Cardiovascular

Strokes

Latest AI and machine learning research in strokes for healthcare professionals.

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ESUS-AI:a machine learning framework to estimate the most likely embolic source in embolic stroke of undetermined source

Background and Purpose Embolic stroke of undetermined source (ESUS) emains a major diagnostic challe...

VRIQ: Benchmarking and Analyzing Visual-Reasoning IQ of VLMs

Recent progress in Vision Language Models (VLMs) has raised the question of whether they can reliabl...

Personalised approach to hypertension treatment: Rationale and design of the HYPERMARKER randomised trial

Background and Objective: Blood pressure treatment response is variable in individual patients, and ...

Predicting Post-Stroke Aphasia Speech Performance from Multimodal Data with Explainable Machine Learning

Aphasia, an acquired language deficit, is the most common post-stroke focal cognitive impairment, an...

Stroke Lesions as a Rosetta Stone for Language Model Interpretability

Large language models (LLMs) have achieved remarkable capabilities, yet methods to verify which mode...

Machine Learning Driven 'Therapy Calculator' for Self-Managed Digital Speech-Language Therapy for Individuals with Post-stroke Aphasia

Individuals with post-stroke aphasia live with long-term disabilities, yet they do not know whether ...

FMIR, a foundation model-based Image Registration Framework for Robust Image Registration

Deep learning has revolutionized medical image registration by achieving unprecedented speeds, yet i...

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

Background: Sarcopenia is associated with mortality and morbidity following acute ischemic stroke (A...

Reasoning-Enhanced Rare-Event Prediction with Balanced Outcome Correction

Rare-event prediction is critical in domains such as healthcare, finance, reliability engineering, c...

Digital FAST: An AI-Driven Multimodal Framework for Rapid and Early Stroke Screening

Early identification of stroke symptoms is essential for enabling timely intervention and improving ...

Robust and Generalizable Atrial Fibrillation Detection from ECG Using Time-Frequency Fusion and Supervised Contrastive Learning

Atrial fibrillation (AF) is a common cardiac arrhythmia that significantly increases the risk of str...

Deep learning enables diagnosis of atrial cardiomyopathy from routine 12-lead electrocardiogram

BackgroundAtrial cardiomyopathy (AtCM) is both a cause and a consequence of atrial fibrillation and ...

Single-cell omics: moving towards a new era in ischemic stroke research.

Ischemic stroke (IS) is a highly complex and heterogeneous disease involving multiple pathophysiolog...

Aug 2025 40350018
Metabolomic machine learning predictor for arsenic-associated hypertension risk in male workers.

Arsenic (As)-induced hypertension is a significant public health concern, highlighting the need for ...

Jul 2025 40024027
Depth-Sequence Transformer (DST) for Segment-Specific ICA Calcification Mapping on Non-Contrast CT

While total intracranial carotid artery calcification (ICAC) volume is an established stroke bioma...

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation

The analysis of carotid arteries, particularly plaques, in multi-sequence Magnetic Resonance Imagi...

Bleeding risk assessment tools in patients with atrial fibrillation taking anticoagulants: a comparative review and clinical implications.

INTRODUCTION: Bleeding risk assessment plays a critical role in the anticoagulation management for a...

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Jul 2025 40550625
ABCD: A Simulation Method for Accelerating Conversational Agents With Applications in Aphasia Therapy.

PURPOSE: Development of aphasia therapies is limited by clinician shortages, patient recruitment cha...

(2)
Jul 2025 40512969
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