Strokes

Predictive modeling of preoperative acute heart failure in older adults with hypertension: a dual perspective of SHAP values and interaction analysis.

BACKGROUND: In older adults with hypertension, hip fractures accompanied by preoperative acute heart failure significantly elevate surgical risks and adverse outcomes, necessitating timely identification and management to improve patient outcomes.

Multimodal Machine Learning for Stroke Prognosis and Diagnosis: A Systematic Review.

Stroke is a life-threatening medical condition that could lead to mortality or significant sensorimotor deficits. Various machine learning techniques have been successfully used to detect and predict stroke-related outcomes. Considering the diversity in the type of clinical modalities involved during management of patients with stroke, such as medical images, bio-signals, and clinical data, multim...

Machine Learning-Based Prediction for Incident Hypertension Based on Regular Health Checkup Data: Derivation and Validation in 2 Independent Nationwide Cohorts in South Korea and Japan.

BACKGROUND: Worldwide, cardiovascular diseases are the primary cause of death, with hypertension as a key contributor. In 2019, cardiovascular disease...

Identification of novel hypertension biomarkers using explainable AI and metabolomics.

BACKGROUND: The global incidence of hypertension, a condition of elevated blood pressure, is rising alarmingly. According to the World Health Organiza...

A cross-attention-based deep learning approach for predicting functional stroke outcomes using 4D CTP imaging and clinical metadata.

Acute ischemic stroke (AIS) remains a global health challenge, leading to long-term functional disabilities without timely intervention. Spatio-tempor...

Brain Activation Pattern Caused by Soft Rehabilitation Glove and Virtual Reality Scenes: A Pilot fNIRS Study.

Clinical studies have proved significant improvements in hand motor function in stroke patients when assisted by robotic devices. However, there were ...

SDS-Net: A Synchronized Dual-Stage Network for Predicting Patients Within 4.5-h Thrombolytic Treatment Window Using MRI.

Timely and precise identification of acute ischemic stroke (AIS) within 4.5 h is imperative for effective treatment decision-making. This study aims t...

Deep Learning Using One-stop-shop CT Scan to Predict Hemorrhagic Transformation in Stroke Patients Undergoing Reperfusion Therapy: A Multicenter Study.

RATIONALE AND OBJECTIVES: Hemorrhagic transformation (HT) is one of the most serious complications in patients with acute ischemic stroke (AIS) follow...

Charting the growth through intelligence: A SWOC analysis on AI-assisted radiologic bone age estimation.

Bone age estimation (BAE) is based on skeletal maturity and degenerative process of the skeleton. The clinical importance of BAE is in understanding t...

Automated Extraction of Stroke Severity From Unstructured Electronic Health Records Using Natural Language Processing.

BACKGROUND: Multicenter electronic health records can support quality improvement and comparative effectiveness research in stroke. However, limitatio...

Predicting laboratory aspirin resistance in Chinese stroke patients using machine learning models by GP1BA polymorphism.

This study aims to use machine learning model to predict laboratory aspirin resistance (AR) in Chinese stroke patients by incorporating patient charac...

A Study on Prevalence and Factors Affecting Hypertension in an Iranian Population: Results from the Fasa Cohort Study.

BACKGROUND: In recent years, hypertension has been one of the most important noncommunicable diseases worldwide. In this context, identifying the pred...

Enhancing amide proton transfer imaging in ischemic stroke using a machine learning approach with partially synthetic data.

Amide proton transfer (APT) imaging, a technique sensitive to tissue pH, holds promise in the diagnosis of ischemic stroke. Achieving accurate and rap...

Diagnostic performance of single-lead electrocardiograms for arterial hypertension diagnosis: a machine learning approach.

Awareness and early identification of hypertension is crucial in reducing the burden of cardiovascular disease (CVD). Artificial intelligence-based an...

Deep learning assists early-detection of hypertension-mediated heart change on ECG signals.

Arterial hypertension is a major risk factor for cardiovascular diseases. While cardiac ultrasound is a typical way to diagnose hypertension-mediated ...

Advancing Vascular Surgery: The Role Of Artificial Intelligence And Machine Learning In Managing Carotid Stenosis.

INTRODUCTION: Cardiovascular diseases affect 17.7 million people annually, worldwide. Carotid degenerative disease, commonly described as atherosclero...

Evaluating retinal blood vessels for predicting white matter hyperintensities in ischemic stroke: A deep learning approach.

OBJECTIVE: This study aims to investigate whether a deep learning approach incorporating retinal blood vessels can effectively identify ischemic strok...

The role of artificial intelligence in optimizing management of atrial fibrillation in acute ischemic stroke.

Atrial fibrillation (AF) is a severe condition associated with high morbidity and mortality, including an increased risk of stroke and poor outcomes p...

Deep Learning Classification of Ischemic Stroke Territory on Diffusion-Weighted MRI: Added Value of Augmenting the Input with Image Transformations.

Our primary aim with this study was to build a patient-level classifier for stroke territory in DWI using AI to facilitate fast triage of stroke to a ...

Predicting Individual Treatment Effects to Determine Duration of Dual Antiplatelet Therapy After Stent Implantation.

BACKGROUND: After coronary stent implantation, prolonged dual antiplatelet therapy (DAPT) increases bleeding risk, requiring personalization of DAPT d...