Cardiovascular

Congestive Heart Failure

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

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Showing 736-756 of 3,389 articles
Efficient method for analyzing MR real-time cines: Toward accurate quantification of left ventricular function.

BACKGROUND: To develop and assess an efficient method to identify end-expiratory end-diastolic (ED) ...

Determining the appropriate amount of anesthetic gas using DWT and EMD combined with neural network.

The spectrum of EEG has been studied to predict the depth of anesthesia using variety of signal proc...

Lower Extremity Bypass Surveillance and Peak Systolic Velocities Value Prediction Using Recurrent Neural Networks.

Routine duplex ultrasound surveillance is recommended after femoral-popliteal and femoral-tibial-ped...

Mechanistic exploration of hexokinase 2 and metabolism in diabetic cardiomyopathy.

The pathogenesis of diabetic cardiomyopathy (DCM) remains incompletely understood. The present study...

A novel approach for ECG signal classification using sliding Euclidean quantization and bitwise pattern encoding.

This study aims to introduce a novel, computationally lightweight feature extraction technique calle...

Metabolomic machine learning predictor for arsenic-associated hypertension risk in male workers.

Arsenic (As)-induced hypertension is a significant public health concern, highlighting the need for ...

Precision medicine applications in dilated cardiomyopathy: Advancing personalized care.

Dilated cardiomyopathy (DCM) is a prevalent cardiac disorder affecting 1 in 250-500 individuals, cha...

Machine learning risk-prediction model for in-hospital mortality in Takotsubo cardiomyopathy.

BACKGROUND: Takotsubo cardiomyopathy (TC) is an acute heart failure syndrome characterized by transi...

Machine learning applied to wearable fitness tracker data and the risk of hospitalizations and cardiovascular events.

BACKGROUND: Wearable fitness trackers generate extensive physiological and activity data, offering p...

From Acquisition to Prognosis: The Role of AI in Cardiac Magnetic Resonance Imaging Evaluation of Ischemic Cardiomyopathy.

Acute and chronic ischemic cardiomyopathy (ICM) still represents a leading cause of morbidity and mo...

Predicting blood pressure without a cuff using a unique multi-modal wearable device and machine learning algorithm.

Blood pressure is a critical risk factor for cardiovascular diseases (CVDs), yet most adults do not ...

Eigenhearts: Cardiac diseases classification using eigenfaces approach.

In the realm of cardiovascular medicine, medical imaging plays a crucial role in accurately classify...

AI driven cardiovascular risk prediction using NLP and Large Language Models for personalized medicine in athletes.

The performance and long-term health of athletes are significantly influenced by their cardiovascula...

Deep Learning Algorithms to Predict Differential Renal Function <40% in Unilateral Hydronephrosis Based on Key Parameters of Urinary Tract Ultrasound.

OBJECTIVE: To identify the correlation between ultrasound findings and the incidence of differential...

Machine Learning Assessment of Gestational Age in Accelerated Maturation, Delayed Maturation, Villous Edema, Chorangiosis, and Intrauterine Fetal Demise.

CONTEXT.—: Assessment of placental villous maturation is among the most common tasks in perinatal pa...

Artificial Intelligence Tools for Preconception Cardiomyopathy Screening Among Women of Reproductive Age.

PURPOSE: Identifying cardiovascular disease before conception and in early pregnancy can better info...

Predicting cardiovascular risk with hybrid ensemble learning and explainable AI.

Cardiovascular diseases (CVDs) are still one of the leading causes of death globally, underscoring t...

Self-supervised model-informed deep learning for low-SNR SS-OCT domain transformation.

This article introduces a novel deep-learning based framework, Super-resolution/Denoising network (S...

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