Strokes

Shortening MRI scanning time for acute ischemic stroke: analysis of the effect of 3.0T MRI compressed sensing deep learning reconstruction.

BACKGROUND: Acute ischemic stroke requires rapid and accurate MRI diagnosis. This study aimed to evaluate whether 3.0T brain MRI with compressed sensing deep learning reconstruction (CS‑DLR) can reduce scanning time while maintaining diagnostic image quality. METHODS: We retrospectively enrolled 69 patients with acute ischemic stroke who underwent 3.0T MRI. Conventional images and CS‑DLR reconstru...

Prognostic value of AI-enabled quantitative coronary CT angiography for major adverse cardiovascular events: A systematic review and meta-analysis.

BACKGROUND: Artificial intelligence-enabled quantitative coronary computed tomography angiography (AI-QCCTA) offers automated assessment of coronary plaque burden and morphology. Although AI-QCCTA has improved diagnostic consistency and downstream testing efficiency, its prognostic value for major adverse cardiovascular events (MACE) has not been comprehensively quantified. METHODS: We systematica...

Suicidal Ideation in Online Spaces Through the Lens of Interpersonal Theory of Suicide: Exploratory Study of Self-Disclosure, Peer Support, and AI Responses.

BACKGROUND: Suicide is a critical global public health issue, with millions experiencing suicidal ideation (SI) each year. Global estimates suggest th...

Immersive virtual reality for cognitive rehabilitation in patients who had a stroke: study protocol for a randomised controlled trial.

INTRODUCTION: Poststroke cognitive impairment (PSCI) is a prevalent complication of stroke, characterised by deficits in one or more cognitive domains...

Association of Deep Learning-Derived Temporalis Sarcopenia with Mortality in Acute Ischemic Stroke.

BACKGROUND: Sarcopenia is associated with mortality following acute ischemic stroke (AIS), but diagnosis is time-consuming. Computed tomography (CT) m...

Happiness and hypertension prevalence: A global analysis.

The prevalence of hypertension around the world is high while hypertension control is relatively low. The objective of this study is to investigate th...

Machine learning models of segmentation in acute ischemic stroke: a systematic review and meta-analysis.

BACKGROUND: Accurate segmentation of acute ischemic stroke (AIS) lesions on neuroimaging is essential for diagnosis, treatment decision-making, and pr...

Accuracy of Automated Deep Learning versus Expert Clinicians for Diagnosis of Acute Lacunar Stroke on CT Perfusion.

BACKGROUND: Accurate diagnosis of lacunar stroke in the acute setting is challenging and often depends on MRI or clinical suspicion. CT perfusion (CTP...

Predicting diffusion-FLAIR mismatch from B1000 and ADC without FLAIR: A deep learning-based approach.

Diffusion-FLAIR mismatch (DFM), defined as the discrepancy between diffusion-weighted imaging (DWI) and FLAIR sequences, is a key imaging biomarker us...

Long-term exposure to wildfire smoke particulate matter and incident stroke: a US nationwide study.

BACKGROUND AND AIMS: Wildfire smoke is a growing public health concern, particularly due to its high fine particulate matter (PM2.5) concentrations. W...

Beyond Training: Systems Framework for Sustainable Health Informatics Investment in Africa.

Across Africa, substantial investment has built national health information systems (HISs), including the surveillance platforms, reporting tools, and...

Literature Commentary June, 2026.

In this issue, the following 4 articles are reviewed by Drs. Mark L. Moster and Deborah I. Friedman, most of which include authors who are members of ...

Exploring the Lung-Liver Axis in Pulmonary Arterial Hypertension.

The liver's contribution to pulmonary arterial hypertension (PAH) pathogenesis remains unclear. We hypothesized that the liver promotes inflammatory i...

Mechanistic Insights into PET-MPs-Aggravated Ischemic Stroke via Integrated Computational-Experimental Approaches.

Microplastics, especially the environmentally pervasive polyethylene terephthalate microplastics (PET-MPs), are important environmental pollutants, an...

Deep learning algorithm for automatic detection of acute ischemic stroke on noncontrast brain CT.

This study aimed to evaluate the diagnostic performance of a deep learning-based algorithm for detecting acute ischemic stroke (AIS), including small ...

Identification of zinc finger protein-related feature genes in ischemic stroke via explainable machine learning: A comprehensive analysis.

BACKGROUND: Zinc finger proteins (ZFPs) play important roles following ischemic stroke (IS), but systematic investigations into their potential as bio...

Identification and multicenter validation of a 4-gene plasma signature for early recognition and risk assessment in hypertensive intracerebral hemorrhage.

Hypertensive intracerebral hemorrhage (ICH) is a devastating stroke subtype with high mortality and disability, yet reliable early risk biomarkers rem...

Frequency-Specific Alterations of Spontaneous Brain Activity in Stroke Patients with Upper Limb Motor Dysfunction: A Multi-Metric rs-fMRI and Machine Learning Study.

BACKGROUND: Post-stroke upper limb motor dysfunction is associated with complex alterations in brain function, but the frequency-specific characterist...

Multimodal machine learning for early risk stratification of post-stroke cognitive impairment.

BackgroundPost-stroke cognitive impairment (PSCI) is a major vascular contributor to dementia, significantly impacting long-term recovery and quality ...

Optimizing cervical breath sound acquisition protocol in dysphagia patients: an orthogonal experimental study.

BACKGROUND: Dysphagia is a highly prevalent condition among patients with stroke. Cervical auscultation serves as a crucial screening method for dysph...