Strokes

Image Features of Magnetic Resonance Angiography under Deep Learning in Exploring the Effect of Comprehensive Rehabilitation Nursing on the Neurological Function Recovery of Patients with Acute Stroke.

This study was to explore the effects of imaging characteristics of magnetic resonance angiography (MRA) based on deep learning on the comprehensive rehabilitation nursing on the neurological recovery of patients with acute stroke. In this study, 84 patients with acute stroke who were treated in hospital were selected as the research objects, and they were rolled into a control group (routine care...

Deep Learning-Based Automated Thrombolysis in Cerebral Infarction Scoring: A Timely Proof-of-Principle Study.

BACKGROUND AND PURPOSE: Mechanical thrombectomy is an established procedure for treatment of acute ischemic stroke. Mechanical thrombectomy success is commonly assessed by the Thrombolysis in Cerebral Infarction (TICI) score, assigned by visual inspection of X-ray digital subtraction angiography data. However, expert-based TICI scoring is highly observer-dependent. This represents a major obstacle...

A network of core and subtype-specific gene expression programs in myositis.

Myositis comprises a heterogeneous group of skeletal muscle disorders which converge on chronic muscle inflammation and weakness. Our understanding of...

MTANS: Multi-Scale Mean Teacher Combined Adversarial Network with Shape-Aware Embedding for Semi-Supervised Brain Lesion Segmentation.

The annotation of brain lesion images is a key step in clinical diagnosis and treatment of a wide spectrum of brain diseases. In recent years, segment...

Identifying the predictive effectiveness of a genetic risk score for incident hypertension using machine learning methods among populations in rural China.

Current studies have shown the controversial effect of genetic risk scores (GRSs) in hypertension prediction. Machine learning methods are used extens...

Vaso reactivity test using inhaled nitric oxide for pulmonary arterial hypertension accompanied by severe interstitial lung disease attributed to systemic sclerosis: A case report.

A 70-year-old man with severe interstitial pneumonia attributed to limited cutaneous systemic sclerosis was referred to our institution because of wor...

Machine learning models of ischemia/hemorrhage in moyamoya disease and analysis of its risk factors.

OBJECT: This study aimed to determine the risk factors of ischemic/hemorrhagic stroke in patients suffering moyamoya disease (MMD), as well as to comp...

Development of a machine learning-based real-time location system to streamline acute endovascular intervention in acute stroke: a proof-of-concept study.

BACKGROUND: Delivery of acute stroke endovascular intervention can be challenging because it requires complex coordination of patient and staff across...

Quantifying changes over 1 year in motor and cognitive skill after transient ischemic attack (TIA) using robotics.

Recent work has highlighted that people who have had TIA may have abnormal motor and cognitive function. We aimed to quantify deficits in a cohort of ...

Multilevel Strip Pooling-Based Convolutional Neural Network for the Classification of Carotid Plaque Echogenicity.

Carotid plaque echogenicity in ultrasound images has been found to be closely correlated with the risk of stroke in atherosclerotic patients. The auto...

Artificial Inteligence-Based Decision for the Prediction of Cardioembolism in Patients with Chagas Disease and Ischemic Stroke.

BACKGROUND: Chagas disease (CD) and ischemic stroke (IS) have a close, but poorly understood, association. There is paucity of evidence on the ideal s...

A Machine Learning Approach to Predict Acute Ischemic Stroke Thrombectomy Reperfusion using Discriminative MR Image Features.

Mechanical thrombectomy (MTB) is one of the two standard treatment options for Acute Ischemic Stroke (AIS) patients. Current clinical guidelines instr...

AIFNet: Automatic vascular function estimation for perfusion analysis using deep learning.

Perfusion imaging is crucial in acute ischemic stroke for quantifying the salvageable penumbra and irreversibly damaged core lesions. As such, it help...

Deep Learning-Based Measurement of Total Plaque Area in B-Mode Ultrasound Images.

Measurement of total-plaque-area (TPA) is important for determining long term risk for stroke and monitoring carotid plaque progression. Since delinea...

CAFT: a deep learning-based comprehensive abdominal fat analysis tool for large cohort studies.

BACKGROUND: There is increasing appreciation of the association of obesity beyond co-morbidities, such as cancers, Type 2 diabetes, hypertension, and ...

Hybrid deep learning segmentation models for atherosclerotic plaque in internal carotid artery B-mode ultrasound.

The automated and accurate carotid plaque segmentation in B-mode ultrasound (US) is an essential part of stroke risk stratification. Previous segmente...

Machine-Learning-Derived Model for the Stratification of Cardiovascular risk in Patients with Ischemic Stroke.

UNLABELLED: Background Stratification of cardiovascular risk in patients with ischemic stroke is important as it may inform management strategies. We ...

Beyond motor recovery after stroke: The role of hand robotic rehabilitation plus virtual reality in improving cognitive function.

Robot-assisted hand training adopting end-effector devices results in an additional reduction of motor impairment in comparison to usual care alone in...

Effects of periodic robot rehabilitation using the Hybrid Assistive Limb for a year on gait function in chronic stroke patients.

Using a robot for gait training in stroke patients has attracted attention for the last several decades. Previous studies reported positive effects of...

A robot goes to rehab: a novel gamified system for long-term stroke rehabilitation using a socially assistive robot-methodology and usability testing.

BACKGROUND: Socially assistive robots (SARs) have been proposed as a tool to help individuals who have had a stroke to perform their exercise during t...