Cardiovascular

Strokes

Latest AI and machine learning research in strokes for healthcare professionals.

3,008 articles
Stay Ahead - Weekly Strokes research updates
Subscribe
Browse Categories
Showing 1156-1176 of 3,008 articles
Integrating uncertainty in deep neural networks for MRI based stroke analysis.

At present, the majority of the proposed Deep Learning (DL) methods provide point predictions withou...

Cuffless Blood Pressure Monitoring: Promises and Challenges.

Current BP measurements are on the basis of traditional BP cuff approaches. Ambulatory BP monitoring...

Fully automated quantification of left ventricular volumes and function in cardiac MRI: clinical evaluation of a deep learning-based algorithm.

To investigate the performance of a deep learning-based algorithm for fully automated quantification...

Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph for the differential diagnosis of dyspnea.

Dyspnea is one of the most common manifestations of patients with pulmonary disease, myocardial dysf...

Machine learning-based segmentation of ischemic penumbra by using diffusion tensor metrics in a rat model.

BACKGROUND: Recent trials have shown promise in intra-arterial thrombectomy after the first 6-24 h o...

Robotic assessment of rapid motor decision making in children with perinatal stroke.

BACKGROUND: Activities of daily living frequently require children to make rapid decisions and execu...

Future possibilities for artificial intelligence in the practical management of hypertension.

The use of artificial intelligence in numerous prediction and classification tasks, including clinic...

A Machine Learning Approach for Predicting Early Phase Postoperative Hypertension in Patients Undergoing Carotid Endarterectomy.

BACKGROUND: This study aimed to establish and validate a machine learning-based model for the predic...

Identifying diagnosis evidence of cardiogenic stroke from Chinese echocardiograph reports.

BACKGROUND: Cardiogenic stroke has increasing morbidity in China and brought economic burden to pati...

Using diffusion tensor imaging to detect cortical changes in fronto-temporal dementia subtypes.

Fronto-temporal dementia (FTD) is a common type of presenile dementia, characterized by a heterogene...

A novel optimized repeatedly random undersampling for selecting negative samples: A case study in an SVM-based forest fire susceptibility assessment.

The negative sample selection method is a key issue in studies of using machine learning approaches ...

Comparison study of classification methods of intramuscular electromyography data for non-human primate model of traumatic spinal cord injury.

Traumatic spinal cord injury is a serious neurological disorder. Patients experience a plethora of s...

An artificial neural network approach for predicting hypertension using NHANES data.

This paper focus on a neural network classification model to estimate the association among gender, ...

Continuous blood pressure measurement from one-channel electrocardiogram signal using deep-learning techniques.

Continuous blood pressure (BP) measurement is crucial for reliable and timely hypertension detection...

Robot-assisted therapy for upper-limb rehabilitation in subacute stroke patients: A systematic review and meta-analysis.

BACKGROUND: Stroke survivors often experience upper-limb motor deficits and achieve limited motor re...

A machine learning approach to select features important to stroke prognosis.

Ischemic stroke is a common neurological disorder, and is still the principal cause of serious long-...

Efficacy of enoxaparin in preventing coagulation during high-flux haemodialysis, expanded haemodialysis and haemodiafiltration.

BACKGROUND: Low-molecular-weight heparins (LMWHs) are easily dialysable with high-flow membranes; ho...

Machine learning and natural language processing methods to identify ischemic stroke, acuity and location from radiology reports.

Accurate, automated extraction of clinical stroke information from unstructured text has several imp...

How Will Machine Learning Inform the Clinical Care of Atrial Fibrillation?

Machine learning applications in cardiology have rapidly evolved in the past decade. With the availa...

Browse Categories