Strokes

In-Hospital Stroke Prediction from PPG-Derived Hemodynamic Features

The absence of pre-hospital physiological data in standard clinical datasets fundamentally constrains the early prediction of stroke, as patients typically present only after stroke has occurred, leaving the predictive value of continuous monitoring signals such as photoplethysmography (PPG) unvalidated. In this work, we overcome this limitation by focusing on a rare but clinically critical cohort...

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 challenge in vascular neurology, as a substantial proportion of patients lack an identifiable embolic source despite standardized diagnostic workup. The failure of empiric anticoagulation strategies highlights the need for individualized, mechanism-oriented risk stratification. We aimed to develop a machi...

VRIQ: Benchmarking and Analyzing Visual-Reasoning IQ of VLMs

Recent progress in Vision Language Models (VLMs) has raised the question of whether they can reliably perform nonverbal reasoning. To this end, we int...

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 the choice of medical therapy is often dependent o...

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, and roughly 60% cases become chronic (duration >6 mo...

Stroke Lesions as a Rosetta Stone for Language Model Interpretability

Large language models (LLMs) have achieved remarkable capabilities, yet methods to verify which model components are truly necessary for language func...

Physics-Informed Neural Network for Mapping Vascular and Tissue Dynamics Using Laser Speckle Contrast Imaging

Significance: Quantitatively mapping both cerebral blood flow and tissue dynamics from laser speckle contrast imaging (LSCI) is powerful for studying ...

Automated Intracranial Thrombus Segmentation from CT Images of Patients with Acute Ischemic Stroke: A Dual-Channel nnU-Net Approach with Uncertainty Quantification

Background: Automated thrombus segmentation on CT imaging could enable routine extraction of clot volume and other biomarkers in large vessel occlusio...

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 they will improve their communication and cognitiv...

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

Deep learning has revolutionized medical image registration by achieving unprecedented speeds, yet its clinical application is hindered by a limited a...

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 (AIS), but the diagnosis requires specialized equipm...

Reasoning-Enhanced Rare-Event Prediction with Balanced Outcome Correction

Rare-event prediction is critical in domains such as healthcare, finance, reliability engineering, customer support, aviation safety, where positive o...

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 patient outcomes, particularly in prehospital sett...

Non-reproducibility of wearable accelerometer methods in protective association between physical activity and cardiovascular disease: a cohort study.

BackgroundThe selection of accelerometer processing methods may influence the shape of the dose-response association between wearable-measured physica...

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 stroke and heart failure, necessitating reliable and ...

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 flutter (AF) and can lead to ischemic stroke. Imag...

Utilizing artificial intelligence and medical experts to identify predictors for common diagnoses in dyspneic adults: A cross-sectional study of consecutive emergency department patients from Southern Sweden.

OBJECTIVE: Half of all adult emergency department (ED) visits with a complaint of dyspnea involve acute heart failure (AHF), exacerbation of chronic o...

Generating a vast chemical space for high polar surface area triphenylamine polymers by machine learning-DFT calculations assisted reverse engineering for photovoltaics.

The total polar surface area (TPSA) is a crucial parameter in photovoltaic (PV) materials, as it directly influences their solubility, processability,...

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

Ischemic stroke (IS) is a highly complex and heterogeneous disease involving multiple pathophysiological events. A better understanding of the pathoph...

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 early risk prediction. This study aimed to develop...