Congestive Heart Failure

Segmentation of the aorta in systolic phase from 4D flow MRI: multi-atlas vs. deep learning.

OBJECTIVE: In the management of the aortic aneurysm, 4D flow magnetic resonance Imaging provides valuable information for the computation of new biomarkers using computational fluid dynamics (CFD). However, accurate segmentation of the aorta is required. Thus, our objective is to evaluate the performance of two automatic segmentation methods on the calculation of aortic wall pressure.

Deep learning automates bidimensional and volumetric tumor burden measurement from MRI in pre- and post-operative glioblastoma patients.

Tumor burden assessment by magnetic resonance imaging (MRI) is central to the evaluation of treatment response for glioblastoma. This assessment is, however, complex to perform and associated with high variability due to the high heterogeneity and complexity of the disease. In this work, we tackle this issue and propose a deep learning pipeline for the fully automated end-to-end analysis of gliobl...

Deep learning-based diagnosis of feline hypertrophic cardiomyopathy.

Feline hypertrophic cardiomyopathy (HCM) is a common heart disease affecting 10-15% of all cats. Cats with HCM exhibit breathing difficulties, letharg...

The predictive value of deep learning-based cardiac ultrasound flow imaging for hypertrophic cardiomyopathy complicating arrhythmias.

OBJECTIVE: To investigate the predictive value of deep learning-based cardiac ultrasound flow imaging for hypertrophic cardiomyopathy (HCM) complicate...

NVTrans-UNet: Neighborhood vision transformer based U-Net for multi-modal cardiac MR image segmentation.

With the rapid development of artificial intelligence and image processing technology, medical imaging technology has turned into a critical tool for ...

Reliability of respiratory-gated real-time two-dimensional cine incorporating deep learning reconstruction for the assessment of ventricular function in an adult population.

This study aimed to assess the image quality and accuracy of respiratory-gated real-time two-dimensional (2D) cine incorporating deep learning reconst...

An end-end deep learning framework for lesion segmentation on multi-contrast MR images-an exploratory study in a rat model of traumatic brain injury.

Traumatic brain injury (TBI) engenders traumatic necrosis and penumbra-areas of secondary neural injury which are crucial targets for therapeutic inte...

A deep learning framework assisted echocardiography with diagnosis, lesion localization, phenogrouping heterogeneous disease, and anomaly detection.

Echocardiography is the first-line diagnostic technique for heart diseases. Although artificial intelligence techniques have made great improvements i...

A deep learning platform to assess drug proarrhythmia risk.

Drug safety initiatives have endorsed human iPSC-derived cardiomyocytes (hiPSC-CMs) as an in vitro model for predicting drug-induced cardiac arrhythmi...

Robotic Redo Mitral Valve Replacement and Atrioventricular Groove Pseudoaneurysm Repair.

We report the use of robot-assisted right thoracotomy in the management of a patient who presented with acute-on-chronic congestive heart failure, ass...

A machine learning approach for predicting perihematomal edema expansion in patients with intracerebral hemorrhage.

OBJECTIVES: Preventing the expansion of perihematomal edema (PHE) represents a novel strategy for the improvement of neurological outcomes in intracer...

Aortic Distensibility Measured by Automated Analysis of Magnetic Resonance Imaging Predicts Adverse Cardiovascular Events in UK Biobank.

Background Automated analysis of cardiovascular magnetic resonance images provides the potential to assess aortic distensibility in large populations....

Research on Assistant Diagnosis of Fundus Optic Neuropathy Based on Deep Learning.

PURPOSE: The purpose of this study was to use the neural network to distinguish optic edema (ODE), and optic atrophy from normal fundus images and try...

Natural language processing for identification of hypertrophic cardiomyopathy patients from cardiac magnetic resonance reports.

BACKGROUND: Cardiac magnetic resonance (CMR) imaging is important for diagnosis and risk stratification of hypertrophic cardiomyopathy (HCM) patients....

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 increased the use of cardiovascular imaging for the asses...

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

PURPOSE OF REVIEW: Artificial intelligence (AI) techniques have the potential to remarkably change the practice of cardiology in order to improve and ...

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 synthesize clinically useful optical coherence tomogr...

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 analyze for widespread use and have lacked robust compar...

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 significant role in tumor treatment. However, the ex...