Cardiovascular

Towards a diagnostic tool for neurological gait disorders in childhood combining 3D gait kinematics and deep learning.

Gait abnormalities are frequent in children and can be caused by different pathologies, such as cere...

The downtrending cost of robotic bariatric surgery: a cost analysis of 47,788 bariatric patients.

The surgical robot is assumed to be a fixed, indirect cost. We hypothesized rising volume of robotic...

Applicable Machine Learning Model for Predicting Contrast-induced Nephropathy Based on Pre-catheterization Variables.

Artificial intelligence in academic cardiothoracic surgery.

Scale based entropy measures and deep learning methods for analyzing the dynamical characteristics of cardiorespiratory control system in COVID-19 subjects during and after recovery.

COVID-19, known as Coronavirus Disease 2019 primarily targets the respiratory system and can impact ...

Deep learning approach for automated segmentation of myocardium using bone scintigraphy single-photon emission computed tomography/computed tomography in patients with suspected cardiac amyloidosis.

BACKGROUND: We employed deep learning to automatically detect myocardial bone-seeking uptake as a ma...

Scalar invariant transform based deep learning framework for detecting heart failures using ECG signals.

Heart diseases are leading to death across the globe. Exact detection and treatment for heart diseas...

VITAMIN D DEFICIENCY AS AN INDEPENDENT PREDICTOR OF CARDIOVASCULAR DISEASE.

CONTEXT: In addition to traditional risk factors for cardiovascular diseases, there are new risk fac...

Molecular Investigation and Preliminary Validation of Candidate Genes Associated with Neurological Damage in Heat Stroke.

Heat stroke (HS) is a severe medical condition characterized by a systemic inflammatory response tha...

CT angiography prior to endovascular procedures: can artificial intelligence improve reporting?

CT angiography prior to endovascular aortic surgery is the standard non-invasive imaging method for ...

Incorporating longitudinal history of risk factors into atherosclerotic cardiovascular disease risk prediction using deep learning.

It is increasingly clear that longitudinal risk factor levels and trajectories are related to risk f...

Point-Of-Care low-field MRI in acute Stroke (POCS): protocol for a multicentric prospective open-label study evaluating diagnostic accuracy.

INTRODUCTION: Fast and accurate diagnosis of acute stroke is crucial to timely initiate reperfusion ...

Reminiscent music therapy combined with robot-assisted rehabilitation for elderly stroke patients: a pilot study.

BACKGROUND: Although some studies suggest that robot-assisted technology can significantly improve u...

Improved image quality in CT pulmonary angiography using deep learning-based image reconstruction.

We investigated the effect of deep learning-based image reconstruction (DLIR) compared to iterative ...

Deep learning-based white matter lesion volume on CT is associated with outcome after acute ischemic stroke.

BACKGROUND: Intravenous thrombolysis (IVT) before endovascular treatment (EVT) for acute ischemic st...

Analysis of tricuspid annulus dimensions and RCA-proximity with artificial intelligence-based software for procedural planning of percutaneous tricuspid annuloplasty.

Using Deep Learning and B-Splines to Model Blood Vessel Lumen from 3D Images.

Accurate geometric modeling of blood vessel lumen from 3D images is crucial for vessel quantificatio...

CLINet: A novel deep learning network for ECG signal classification.

Machine learning is poised to revolutionize medicine with algorithms that spot cardiac arrhythmia. A...

M2AI-CVD: Multi-modal AI approach cardiovascular risk prediction system using fundus images.

Cardiovascular diseases (CVD) represent a significant global health challenge, often remaining undet...

Identification of high-risk imaging features in hypertrophic cardiomyopathy using electrocardiography: A deep-learning approach.

BACKGROUND: Patients with hypertrophic cardiomyopathy (HCM) are at risk of sudden death, and individ...

Development and validation of outcome prediction model for reperfusion therapy in acute ischemic stroke using nomogram and machine learning.

OBJECTIVE: To develop logistic regression nomogram and machine learning (ML)-based models to predict...