Cardiovascular

Congestive Heart Failure

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

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AI-Assisted identification of sex-specific patterns in diabetic retinopathy using retinal fundus images.

Diabetic retinopathy (DR) is a microvascular complication of diabetes that can lead to blindness if ...

Brain tumor segmentation by optimizing deep learning U-Net model.

BackgroundMagnetic Resonance Imaging (MRI) is a cornerstone in diagnosing brain tumors. However, the...

Prescreening depression using wearable electrocardiogram and photoplethysmogram data from a psycholinguistic experiment.

Depression is a prevalent mental health disorder that significantly impacts well-being and qual...

Investigating the Impact of the Stationarity Hypothesis on Heart Failure Detection using Deep Convolutional Scattering Networks and Machine Learning.

Detection of Cardiovascular Diseases (CVDs) has become crucial nowadays, as the World Health Organiz...

Identification and Verification of SLC6A15 Involved in Keloid via Bioinformatics Analysis and Machine Learning.

Keloid is a fibroproliferative disorder that poses a challenge in clinical management. This study ai...

Exploration on Bubble Entropy.

Bubble entropy is a recently proposed entropy metric. Having certain advantages over popular definit...

Identifying key physiological and clinical factors for traumatic brain injury patient management using network analysis and machine learning.

In the intensive care unit (ICU), managing traumatic brain injury (TBI) patients presents significan...

Convolutional Neural Network Fused With Recurrent Network for ECG-Based Detection of Hypertrophic Cardiomyopathy.

BACKGROUND: Hypertrophic Cardiomyopathy (HCM) affects the left ventricle of the heart, leading to th...

Functionalized Nanofinger Enhances Pretrained Language Model Performance for Ultrafast Early Warning of Heart Attacks.

Heart attacks are the leading cause of death worldwide, which means an accurate early warning system...

Artificial intelligence-enabled electrocardiography and echocardiography to track preclinical progression of transthyretin amyloid cardiomyopathy.

BACKGROUND AND AIMS: The diagnosis of transthyretin amyloid cardiomyopathy (ATTR-CM) requires advanc...

Monitoring systemic ventriculoarterial coupling after cardiac surgery using continuous transoesophageal echocardiography and deep learning.

Deterioration of ventriculoarterial coupling is detrimental to cardiovascular and left ventricular f...

Late gadolinium enhancement imaging and sudden cardiac death.

The prediction and management of sudden cardiac death risk continue to pose significant challenges i...

Using Data Mining to Differentiate Dengue with Warning Signs from Severe Dengue: A Predictive Model from Oaxaca, Mexico.

Dengue with warning signs (DWS) and severe dengue are significant public health concerns in tropical...

Inflammatory, fibrotic and endothelial biomarker profiles in COVID-19 patients during and following hospitalization.

Survivors of severe COVID-19 often suffer from long-term respiratory issues, but the molecular drive...

Inter-AI Agreement in Measuring Cine MRI-Derived Cardiac Function and Motion Patterns: A Pilot Study.

Manually analyzing a series of MRI images to obtain information about the heart's motion is a time-c...

Artificial intelligence in cardiac sarcoidosis: ECG, Echo, CPET and MRI.

PURPOSE OF REVIEW: Cardiac sarcoidosis is a form of inflammatory cardiomyopathy that varies in its c...

Evaluating the Performance and Potential Bias of Predictive Models for Detection of Transthyretin Cardiac Amyloidosis.

BACKGROUND: Delays in the diagnosis of transthyretin amyloid cardiomyopathy (ATTR-CM) contribute to ...

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