Strokes

Low atherogenic index of plasma is associated with spontaneous hemorrhagic transformation in acute ischemic stroke patients: a retrospective cohort study and predictive modeling based on machine learning.

Predicting infarct outcomes after extended time window thrombectomy in large vessel occlusion using knowledge guided deep learning.

BACKGROUND: Predicting the final infarct after an extended time window mechanical thrombectomy (MT) is beneficial for treatment planning in acute ischemic stroke (AIS). By introducing guidance from prior knowledge, this study aims to improve the accuracy of the deep learning model for post-MT infarct prediction using pre-MT brain perfusion data. METHODS: This retrospective study collected CT perfu...

Automated CTA-Derived Collateral Grading and Morphologic Metrics for Enhanced Prediction of Post-Stroke Outcomes.

BACKGROUND AND PURPOSE: Collateral circulation is a key determinant of treatment response and outcomes in acute ischemic stroke (AIS), yet its assessm...

The Quality of Artificial Intelligence Chatbot Responses to Patient Questions on Surgical Infection Prevention.

BACKGROUND: As patients increasingly turn to artificial intelligence (AI) chatbots for medical information, concerns remain regarding the accuracy, tr...

Evolutionary-Based Deep Learning Network Model using Adaptive Mixing Differential Evolution and Application in Acute Pulmonary Embolism.

INTRODUCTION: Acute pulmonary embolism (APE) is characterized by high incidence and mortality, along with non-specific clinical manifestations. Its co...

Language-Related Functional Connectivity in Post-Stroke Aphasia: Preliminary Findings from a Graph-Theoretical and Interpretable Machine Learning Study.

PURPOSE: To have an insight into language-related functional connectivity in post-stroke aphasia (PSA) from graph theory measurements when performing ...

Cross-Platform Proteomics and Machine Learning Algorithms Nominate Plasma Biomarkers of Stroke Diagnosis.

BACKGROUND: Blood-based biomarkers for stroke subtyping could improve triage in emergency settings. We used cross-platform proteomics to identify plas...

Enhanced Prediction of Atrial Fibrillation in Patients With Ischemic Stroke Through Electronic Medical Records and Text Mining: Algorithm Development and Validation.

BACKGROUND: Stroke remains one of the leading causes of mortality and long-term disability worldwide. Atrial fibrillation (AF) is a major and often un...

Clinical Impact of Postrecanalization Hemorrhagic Transformation and Its Prediction Using Baseline Noncontrast CT.

BACKGROUND: Hemorrhagic transformation (HT) after recanalization therapy remains a critical concern in acute ischemic stroke management. While severe ...

Effects of long-term exposure to nitrogen dioxide with cardiovascular mortality: Evidence from a nationwide cohort study.

Nitrogen dioxide (NO2) is a significant ambient pollutant whose independent health effects have not been well confirmed. Evidence on the relationship ...

Identifying key gait features in stroke patients using wearable inertial sensors and supervised and unsupervised machine learning.

Stroke is a major cause of motor disability, degrading walking and quality of life. Wearable gait analysis with magneto-inertial measurement units (MI...

Precise oxygen therapy to emphysema patients by fuzzy-based gain tuning control of set-point regulated MRAC.

Emphysema, a primary component of chronic obstructive pulmonary disease (COPD), causes progressive dyspnea through the destruction of alveolar membran...

Preoperative Risk Score for Mortality Within 3 Years of Elective Aortobifemoral Artery Bypass.

BACKGROUND: The purpose of this study was to create a risk score for mortality within 3 years of elective aortobifemoral artery bypass for aortoiliac ...

Mixture-of-Skip-Connection Deep Learning Model to Classify Stroke Severity from Diffusion Weighted Imaging Based on NIHSS.

The National Institutes of Health Stroke Scale (NIHSS) is a quantitative tool, grading neurological deficits and guiding acute stroke management; howe...

Single-cell and multi-omics analysis identifies mitophagy-related biomarkers and therapeutic targets in ischemic stroke.

Ischemic stroke (IS) remains a leading cause of death and disability, with limited effective treatments in the acute phase. Mitophagy, the selective d...

Integrated prognostic evaluation of TyG and TyG-BMI in critically ill patients with comorbid atrial fibrillation and ischemic stroke using machine learning.

PAH-former: Transfer learning for efficient discovery of pulmonary arterial hypertension-associated genes.

BACKGROUND AND AIMS: Pulmonary arterial hypertension (PAH) is a severe disease with limited effective therapies, making the discovery of new therapeut...

Automated detection of new cerebral infarctions and prognostic implications using deep learning on serial MRI.

We developed and externally validated a deep learning model to automatically detect new ischemic lesions on serial FLAIR MRI scans in patients with st...

Time-frequency-spatial channel attention network for semantic decoding: an exploratory EEG study.

Semantic decoding is a crucial approach for investigating the neural mechanisms underlying language processing and representation. Informed by brain-c...

Heterogeneous associations of a mobile health-based disease management program on uncontrolled hypertension: A target trial emulation study.

Long-term effectiveness of digital health interventions for hypertension remains unclear, particularly regarding individual variability in treatment r...