Strokes

Balancing Innovation and Responsibility: Ethical and Privacy Challenges in Stroke Digital Health.

Digital health solutions are being increasingly used to support care delivery across various medical fields. In stroke care, technology has contributed to enhancing prehospital workflows, facilitating communication among health care professionals, and enabling fast-track systems for hyperacute stroke management. Furthermore, digital tools are used to improve etiological diagnosis and allow for mor...

A comparative analysis of SCALE-CTP and RAPID in determining the affected brain volumes of patients with ischemic stroke.

BACKGROUND: Computed tomography perfusion (CTP) is widely used to treat acute strokes. The affected brain volume, measured based on the threshold value in the calculated CTP map, has helped guide treatment decisions. We proposed a new software program developed for CTP analysis, SCALE-CTP, and compared the affected brain volumes estimated using RAPID and SCALE-CTP. METHODS: We recruited 362 indivi...

Precision medicine in Moyamoya vasculopathy.

PURPOSE OF REVIEW: Moyamoya vasculopathy is a progressive cerebrovascular steno-occlusive disease with variable presentation. As revascularization tec...

Effects of Robot-Assisted Upper Extremity Training on Cognitive and Physical Functions in Patients With Stroke: A Randomized Controlled Study.

OBJECTIVE: Primary aim was to investigate the effects of upper extremity robot-assisted training, applied in addition to conventional rehabilitation p...

Creating Virtual Stroke Networks: Current and Future Role of Artificial Intelligence, Mobile Imaging Applications, and Telehealth in Triage and Treatment of Acute Ischemic Stroke: A Scientific Statement From the American Heart Association: The American Academy of Neurology affirms the value of this statement as an educational tool for neurologists.

Rapid evaluation, triage, and transport of patients with stroke for thrombolytics, thrombectomy, and other acute treatments have become a vital part o...

Development of advanced lung cancer inflammation index-based machine learning models for predicting stroke and mortality: A comparative and interpretable study.

BACKGROUND: The Advanced Lung Cancer Inflammation Index (ALI) is a novel composite index that enables a more holistic evaluation of inflammation and n...

Interpretable Multiwavelength SERS Fingerprints of Human Urine for Ischemic Stroke Diagnosis.

Surface-enhanced Raman spectroscopy (SERS) can capture single-molecule-level component information from complex biological samples by providing their ...

Deep learning model for identifying significant tricuspid regurgitation using standard 12-lead electrocardiogram.

INTRODUCTION: Transthoracic echocardiography (TTE) is the current standard for detecting tricuspid regurgitation (TR); however, it incurs additional c...

Personalized adrenal gland volume reference ranges and development of a fully automated deep learning screening tool.

OBJECTIVE: This study aims to develop a low-dose CT-based, fully automated deep learning tool for screening adrenal gland volume abnormalities and est...

Anomaly changes in the functional connectome of post-operative neurosurgical patients: A case series.

PURPOSE: The use of neuronavigation with superimposed mapping tools has enabled visualization of key fiber tracts and improved peri-operative planning...

Emerging risk factors for stroke and bleeding in patients with atrial fibrillation and heart failure-a narrative review.

Atrial fibrillation (AF) and heart failure (HF) frequently coexist, which leads to adverse clinical outcomes and a significant increase in the risk of...

Delineating white matter phenotypes of sensori-/psychomotor functioning in large-scale cohorts of healthy individuals and patients with mental disorders across the lifespan (whiteSPAN): rationale and methods of an interdisciplinary bicentric project.

Aberrant sensori-/psychomotor functioning-including muscular hand weakness, sedentary behavior, psychomotor agitation, slowing, agitation, apathy, and...

Preliminary findings on a deep learning model using electroencephalogram for multi-level neuropathic pain detection in post-stroke patients.

AIM: Neuropathic pain occurs commonly after stroke and represents a major source of disability for affected patients. This study aims to develop an ac...

Development and validation of a machine learning model to predict 30-day mortality in ischemic stroke patients with consciousness impairment: Insights from MIMIC-IV database and multicenter ICU data in China.

BACKGROUND: Patients with ischemic stroke complicated by consciousness disorders remain associated with high mortality risks. This study aims to devel...

Clinically oriented deep learning framework for automated vessel wall segmentation in black-blood MRI: a multi-center study.

OBJECTIVES: To develop and validate a clinically applicable deep learning framework for automated segmentation of intracranial and carotid vessel wall...

Efficacy of MRI-based deep learning algorithm for detecting acute ischemic stroke: evaluation among diverse readers.

OBJECTIVES: The efficacy of an MRI-based deep learning algorithm (DLA) for detecting acute ischemic stroke (AIS) was evaluated across readers with div...

Optimal adherence thresholds for oral anticoagulants in patients with atrial fibrillation using machine learning and population administrative data.

BACKGROUND: Adherence to oral anticoagulants (OACs) for atrial fibrillation (AF) stroke prevention is traditionally defined as taking 80% of doses as ...

Digital twins in stroke rehabilitation: a scoping review of objectives, data sources, mechanisms, outcomes, and desirable properties.

BACKGROUND: Digital Twins (DTs) have transitioned from theory to reality, with growing applications in healthcare. Data generated by technologies (e.g...

Exploring latent diffusion models for ECG generation on the minute scale.

BACKGROUND AND OBJECTIVE: Artificial Intelligence (AI) models for electrocardiogram (ECG) interpretation rely on large, diverse datasets, but existing...

Association between triglyceride-glucose index and depression and the mediating role of hypertension: a cross-sectional study of obese adults.

BACKGROUND: Recent findings indicate a positive correlation between the TyG (triglyceride-glucose) index and the incidence of depression. However, the...