Cardiovascular

Congestive Heart Failure

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

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A machine-learning-based method to predict adverse events in patients with dilated cardiomyopathy and severely reduced ejection fractions.

OBJECTIVE: Patients with dilated cardiomyopathy (DCM) and severely reduced left ventricular ejection...

Ambulatory Cardiovascular Monitoring Via a Machine-Learning-Assisted Textile Triboelectric Sensor.

Wearable bioelectronics for continuous and reliable pulse wave monitoring against body motion and pe...

Exposure to polydopamine nanoparticles induces neurotoxicity in the developing zebrafish.

Currently, the potential applications of polydopamine (PDA) nanoparticles in the biomedical field ar...

Detection of hypertrophic cardiomyopathy by an artificial intelligence electrocardiogram in children and adolescents.

BACKGROUND: There is no established screening approach for hypertrophic cardiomyopathy (HCM). We rec...

Estimated Artificial Neural Network Modeling of Maximal Oxygen Uptake Based on Multistage 10-m Shuttle Run Test in Healthy Adults.

We aimed to develop an artificial neural network (ANN) model to estimate the maximal oxygen uptake (...

The effect of principal component analysis in the diagnosis of congestive heart failure via heart rate variability analysis.

In this study, we investigated the effect of principal component analysis (PCA) in congestive heart ...

Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model.

Diabetic retinopathy (DR) is an eye disease that alters the blood vessels of a person suffering from...

Imputation of the continuous arterial line blood pressure waveform from non-invasive measurements using deep learning.

In two-thirds of intensive care unit (ICU) patients and 90% of surgical patients, arterial blood pre...

How to standardize the measurement of left ventricular ejection fraction.

Despite recent advances in imaging for myocardial deformation, left ventricular ejection fraction (L...

A Multitask Deep-Learning System to Classify Diabetic Macular Edema for Different Optical Coherence Tomography Devices: A Multicenter Analysis.

OBJECTIVE: Diabetic macular edema (DME) is the primary cause of vision loss among individuals with d...

Diagnosis of retinal disorders from Optical Coherence Tomography images using CNN.

An efficient automatic decision support system for detection of retinal disorders is important and i...

Artificial Intelligence-Enabled Electrocardiography to Screen Patients with Dilated Cardiomyopathy.

Undiagnosed dilated cardiomyopathy (DC) can be asymptomatic or present as sudden cardiac death, ther...

Automatic multiclass intramedullary spinal cord tumor segmentation on MRI with deep learning.

Spinal cord tumors lead to neurological morbidity and mortality. Being able to obtain morphometric q...

PEDF, a pleiotropic WTC-LI biomarker: Machine learning biomarker identification and validation.

Biomarkers predict World Trade Center-Lung Injury (WTC-LI); however, there remains unaddressed multi...

Using Wearables and Machine Learning to Enable Personalized Lifestyle Recommendations to Improve Blood Pressure.

Blood pressure (BP) is an essential indicator for human health and is known to be greatly influence...

Distinct tumor signatures using deep learning-based characterization of the peritumoral microenvironment in glioblastomas and brain metastases.

Tumor types are classically distinguished based on biopsies of the tumor itself, as well as a radiol...

Automatic left ventricle volume calculation with explainability through a deep learning weak-supervision methodology.

BACKGROUND AND OBJECTIVE: Magnetic resonance imaging is the most reliable imaging technique to asses...

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