Strokes

Temporally downsampled cerebral CT perfusion image restoration using deep residual learning.

PURPOSE: Acute ischemic stroke is one of the most causes of death all over the world. Onset to treatment time is critical in stroke diagnosis and treatment. Considering the time consumption and high price of MR imaging, CT perfusion (CTP) imaging is strongly recommended for acute stroke. However, too much CT radiation during CTP imaging may increase the risk of health problems. How to reduce CT ra...

Development of a deep learning model to identify hyperdense MCA sign in patients with acute ischemic stroke.

PURPOSE: The aim of this study was to develop an interactive deep learning-assisted identification of the hyperdense middle cerebral artery (MCA) sign (HMCAS) on non-contrast computed tomography (CT) among patients with acute ischemic stroke.

Automated label-free detection of injured neuron with deep learning by two-photon microscopy.

Stroke is a significant cause of morbidity and long-term disability globally. Detection of injured neuron is a prerequisite for defining the degree of...

A machine learning approach for the prediction of pulmonary hypertension.

BACKGROUND: Machine learning (ML) is a powerful tool for identifying and structuring several informative variables for predictive tasks. Here, we inve...

Artificial Intelligence Meets Chinese Medicine.

As an interdisciplinary subject of medicine and artificial intelligence, intelligent diagnosis and treatment has received extensive attention. The sta...

A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical dataset.

BACKGROUND AND OBJECTIVE: Cerebral stroke has become a significant global public health issue in recent years. The ideal solution to this concern is t...

A New Approach for Brain Tumor Segmentation and Classification Based on Score Level Fusion Using Transfer Learning.

Brain tumor is one of the most death defying diseases nowadays. The tumor contains a cluster of abnormal cells grouped around the inner portion of hum...

Data-efficient deep learning of radiological image data for outcome prediction after endovascular treatment of patients with acute ischemic stroke.

Treatment selection is becoming increasingly more important in acute ischemic stroke patient care. Clinical variables and radiological image biomarker...

Training for Walking Efficiency With a Wearable Hip-Assist Robot in Patients With Stroke: A Pilot Randomized Controlled Trial.

Background and Purpose- The purpose of this study was to investigate the effects of gait training with a newly developed wearable hip-assist robot on ...

Extreme Gradient Boosting Model Has a Better Performance in Predicting the Risk of 90-Day Readmissions in Patients with Ischaemic Stroke.

OBJECT: Ischemic stroke readmission within 90 days of hospital discharge is an important quality of care metric. The readmission rates of ischemic str...

Atrial Fibrillation Burden Signature and Near-Term Prediction of Stroke: A Machine Learning Analysis.

BACKGROUND: Atrial fibrillation (AF) increases the risk of stroke 5-fold and there is rising interest to determine if AF severity or burden can furthe...

An application of machine learning with feature selection to improve diagnosis and classification of neurodegenerative disorders.

BACKGROUND: The analysis of health and medical data is crucial for improving the diagnosis precision, treatments and prevention. In this field, machin...

Acute ischemic stroke lesion core segmentation in CT perfusion images using fully convolutional neural networks.

The use of Computed Tomography (CT) imaging for patients with stroke symptoms is an essential step for triaging and diagnosis in many hospitals. Howev...

Artificial intelligence to diagnose ischemic stroke and identify large vessel occlusions: a systematic review.

BACKGROUND AND PURPOSE: Acute stroke caused by large vessel occlusions (LVOs) requires emergent detection and treatment by endovascular thrombectomy. ...

Applying density-based outlier identifications using multiple datasets for validation of stroke clinical outcomes.

INTRODUCTION: Clinicians commonly use the modified Rankin Scale (mRS) and the Barthel Index (BI) to measure clinical outcome after stroke. These are p...

Predicting post-stroke pneumonia using deep neural network approaches.

BACKGROUND AND PURPOSE: Pneumonia is a common complication after stroke, causing an increased length of hospital stay and death. Therefore, the timely...

Clinical Study on Minimally Invasive Liquefaction and Drainage of Hypertensive Putaminal Hemorrhage through Frontal Approach.

 Hypertensive intracerebral hemorrhage is one of the most common cerebrovascular diseases with high mortality and high disability rate. The aim of th...

Label Self-Advised Support Vector Machine (LSA-SVM)-Automated Classification of Foot Drop Rehabilitation Case Study.

Stroke represents a major health problem in our society. One of the effects of stroke is foot drop. Foot drop (FD) is a weakness that occurs in specif...

Machine Learning-Enabled Automated Determination of Acute Ischemic Core From Computed Tomography Angiography.

Background and Purpose- The availability of and expertise to interpret advanced neuroimaging recommended in the guideline-based endovascular stroke th...

Robotic Exoskeleton for Wrist and Fingers Joint in Post-Stroke Neuro-Rehabilitation for Low-Resource Settings.

Robots have the potential to help provide exercise therapy in a repeatable and reproducible manner for stroke survivors. To facilitate rehabilitation ...