Machine learning model for predicting improvement in left ventricular systolic function in patients with heart failure and reduced ejection fraction.

BACKGROUND: About one-third of patients with heart failure (HF) with reduced ejection fraction (HFrEF) may demonstrate left ventricular ejection fract...

Cross-sectional evaluation of cardiovascular biological age using point-of-care ultrasound.

AIMS: Biological age is increasingly recognized as a superior predictor of morbidity, mortality, compared with chronological age. Artificial intellige...

Deep-Learning-Based Image Reconstruction to Improve End-Diastolic and Systolic Cardiac T1 Mapping.

PURPOSE: To develop an image reconstruction method that enables increased spatial resolution cardiac T1 mapping in both the end-diastolic and systolic...

Artificial Intelligence-Driven Electrocardiogram Screening for Asymptomatic Left Ventricular Systolic Dysfunction in the General Population.

BACKGROUND: Asymptomatic left ventricular systolic dysfunction (LVSD) is a well-established precursor of overt heart failure (HF), yet it often remain...

Artificial Intelligence and the Left Atrial Filling Index (Mitral E/LA Strain Ratio) as Novel Tools for the Evaluation of Left Ventricular Diastolic Dysfunction: An Editorial Commentary and Systematic Review.

Comparison of Left Ventricular Ejection Fraction Among 18F-FDG Dual-Gated PET/CT, Single-Gated PET/MRI, and Cardiac MRI.

Left ventricular ejection fraction (LVEF) is a critical parameter in the evaluation of cardiac function, and its measurement can guide treatment decis...

Adiposome Lipidomic Shifts Following Bariatric Surgery in Association With Weight Loss and Cardiometabolic Benefit.

CONTEXT: Adiposomes carry bioactive lipids that shape systemic metabolism and vascular function. OBJECTIVE: Building on our previous findings that obe...

Coronary artery disease diagnosis with signal processing and machine learning of heart sound signals: a systematic review.

Coronary artery disease (CAD) remains a major contributor to morbidity and mortality worldwide. Heart sound analysis has been investigated as a noninv...