Congestive Heart Failure

Multimodal data for systolic and diastolic blood pressure prediction: The hypertension conscious artificial intelligence.

Cardiovascular Imaging in China: Yesterday, Today, and Tomorrow.

The high prevalence and mortality of cardiovascular diseases in China's large population has increas...

Artificial Intelligence Applied to Cardiomyopathies: Is It Time for Clinical Application?

PURPOSE OF REVIEW: Artificial intelligence (AI) techniques have the potential to remarkably change t...

Bridging the resources gap: deep learning for fluorescein angiography and optical coherence tomography macular thickness map image translation.

BACKGROUND: To assess the ability of the pix2pix generative adversarial network (pix2pix GAN) to syn...

Utility of Normalized Body Composition Areas, Derived From Outpatient Abdominal CT Using a Fully Automated Deep Learning Method, for Predicting Subsequent Cardiovascular Events.

CT-based body composition (BC) measurements have historically been too resource intensive to analyz...

MSFR-Net: Multi-modality and single-modality feature recalibration network for brain tumor segmentation.

BACKGROUND: Accurate and automated brain tumor segmentation from multi-modality MR images plays a si...

Deep learning of ECG waveforms for diagnosis of heart failure with a reduced left ventricular ejection fraction.

The performance and clinical implications of the deep learning aided algorithm using electrocardiogr...

DeepCNAP: A Deep Learning Approach for Continuous Noninvasive Arterial Blood Pressure Monitoring Using Photoplethysmography.

Arterial blood pressure (ABP) monitoring may permit the early diagnosis and management of cardiovasc...

Robust Fovea Detection in Retinal OCT Imaging Using Deep Learning.

The fovea centralis is an essential landmark in the retina where the photoreceptor layer is entirely...

Deep Learning Detects Changes Indicative of Axial Spondyloarthritis at MRI of Sacroiliac Joints.

Background MRI is frequently used for early diagnosis of axial spondyloarthritis (axSpA). However, e...

Machine Learning and Electrocardiography Signal-Based Minimum Calculation Time Detection for Blood Pressure Detection.

OBJECTIVE: Measurement and monitoring of blood pressure are of great importance for preventing disea...

A Novel Deep Learning and Ensemble Learning Mechanism for Delta-Type COVID-19 Detection.

Recently, the novel coronavirus disease 2019 (COVID-19) has posed many challenges to the research co...

Phenotypic screening with deep learning identifies HDAC6 inhibitors as cardioprotective in a BAG3 mouse model of dilated cardiomyopathy.

Dilated cardiomyopathy (DCM) is characterized by reduced cardiac output, as well as thinning and enl...

Photoplethysmogram based vascular aging assessment using the deep convolutional neural network.

Arterial stiffness due to vascular aging is a major indicator during the assessment of cardiovascula...

XGBoost Regression of the Most Significant Photoplethysmogram Features for Assessing Vascular Aging.

The purpose of this study was to confirm the potential of XGBoost as a vascular aging assessment mod...

Performance of artificial intelligence for biventricular cardiovascular magnetic resonance volumetric analysis in the clinical setting.

Cardiovascular magnetic resonance (CMR) derived ventricular volumes and function guide clinical deci...

Radiomics and deep learning for myocardial scar screening in hypertrophic cardiomyopathy.

BACKGROUND: Myocardial scar burden quantified using late gadolinium enhancement (LGE) cardiovascular...

Dynamic risk stratification using Markov chain modelling in patients with chronic heart failure.

AIMS: Risk changes with the progression of disease and the impact of treatment. We developed a dynam...

Fast and Efficient Method for Optical Coherence Tomography Images Classification Using Deep Learning Approach.

The use of optical coherence tomography (OCT) in medical diagnostics is now common. The growing amou...

End-systole and end-diastole detection in short axis cine MRI using a fully convolutional neural network with dilated convolutions.

The correct assessment and characterization of heart anatomy and functionality is usually done throu...

Empirical analyses and simulations showed that different machine and statistical learning methods had differing performance for predicting blood pressure.

Machine learning is increasingly being used to predict clinical outcomes. Most comparisons of differ...