Cardiovascular

Congestive Heart Failure

Latest AI and machine learning research in congestive heart failure for healthcare professionals.

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Predictive value of CT-based and AI-reconstructed 3D-TAPSE in patients undergoing transcatheter tricuspid valve repair.

BACKGROUND: The tricuspid annular plane systolic excursion (TAPSE) assessed by echocardiography has ...

Predicting Blood Pressures for Pregnant Women by PPG and Personalized Deep Learning.

Blood pressure (BP) is predicted by this effort based on photoplethysmography (PPG) data to provide ...

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

Left ventricular systolic dysfunction (LVSD) and its severity are correlated with the prognosis of c...

Quantitative imaging for early detection and risk stratification of cardiovascular disease using 4D flow MRI and arterial spin labelling.

Heart failure (HF) significantly burdens global healthcare, necessitating early detection and precis...

Development of a Self-Deploying Extra-Aortic Compression Device for Medium-Term Hemodynamic Stabilization: A Feasibility Study.

Hemodynamic stabilization is crucial in managing acute cardiac events, where compromised blood flow ...

Development of a Predictive Model of Occult Cancer After a Venous Thromboembolism Event Using Machine Learning: The CLOVER Study.

: Venous thromboembolism (VTE) can be the first manifestation of an underlying cancer. This study ai...

Optimizing autonomous artificial intelligence diagnostics for neuro-ocular health in space missions.

Spaceflight-Associated Neuro-Ocular Syndrome (SANS) presents a critical risk in long-duration missio...

Machine learning for predicting acute myocardial infarction in patients with sepsis.

Acute myocardial infarction (AMI) and sepsis are the leading causes of high mortality rates in inten...

Detection of late gadolinium enhancement in patients with hypertrophic cardiomyopathy using machine learning.

BACKGROUND: Late gadolinium enhancement (LGE) on cardiac magnetic resonance (CMR) in hypertrophic ca...

Prediction of new-onset atrial fibrillation in patients with hypertrophic cardiomyopathy using machine learning.

AIMS: Atrial fibrillation (AF) is the most common sustained arrhythmia among patients with hypertrop...

Artificial Intelligence Advancements in Cardiomyopathies: Implications for Diagnosis and Management of Arrhythmogenic Cardiomyopathy.

PURPOSE OF REVIEW: This review aims to explore the emerging potential of artificial intelligence (AI...

Robust modelling of arterial blood pressure reconstruction from photoplethysmography.

Blood pressure is a crucial indicator of cardiovascular disease, and arterial blood pressure (ABP) w...

BP-Net: Monitoring "Changes" in Blood Pressure Using PPG With Self-Contrastive Masking.

Estimating blood pressure (BP) values from physiological signals (e.g., photoplethysmogram (PPG)) us...

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