Strokes

Real-Time Detection of Compensatory Patterns in Patients With Stroke to Reduce Compensation During Robotic Rehabilitation Therapy.

OBJECTIVES: Compensations are commonly employed by patients with stroke during rehabilitation without therapist supervision, leading to suboptimal recovery outcomes. This study investigated the feasibility of the real-time monitoring of compensation in patients with stroke by using pressure distribution data and machine learning algorithms. Whether trunk compensation can be reduced by combining th...

Deep Learning Detection of Penumbral Tissue on Arterial Spin Labeling in Stroke.

Background and Purpose- Selection of patients with acute ischemic stroke for endovascular treatment generally relies on dynamic susceptibility contrast magnetic resonance imaging or computed tomography perfusion. Dynamic susceptibility contrast magnetic resonance imaging requires injection of contrast, whereas computed tomography perfusion requires high doses of ionizing radiation. The purpose of ...

Development of a Novel Prognostic Model to Predict 6-Month Swallowing Recovery After Ischemic Stroke.

Background and Purpose- The aim of this study was to explore clinical and radiological prognostic factors for long-term swallowing recovery in patient...

Deep learning approaches for plethysmography signal quality assessment in the presence of atrial fibrillation.

OBJECTIVE: Photoplethysmography (PPG) monitoring has been implemented in many portable and wearable devices we use daily for health and fitness tracki...

Analyzing brain structural differences associated with categories of blood pressure in adults using empirical kernel mapping-based kernel ELM.

BACKGROUND: Hypertension increases the risk of angiocardiopathy and cognitive disorder. Blood pressure has four categories: normal, elevated, hyperten...

Intracatheter Tissue Plasminogen Activator for Chronic Subdural Hematomas after Failed Bedside Twist Drill Craniostomy: A Retrospective Review.

Introduction Chronic subdural hematomas (cSDH) are common in neurosurgery with various symptoms and significant morbidity and mortality. Treatment var...

Automatic segmentation of cerebral infarcts in follow-up computed tomography images with convolutional neural networks.

BACKGROUND AND PURPOSE: Infarct volume is a valuable outcome measure in treatment trials of acute ischemic stroke and is strongly associated with func...

Using machine learning models to improve stroke risk level classification methods of China national stroke screening.

BACKGROUND: With the character of high incidence, high prevalence and high mortality, stroke has brought a heavy burden to families and society in Chi...

Highly precise risk prediction model for new-onset hypertension using artificial intelligence techniques.

Hypertension is a significant public health issue. The ability to predict the risk of developing hypertension could contribute to disease prevention s...

A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images.

Automatic identification of brain lesions from magnetic resonance imaging (MRI) scans of stroke survivors would be a useful aid in patient diagnosis a...

Therapeutic Anticoagulation With an Ultra-Low Concentration Argatroban-Based Purge Solution for Percutaneous Ventricular Assist Device in Patient With Heparin-Induced Thrombocytopenia.

Percutaneous left ventricular assist devices (pVADs) require a continuous purge solution containing heparin to prevent pump thrombosis and device fai...

Orbit image analysis machine learning software can be used for the histological quantification of acute ischemic stroke blood clots.

Our aim was to assess the utility of a novel machine learning software (Orbit Image Analysis) in the histological quantification of acute ischemic str...

Recurrent ischemic stroke in patients with atrial fibrillation ablation and prior stroke: A study based on etiological classification.

BACKGROUND: Different subtypes of ischemic stroke may have different risk factors, clinical features, and prognoses. This study investigated the incid...

Potential of machine learning methods to identify patients with nonvalvular atrial fibrillation.

Nonvalvular atrial fibrillation (NVAF) is associated with an increased risk of stroke however many patients are diagnosed after onset. This study ass...

A novel computer-aided diagnosis system for the early detection of hypertension based on cerebrovascular alterations.

Hypertension is a leading cause of mortality in the USA. While simple tools such as the sphygmomanometer are widely used to diagnose hypertension, the...

[Predicting atrial fibrillation through a sinus-rhythm electrocardiogram; useful or not?].

In patients with cryptogenic stroke, the detection of atrial fibrillation (AF) is important, since it is an indication for the prescription of oral an...

The neural and neurocomputational bases of recovery from post-stroke aphasia.

Language impairment, or aphasia, is a disabling symptom that affects at least one third of individuals after stroke. Some affected individuals will sp...

Data-driven analyses of motor impairments in animal models of neurological disorders.

Behavior provides important insights into neuronal processes. For example, analysis of reaching movements can give a reliable indication of the degree...

Platelet-rich emboli are associated with von Willebrand factor levels and have poorer revascularization outcomes.

BACKGROUND AND AIMS: Platelets and von Willebrand factor (vWF) are key factors in thrombosis and thus are likely key components of acute ischemic stro...

Machine Learning-Based Forecast of Hemorrhagic Stroke Healthcare Service Demand considering Air Pollution.

This study aimed to forecast the pattern of the demand for hemorrhagic stroke healthcare services based on air quality and machine learning. Hemorrhag...