Strokes

Risk for ocular hypertension progression to early glaucoma: A predictive model and key predictors.

BACKGROUND: Ocular hypertension (OHT) is the most significant risk factor for glaucoma. We aimed to develop a model for predicting OHT progression to early glaucoma and to identify key predictors.

Application and optimization of the U-Net++ model for cerebral artery segmentation based on computed tomographic angiography images.

Accurate segmentation of cerebral arteries on computed tomography angiography (CTA) images is essential for the diagnosis and management of cerebrovascular diseases, including ischemic stroke. This study implemented a deep learning-based U-Net++ model for cerebral artery segmentation in CTA images, focusing on optimizing pruning levels by analyzing the trade-off between segmentation performance an...

Evaluation of Perioperative Cardiovascular Event Risk in Gastrointestinal Surgery　- Predictive Modeling and Risk Stratification Using Machine Learning.

BACKGROUND: Preoperative risk assessment is very important to ensure surgical safety and predict postoperative complications. However, no large-scale ...

Exploring hypoxia driven subtypes of pulmonary arterial hypertension through transcriptomics single cell sequencing and machine learning.

Pulmonary arterial hypertension (PAH) is a progressive cardiovascular disease characterized by elevated pulmonary arterial pressure, leading to right ...

Mechanism Exploration of Dietary Supplement Astaxanthin on Improving Atherosclerosis through an Integrated Strategy Encompassing Artificial Intelligence Virtual Screening and Experimental Validation.

Atherosclerosis (AS) is a major and common pathological basis of ischemic intestinal infarction, myocardial infarction, stroke, renal failure, and oth...

Integrating WGCNA and SVM-RFE identifies hub molecular biomarkers driving ischemic stroke progression.

BACKGROUND: Stroke is the second most common cause of death worldwide and the leading cause of long-term severe disability with neurological impairmen...

Post-stroke spontaneous motor recovery in mice can be predicted from acute-phase local field potential using machine learning.

Stroke remains a leading cause of long-term disability, underscoring the urgent need for effective predictors of motor recovery. Understanding the ele...

A wearable ankle-assisted robot for improving gait function and pattern in stroke patients.

BACKGROUND: Hemiplegic gait after a stroke can result in a decreased gait speed and asymmetrical gait pattern. Normal gait patterns and speed are typi...

Bioanalysis of antihypertensive drugs by LC-MS: a fleeting look at the regulatory guidelines and artificial intelligence.

Hypertension is a multifaceted cardiovascular disease, a significant risk factor for stroke, heart attack, heart failure, and renal damage. An essenti...

Diagnostic MicroRNA Signatures to Support Classification of Pulmonary Hypertension.

BACKGROUND: Patients with pulmonary hypertension (PH) are classified based on disease pathogenesis and hemodynamic drivers. Classification informs tre...

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients.

This study introduces a Brain-Computer Interface (BCI)-controlled upper limb assistive robot for post-stroke rehabilitation. The system utilizes elect...

Integrative neurorehabilitation using brain-computer interface: From motor function to mental health after stroke.

Stroke remains a leading cause of mortality and long-term disability worldwide, frequently resulting in impairments in motor control, cognition, and e...

Machine Learning-Based Prediction of Unplanned Readmission Due to Major Adverse Cardiac Events Among Hospitalized Patients with Blood Cancers.

BackgroundHospitalized patients with blood cancer face an elevated risk for cardiovascular diseases caused by cardiotoxic cancer therapies, which can ...

Predicting Intraoperative Burst Suppression Using Preoperative EEG and Patient Characteristics.

Burst suppression (BS) is an electroencephalogram (EEG) pattern observed in patients undergoing general anesthesia. The occurrence of BS is associated...

Framingham Risk Score Prediction at 12 Months in the STANDFIRM Randomized Control Trial.

BACKGROUND: The STANDFIRM (Shared Team Approach Between Nurses and Doctors for Improved Risk Factor Management; ANZCTR registration ACTRN1260800016637...

A deep learning approach for quantifying CT perfusion parameters in stroke.

. Computed tomography perfusion (CTP) imaging is widely used for assessing acute ischemic stroke. However, conventional methods for quantifying CTP im...

Predicting cerebral infarction and transient ischemic attack in healthy individuals and those with dysmetabolism: a machine learning approach combined with routine blood tests.

Ischemic cerebral infarction is the most prevalent type of stroke, causing significant disability and death worldwide. Transient ischemic attack (TIA)...

Comparing machine learning models for predicting preoperative DVT incidence in elderly hypertensive patients with hip fractures: a retrospective analysis.

Hip fractures in the elderly present a significant public health challenge globally, especially among patients with hypertension, who are at an increa...

A dual-branch hybrid network with bilateral-difference awareness for collateral scoring on CT angiography of acute ischemic stroke patients.

Acute ischemic stroke (AIS) patients with good collaterals tend to have better outcomes after endovascular therapy. Existing collateral scoring method...

Risk Factors and Outcomes of Hemorrhagic Transformation in Acute Ischemic Stroke Following Thrombolysis: Analysis of a Single-Center Experience and Review of the Literature.

: This is a retrospective study conducted at the Clinical County Hospital of Craiova, Romania, providing valuable insights into hemorrhagic transforma...